List of acronyms used in this chapter.
\r\n\t
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NMR and mass spectrometry (MS) are the principal methods of metabolomics, the branch of ‘-omics’ that deals with small molecules. The Metabolomics Society describes metabolomics as: “the comprehensive characterization of the small molecule metabolites in biological systems” [1]. NMR has a number of characteristics that meet the requirements of metabolomics: it is accurate, quantitative, comprehensive, unbiased, and is able to provide information that can be used to determine molecular structure. The review will discuss these aspects in detail.
Although MS techniques, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS), are most commonly used in metabolomics, NMR still enjoys a number of key advantages. In particular, NMR is nondestructive, unbiased, quantitative, does not require separation or derivatization, and is amenable to compounds that are difficult to analyze by GC-MS and LC-MS. For example, GC-MS often requires derivatization of compounds, such as sugars and amines. LC-MS, on the other hand, generally requires sample preparation, chromatographic separation, specific experimental and ionization conditions, instrumentation and operator skill [2]. These make it difficult to standardize MS analysis. In contrast, NMR does not require elaborate sample preparation and fractionation, is highly reproducible, and is able to provide both qualitative and quantitative information on chemically diverse compounds [3, 4]. The standardization of the NMR protocol will further improve the usefulness of NMR as a tool for the profiling of natural products extracts. Because NMR is able to detect compounds only down to 0.1% level, it is not suitable for the detection of trace components. NMR is less sensitive than MS, which can detect compounds down to parts per million (ppm) levels. Because of the distinct advantages of each method, NMR and MS are considered as complementary techniques.
NMR is a quantitative spectroscopic tool because the intensity of the peaks is directly proportional to the number of nuclei. With improvements in electronics and the use of higher magnetic field strengths, the sensitivity and resolving power of NMR has improved. However, the lack of standardized protocols has limited its quantitative application and many consider NMR mainly as a qualitative method, mainly for chemical structure determination and molecular dynamics [5].
The use of NMR as a quantitative method has been expanding, giving rise to the term “quantitative NMR” (qNMR). The pharmaceutical industry, which has stringent requirements of analysis, has been turning to the use of qNMR in early drug development to address the need for rapid, selective, and accurate analysis without requiring expensive and tedious chromatographic methods. It is also worth noting that qNMR meets the stringent regulatory standards of the pharmaceutical industry, including the International Conference on Harmonization. qNMR has been applied mainly to 1H nuclei although 19F and 31P NMR have also been used where appropriate because of their 100% isotopic abundance [6]. The main advantages of qNMR are its accuracy, reproducibility, and flexibility with respect to the nature of the analyte, the only requirement being the presence of protons and carbon, and its ability to simultaneously quantify multiple analytes, especially when validated using external calibration. Quantitative 1H NMR (qHNMR) has been shown to have an accuracy and precision of ±1% and an uncertainty of measurement of less than 0.1%. This makes it suitable as a metrological technique for the certification of purity of organic compounds [7].
There are two general approaches to the use of NMR for profiling or metabolomics studies. In the first approach, only the spectral patterns (chemical shifts and intensities) are recorded and are used to compare and group samples. In this approach, compounds are not initially identified. Because statistical tools, such as principal components analysis (PCA) are used, this is sometimes called a chemometric approach. In the second approach, particular compounds which are known to be present in the extract are identified and quantified using a reference spectral library. This approach is referred to as quantitative or targeted metabolomics [8]. These approaches, however, are not mutually exclusive and will likely converge in the future with improved statistical tools and bigger NMR spectral databases.
Because of the large amount of data that are produced, statistical methods, known as chemometrics, are applied to reduce the number of variables. Chemometrics is a family of statistical techniques that are applied to large sets of chemical data, such as NMR chemical shift peaks, with the objective of gaining insights into the characteristics of the samples through the use of graphical representation [9]. Because chemometrics is able to process large amounts of data, it is an ideal tool for NMR which produces a lot of data (chemical shifts). This can be used to find patterns of groupings and correlations among natural product samples which can be used for quality control and standardization [10]. Since chemometrics started to be applied to NMR around the year 2000, progress has been very rapid. Chemometrics has been used to classify whole plant samples based on their NMR profiles according to species, origin, processing treatment, age, and various quality parameters [11].
In a talk given during the William Draper Harkins Lecture, University of Chicago in 1991, Alexander Pines mentioned that his organic chemistry colleagues at Berkeley consider two vital instruments in a research laboratory: a balance and an NMR spectrometer. His view is not surprising as decades of improvement in both instrumentation and techniques had rendered the NMR spectrometer as a tool of choice in characterizing molecules, from the structures of natural products and synthetic organic compounds to biomolecules and organometallic complexes. NMR spectroscopy takes advantage of the interaction between nuclei that are acting as tiny magnets and an external magnetic field and this provides a powerful means of probing the chemical bonding and environment of the nucleus. These phenomena are key to the applicability of 1H and 13C NMR to natural products.
Hydrogen is present in almost every organic molecule, and its major isotope, 1H, has an abundance of 98.985%. The 1H nucleus reports a frequency specific to its immediate vicinity in an NMR spectrum. This frequency is extremely sensitive to the electronic environment thus giving each 1H nucleus in an organic compound a type of identification number, called the NMR chemical shift. Magnetic nuclei, such as 1H, also interact with each other. In solution or liquid-state NMR spectroscopy, these interactions, called couplings, are observed as “splitting” of lines in an NMR spectrum. The magnitude of these couplings not only depends on the number and type of bonds separating the interacting pair of 1H nuclei but also on the spatial orientation between the nuclei. Both NMR chemical shifts and coupling constants provide immense information regarding structure and environment. Hence, NMR spectroscopy has become a powerful tool for the determination of organic structure.
These NMR interactions (chemical shifts and coupling constants), although very sensitive, are quite weak such that improvements in their detection have been one of the primary goals of developments in NMR instrumentation. Such limitations are no longer severe. The use of pulses and data processing by Fourier transformation, first introduced by Ernst and Anderson [12] and the availability of high-field superconducting magnets have allowed for efficient signal averaging such that nowadays, with an 11 T magnet (500 MHz), a 1H NMR spectrum can be obtained even from very dilute solutions (micromolar concentration).
Pulse Fourier transform NMR spectroscopy, as in other spectroscopic methods, involves transitions between energy levels. However, unlike other spectroscopic methods, the transition probability in an NMR excitation is the same regardless of chemical environment. NMR spectroscopy does not need to consider oscillator strengths or extinction coefficients, which are important for infrared and UV-visible spectroscopy, respectively. The intensity of an NMR signal is determined solely by the excitation pulse, strength of the external magnetic field, and temperature. The magnetic field strength and temperature determine the Boltzmann population difference between the two energy levels while the excitation pulse dictates the extent of the transition. Since only one pulse is often used to excite all of the 1H nuclei in a sample, the extent of transitions is the same for all. Furthermore, the NMR chemical shift, which reflects the differences in resonance frequencies of inequivalent 1H nuclei, is very small: the differences are in parts per million (ppm). Hence, the Boltzmann distribution for the two spin states is essentially the same for every 1H in a molecule. Indeed, as early as 1963, the area under each peak in a 1H NMR spectrum has been shown to correspond proportionally to the number of hydrogen atoms sharing the same environment in a given compound [13]. This quantitative aspect applies not only to pure substances but also to mixtures. In fact, during the same year, a successful quantitative analysis by 1H NMR spectroscopy of a mixture of aspirin, phenacetin, and caffeine was demonstrated [14].
13C also has a spin of ½ and is therefore likewise NMR active. However, because the 13C isotope occurs at only 1.108%, it is difficult to observe. (The major carbon isotope, 12C, is not NMR-active.) David Grant and coworkers published a series of papers on 13C NMR spectroscopy that spanned two decades [15, 16]. In the first paper of this series, inherent difficulties in observing 13C NMR spectra were addressed by proton decoupling and sample spinning. Since carbon atoms are frequently attached to hydrogen atoms in organic compounds, 13C-1H coupling is present and leads to splitting of 13C resonances. Proton decoupling removes this interaction, consolidating multiple 13C peaks into a single taller peak. Moreover, additional enhancement of 13C signals is observed when the 1H spin populations are perturbed, similar to the effect observed by Overhauser with electron spins [17]. Taking advantage of both the nuclear Overhauser effect (NOE) and the increased signal due to the collapse of multiple peaks, measurement of 13C NMR spectra became routine and easy to interpret. Being in the proximity of more than one pair of electrons, 13C nuclei offer a much wider range of chemical shifts than 1H (200 ppm for 13C versus 10 ppm for 1H). In addition, since the probability that a 13C nucleus is attached to another 13C nucleus is very small (about 0.0001), 13C-13C couplings are usually not observed thereby providing a much simpler 13C NMR spectrum.
Using 13C NMR spectroscopy as a powerful analytical tool can be easily appreciated by considering the three isomers of a simple hydrocarbon C5H12 (see Figure 1). n-Pentane (CH3CH2CH2CH2CH3), produces three peaks with a 1:2:2 intensity ratio, 2-methylbutane ((CH3)2CHCH2CH3) displays four peaks with a 1:1:2:1 intensity ratio, and neopentane ((CH3)4C) gives two peaks of 4:1 intensity ratio. For the above reasons, a qualitative and quantitative analysis that is nondestructive and requires no separation is possible with 13C NMR spectroscopy [18]. All that one needs is a library of 13C NMR spectra of all possible components, a good spectral prediction software, and an efficient algorithm that can do the search and construct a simulated spectrum that matches the observed spectrum. All of these requirements are already available today. A similar treatment has been shown to be feasible in determining the acyl profile in various vegetable oils [19] and in characterizing the various sesquiterpenes in essential oils from juniper, rosemary, cedarwood, and ginger [20].
13C NMR spectra of (a) n-pentane, (b) 2-methylbutane, and (c) neopentane.
The promise of a wealth of information that NMR spectroscopy offers, however, comes also with challenges. Since the frequencies observed depend on the magnetic field strength, the peaks’ shapes and widths are sensitive to the homogeneity of the magnetic field throughout the sample. Experimentally, corrections to field homogeneity are done through a process called shimming, which involves adding small magnetic field gradients. Shimming used to be an art and both symmetry and narrowness of an NMR peak depended on the expertise of the NMR operator. Fortunately, with new superconducting magnets and automated shimming, 13C NMR spectra can now be made reproducible and comparable regardless of who operates the spectrometer. However, there are still numerous factors that are independent of the NMR operator which can affect the appearance of an NMR spectrum.
Since the frequencies (in hertz) observed for each NMR-active nucleus are dependent on the field strength, chemical shifts are reported in dimensionless units of parts per million (ppm), which then becomes independent of the magnetic field strength. Interactions between nuclei, on the other hand, are independent of field strength, so these are recorded in units of frequency, hertz. Since the ppm equivalent of a hertz is determined by the strength of the magnetic field, splittings will appear narrower in a high-field magnet than in a low-field magnet. When the coupling interactions are of the same magnitude as the chemical shift differences, the coupling pattern is complex [21]. A hypothetical example for 1H NMR is shown in Figure 2, where the coupling constant is equal to the chemical shift difference in a spectrometer operating with a 1H frequency of 100 MHz. As the strength of the magnetic field increases, chemical shift differences (in hertz) also increase, which can dramatically change the appearance of the spectrum. In this particular example, the spectrum only begins to appear simpler with a spectrometer operating at 1 GHz, in which the chemical shift difference is now 10 times bigger than the coupling constant; this is called a first-order spectrum. Thus, 1H spectra taken at different magnetic field strengths appear different. On the other hand, 13C spectra appear similar at different magnetic field strengths. This is because 13C-13C coupling is not observed due to low natural abundance, 13C-1H couplings, although present, are always several orders of magnitude lower than the frequency difference between these two nuclei, and proton-decoupled 13C NMR spectra are singlets. Therefore, although 13C presents detection challenges due to its lower frequency and low natural abundance, 13C has the advantage over 1H with regard to simplicity of NMR spectra.
1H NMR spectra of strongly coupled nuclei at various magnetic field strengths.
Absolute frequencies for NMR transitions are seldom used since these numbers are dependent on the strength of the external magnetic field. Chemical shift differences are instead reported in ppm, which is the ratio of the absolute frequency with respect to the frequency of a reference compound, such as tetramethylsilane (TMS). Alternatively, the solvent can be utilized as internal reference. Due to the sensitivity of the NMR chemical shift, intermolecular effects are also frequently observed [22]. Since solvents are known to induce shifts, it is important that when comparing different spectra, the same solvent should be used. Since 13C has a much wider chemical shift range, the effect of solvent on chemical shift is smaller for 13C (2/200) than that of 1H (0.7/10). Furthermore, since carbon atoms, unlike hydrogen atoms, reside on the interior of the molecule, 13C is generally shielded from environmental effects, such as intermolecular interactions and solvent effects. This is one reason why 13C NMR chemical shifts are nearly exclusively dependent only on its covalent bonding interactions [23]. The greater susceptibility of 1H NMR chemical shifts to solvent effects makes 13C NMR spectroscopy a better alternative in profiling natural products. Solvent effects on both 1H and 13C NMR chemical shifts are expected to be dominated by van der Waals interactions with the solvent. These interactions are largely nonspecific thus an approximation that the solvent simply causes a constant offset on all resonances may be valid. Using an internal reference can therefore easily remove effects of the medium on the observed chemical shifts. Attention, however, is still required for sites that can participate in electrostatic interactions and hydrogen bonding. 13C in carbonyl groups is one example [24].
Temperature can also affect observed chemical shifts through changes in the density of the sample as well as changes in the internal motions of the molecule [25]. For a fair comparison of library and sample spectra, it is important that spectra are taken at the same temperature.
Lastly, a quantitative 13C NMR spectrum requires uniform excitation of all 13C nuclei. The wider chemical shift range and lower frequency for 13C necessitate excitation pulses with much higher power with a pulse that is less than 15 μs long so that the entire chemical shift range is uniformly irradiated [26]. Furthermore, proton decoupling is also regularly used to collapse multiple 13C peaks, but this can lead to NOEs which enhance 13C nuclei that are directly bound to protons, making 13C NMR no longer uniform for carbon nuclei with different numbers of attached protons. The inverse-gated 13C NMR experiment can be used to overcome these problems. This involves turning the proton decoupler on only during acquisition and providing adequate time for all the 13C nuclei to relax [27]. 13C nuclei are most often relaxed by a nearby 1H nucleus. Thus, the needed relaxation time (equal to 5 ×
The use of a library of NMR spectra in the analysis of a mixture of natural products requires reproducibility of both chemical shift and peak intensity. Since samples of natural products are often dissolved in either dimethyl sulfoxide or methanol, confining both library and sample data to these two solvents can easily ameliorate the confounding effects of the solvent on the observed chemical shifts. Modern NMR spectrometers are normally equipped with temperature control so the measurements can be made at a given temperature, which also avoids the temperature dependence of NMR chemical shifts, thus eliminating this problem. The reproducibility of peak intensities, however, requires additional considerations.
The robustness of current NMR instrumentation is evident in successful indirect detection methods during which resonances from 1H nuclei bound to 12C are separated from those attached to 13C [28]. Indirect detection is possible only if the scans or transients are highly reproducible such that these can be added to extract the desired resonances and remove completely the unwanted signals. However, this robustness only entails the reproducibility of an NMR experiment from one transient to the next. It does not address the reproducibility of NMR experiments among different laboratories. Thus, there is a need to standardize both NMR acquisition conditions and processing parameters.
The intensity of an NMR peak depends on the duration of the pulse. Equalizing the m = +½ and m = −½ spin populations requires what is called in NMR spectroscopy as a 90° pulse. Peak intensities are at a maximum with this pulse. Since all nuclei in a sample are subject to the same pulse, it is not necessary that a 90° pulse is always employed. For an NMR spectrum to be quantitative, the relative, not the absolute, peak intensities are sufficient. However, the extent of the pulse determines how much time is required for relaxation between transients. For signal averaging to be effective, one still needs to make sure that the spins have reached equilibrium before applying the next pulse so as to avoid saturation, which leads to loss of signal [29]. When a 90° pulse is employed, the time between transients must be at least five times as long as the relaxation time. The time required between pulses can be reduced by using a smaller flip angle. For example, a 30° pulse requires a delay that is three times shorter. This reduces the peak intensity for each scan but reduces the delay time required between scans enabling the acquisition of more scans for the same amount of time. Another parameter that can affect the appearance of an NMR spectrum is acquisition time, which determines spectral resolution. What is directly acquired from an NMR experiment is a free induction decay (FID), which still needs to be processed to produce the frequency spectrum. During processing, apodization, zero-filling, and baseline and phase corrections are normally applied. All of these can significantly alter the integrated areas under the peaks of an NMR spectrum. Thus, a list of universal parameters for quantitative NMR has been established by national and international round robin tests [30] which includes temperature (300 K), pulse angle (30°), preacquisition delay (5 s), acquisition time (3.4 s), relaxation delay (7/3 of relaxation time), and line broadening (0.3 Hz). For processing, careful manual phase and baseline corrections are recommended since automated features of popular NMR processing software packages are unreliable. This validation has been performed with 1H NMR, but these can be applied to 13C. With 13C, relaxation times are appreciably longer so relaxation agents such as paramagnetic compounds have been used as in the earlier work on petroleum distillates [31].
For the unbiased profiling of natural products extracts, one needs to consider the problems of sensitivity and dynamic range. A natural product extract typically contains major and minor components. Oftentimes, in order to detect minor components, it is necessary to employ separation techniques, such as successive fractionation and chromatography which have the effect of increasing sensitivity to minor constituents and improving dynamic range. However, this introduces bias.
Limits of detection and quantification are often given in terms of signal to noise ratios. The International Conference on Harmonization of Technical Requirements recommends a signal to noise ratio of 3 and 10 for the detection limit and quantification limit, respectively (ICH Expert Working Group, 1994). In practice, for error values less than 1%, a signal to noise ratio of 150 is recommended [30]. The signal-to-noise ratio (S/N) in NMR spectroscopy however depends not only on concentration but also on other factors [32]:
In this equation,
Another consideration is dynamic range. For 1H NMR, signals arising from the solvent, in particular water, can easily use up most of the higher bits in a spectrometer’s digitizer thereby decreasing the precision of signals coming from the natural product constituents. This can be alleviated by suppressing solvent resonances, but this introduces the problem of reproducibility between runs and remains a problem for components which have signals near the solvent.
A quantitative comparison using three magnetic field strengths—300, 400, and 500 MHz—showed that there was no difference in the sensitivity and that the standard protocol could differentiate plant samples which were spiked with 0.2 mg/mL of rutin (MW 610.5; 328 μM). This is due to the mild dependence of S/N on the field strength.
For the application of 1H NMR for pattern recognition, the use of the magnitude spectrum has been suggested [34]. The standard 1H NMR spectrum utilizes the phase-corrected real component of the Fourier transform of the free induction decay (FID), discarding the imaginary component. This yields the absorption spectrum which is useful for normal qualitative analysis due to its good peak resolution. However, this procedure sacrifices reproducibility. The use of the magnitude spectrum, which utilizes the absolute value of both the real and imaginary components of the FID improves the reproducibility of the spectra thereby improving its accuracy for pattern recognition. This method is applicable to one-dimensional 1H NMR.
Peak integrals in an NMR spectrum unfortunately are also sensitive to data processing. Apodization, zero-filling, phase and baseline corrections, and the integration itself can affect the signal-to-noise ratio of an NMR spectrum. Thus, the current limit in the sensitivity of NMR-based metabolomics is not due to magnetic field strength, but is due to the current data processing methodology which uses spectral binning (alternatively called bucketing) and PCA. The usual bin size for 1H NMR is 0.04 ppm. This divides a 10 ppm 1H spectrum into 250 bins, which effectively becomes the resolution of the method. A smaller bin size can be used if the variability in the chemical shift can be minimized. Another problem observed is the effect of different solvent (see below) to move the position of chemical shifts, which will make identification using database comparisons difficult [35].
Because plant samples contain a wide variety of compounds with corresponding differences in polarity, the solvent used for extraction and the NMR analysis is very important. The solvent system must balance the ability to perform a comprehensive extraction with solvent complexity and reproducibility. In particular, multi-component solvent systems are prone to variation, and if there is a wide difference in vapor pressures (boiling points), the solvent composition may change if care is not taken. Acetone and acetonitrile are effective solvents but their use is limited by their low boiling points. The use of methanol-D4 in combination with deuterated water (1:1) have been reported. By using these deuterated solvents, the extracts can be measured directly after extraction without need for evaporation and reconstitution. However, use of water will introduce a strong water peak in the 1H NMR spectrum that must be irradiated. This becomes a source of variability around the water peak across different operators and instruments. To avoid shifts due to differences of pH in 1H NMR measurements a buffer, such as KH2PO4, is used [36].
NMR is capable of providing simultaneous access to both qualitative (chemical structure) and quantitative information. Unfortunately, NMR has been more generally associated with multidimensional qualitative NMR used in structural analysis and qNMR has been living under this shadow. Fan (1996) pointed out that comprehensive metabolite profiling of complex food products can be done using one- and two-dimensional NMR analysis [37]. However, it is in the use of NMR combined with chemometric methods that the extraordinary potential of both the qualitative and quantitative applications have been realized [38].
In view of its ability to be used as an exhaustive molecular fingerprinting technique, 1H NMR has been found to be a suitable method for the identification, quality control, and fraud detection of essential oils, a function normally reserved for GC-MS [39]. NMR fingerprinting involves obtaining 1H or 13C spectra of whole solvent extracts under standardized conditions and ignoring, at least initially, the assignment of peaks. Multivariate statistical methods, such as PCA, are used to compare spectra from the samples to identify clusters so that inferences can be drawn about the classification of individual plant samples. The identities of metabolites responsible for differences between groups can be investigated from loadings plots generated by PCA [40]. The following section will cover applications of 1H NMR in one- and two-dimensions and 13C NMR together with the statistical tools.
One-dimensional 1H NMR (1D HNMR) can be used in the untargeted and targeted mode. The earliest use of 1D HNMR for the profiling of complex extracts had the objective of monitoring the major components of exudates of plants, such as its root system. The relative increase or decrease of primary metabolites, such as lactate, ethanol, and certain amino acids, could be observed [41]. However, its application to natural product compounds is more challenging due to their more complex structures and lower concentrations. Because of its simplicity and speed, 1D HNMR in the untargeted mode can be used by itself or as a first-pass screening to obtain cluster and profile information using HCA and PCA [42]. The majority of HNMR studies combine 1D HNMR for PCA analysis with two-dimensional homonuclear (1H-1H) or heteronuclear (1H-13C) NMR methods for identification of natural product metabolites.
This section discusses applications that make use of 1D HNMR alone. The number of such studies is limited because of the presence of overlapping signals and the need for high magnetic fields. 1D HNMR at 500 MHz was used to authenticate grapes for wine making by analyzing their skin and pulp at maturity. Spectral data were reduced by binning using 0.04 ppm bin size and normalized to generate 183 variables to describe each spectrum. Chemometric methods, in particular PCA and partial least squares (PLS), enabled the identification of compounds that contributed to differences between berries, due to the sugars (glucose, fructose, and sucrose), organic acids (tartaric, malic, citric, and succinic acids), and amino acids (proline, arginine, gamma-aminobutyric acid, valine, alanine, leucine, and isoleucine) [43].
A set of green teas selected from a Japanese tea contest were analyzed by 1D HNMR at 750 MHz to classify tea quality with respect to that judged by tea tasters and to propose a quality prediction model. PCA metabolomics profiling revealed a separation between the high- and the low-quality green teas. The taste marker compounds contributing to the discrimination of tea quality were identified from 1D HNMR as caffeine, theanine, epigallocatechin-3-gallate, epigallocatechin, epicatechin-3-gallate, and epicatechin [44].
The use of the magnitude spectrum showed good reproducibility in the analysis of 4 diverse natural product samples (12 tea extracts, 8 liquor samples, 9 hops extracts, and 25 cannabis extracts) using 1D HNMR at 500-MHz and various statistical tools [45].
Because of problems of signal overlaps in 1D HNMR spectra, two-dimensional NMR techniques are usually used to overcome these limitations. The 2D methods include 1H
1D and 2D NMR at 600 MHz together with chemometric analysis were used to differentiate the origin, purity, and processing methods of chamomile flowers which were obtained from three different countries. The extracts were dissolved in D2O phosphate buffer adjusted to pH 7.4. 1D NMR data were analyzed by PCA analysis to determine the groupings by pattern recognition and 2D COSY and 2D TOCSY pulse sequences were used to assign the resonances and identify constituents [46].
Several NMR-based metabolomic studies have been done on green tea (
The same strategy was used for chemotaxonomic classification of 11 South American
1D HNMR fingerprinting followed by 2D TOCSY and 2D HSQC methods were used to distinguish four Asian and four Korean ginseng products, as well as their commercial products. In this way, the major metabolites—glutamine, arginine, sucrose, malate, and myo-inositol—were identified as chemical markers for quality assurance [52]. In a study on Indian ginseng,
This NMR-based metabolomic strategy was applied to analyze seven spices used in traditional Mediterranean cuisine and to detect metabolic changes over different seasons. Both primary and secondary metabolites were identified and quantified. The major secondary metabolites identified were polyphenols, including flavonoids (apigenin, quercetin, and kaempferol derivatives) and phenylpropanoid derivatives (chlorogenic and rosmarinic acid). This study was performed using a 300 MHz NMR instrument [54].
The application of NMR-based metabolomics method in plant breeding has been reported. Using a 500 MHz instrument, the NMR-based metabolomics was applied to the identification of sugar beet (
Because of its lower sensitivity and longer acquisition time, 13C NMR is used less often than 1H NMR. However, 13C NMR spectra are simpler, have less severe problems with overlapping peaks, are more comparable across different magnetic field strengths, and are less susceptible to solvent effects. In addition, the singlet nature of 13C NMR signals makes it easier to determine the identity of individual compounds in a mixture.
13C NMR methodology was used to study the triacylglycerols of the oil extracted from the seeds of
13C NMR was used in a dereplication strategy for the identification of natural product compounds directly from plant extracts. The whole extract was first separated into fractions of simpler composition, which were then analyzed by 13C NMR. The 13C spectra of all the fractions were aligned and subjected to pattern recognition by HCA. This yielded correlations among 13C signals within each fraction which were visualized as chemical shift clusters, which were assigned to specific compounds in a 13C database. This strategy was applied to the analysis of 5 g of a bark extract from the African tree
Chemical profiling and standardization of the methanol extract from the leaves of
13C NMR is being used more often for dereplication of natural product extracts without fractionation. This approach is being enhanced by availability of 13C NMR databases and predictive software which list compounds that are most likely to be present in the extract. These results have been found to be comparable to those obtained using LC-MS and GC-MS, which require fractionation and sample preparation [62].
The combined use of high-resolution 1H and 13C NMR analysis has the potential to reveal more details that are not available using only one technique. This combined approach was employed to detect and quantify a wide range of triacylglycerols and their component fatty acids in marine cod liver oil supplements. The combination of 1H and 13C spectra permitted the detailed analysis of components, including sn-1 monoacylglycerols, sn-1,2- and sn-1,3-diacylglycerol adducts, and other minor components, such as trans-fatty acids, free glycerol and cholesterol, and added vitamins A and E and synthetic compounds, such as ethyl docosahexaenoate or eicosopentaenoate. The identity of each compound was confirmed using 2D COSY [63].
The use of 1H and 13C NMR for the profiling of natural products extracts is a rapidly growing branch of metabolomics. It will further accelerate with the increasing use of NMR in quality management, the growth of NMR databases, the development of portable and benchtop NMR instrumentation, and advances in the use of statistical analysis. Despite its considerable potential, the routine application of this method is limited by the lack of expertise to run sophisticated NMR experiments and the lack of computational tools for NMR spectral deconvolution, in particular of 1H spectra [64].
NMR has been used for the monitoring and quality management of foods, beverages, cosmetics, and pharmaceuticals. The same can be done for the profiling of natural products. In order to ensure reproducibility and reliability and to minimize experimental artifacts, the entire process—from sample collection and storage, extraction, NMR measurement and data processing, and statistical analysis—should be optimized and standardized [65, 66]. The NMR solvent is of particular importance because of its influence on the chemical shift positions of protons in phenolic compounds [67] and other solvent effects. This problem is more severe for 1H as compared with 13C NMR.
It has been claimed that periodic calibration can deliver accuracy as high as 99.9% and precision as good as 0.59%, and if calibration is performed with each study, the accuracy and precision can reach 100 and 0.35%, respectively [68]. The various experimental parameters are listed below:
Sample preparation: homogeneity of sample, extraction solvent, extraction method, and NMR solvent.
Acquisition parameters: temperature, acquisition time, pulse angle, number of data points, time delay (relaxation time), and electronic amplification.
NMR data processing: smoothing, phase correction, baseline correction, and signal integration.
The usefulness of NMR databases is premised on the reproducibility of the NMR experiment—starting with sample preparation, NMR acquisition, and processing—across different laboratories. It is important to avoid conditions that alter the position of chemical shifts, which will make identification using database comparisons difficult. Open-access and user-contributed 1H and 13C NMR spectral databases have a high potential as a useful tool for natural products researchers provided that sample preparation, instrumentation, and acquisition parameters are standardized. For sample preparation, only selected NMR solvents should be used. Magnetic field strength is more critical for 1H than 13C NMR. Acquisition and processing parameters should be standardized. As of 2015, 1829 1H NMR and 1383 13C NMR spectra have been available in open-access chemical databases. To further promote participation by researchers, the entire process, from data acquisition, conversion of vendor-specific raw data files, and data deposition have to be simplified and standardized [69].
NMR is usually considered to be an expensive analytical technique which is used for research purposes only. However, for NMR to become more useful for the natural products industry where many of the companies are small to medium in size, more affordable instrumentation is needed. There have been numerous announcements regarding the development of portable and benchtop NMR instruments with full spectrum 1H and 13C NMR capability using microcoils with small portable magnets of up to 2 T (approximately 85 MHz 1H) [70]. Although these are limited in capability and reproducibility compared with a full laboratory NMR instrument, they can be used in the field or production site where cryogenic liquids and stable power are not available. Because there is a demand for such instrumentation for other purposes, such as forensic investigation, detection of explosives, and medical diagnostics, their development is certain to accelerate. This will expand the use of NMR for the profiling of natural products.
Although the use of NMR in the analysis of biological extracts was already being done in the 1980s, it was the application of statistical methods that enabled researchers to make use the large amount of NMR data to find patterns and correlations. The first step usually involves the simplification of large NMR data sets to find relationships, groupings, or dependencies using PCA. Second, the groups can be classified with or without a training set which has known information or characteristics against which other sample sets are compared. Linear discriminant analysis (LDA) and soft independent modeling of class analogy (SIMCA) are used for this purpose. For quantitative analysis of constituents, in particular for strongly overlapping peaks, principal component regression (PCR) or PLS regression can be used [71]. Although these statistical techniques are now commonly used, new ones continue to be developed and reported.
One of the most exciting areas of development is the use of statistical methods to correlate NMR signals with biological activity. Since the NMR signals can be related to specific compounds, this in effect allows one to correlate specific compounds with biological activity. Although it has to be emphasized that correlation is not proof of biological activity, this strategy nevertheless allows one to shortcut the process of discovering bioactive compounds in a complex natural product mixture. This also allows one to detect multiple active compounds.
1H and 13C NMR are rapidly expanding it role from its traditional use mainly as a qualitative spectroscopic technique for the determination of chemical structure to a quantitative tool for the metabolomic study of natural product extracts, whether for quality control of phytomedicine products, analysis of the metabolome for plant profiling, identification of constituents as plant markers, or for plant biotechnology. A major enabler for the use of NMR for metabolomic studies is the application of various statistical techniques which are able to find patterns and correlations in the large NMR data sets. The continued expansion of the use of NMR for the metabolomic profiling of natural product extracts will likely depend on the further development of statistical methods and the availability of NMR databases for both 1H and 13C nuclei. It is likely that more compounds will be identified as techniques are improved.
An NMR spectrum is quantitative. An understanding of the physical principles of NMR provides the theoretical basis for its use as a quantitative tool. NMR spectroscopy does not require a standard for each component since the intensity of each signal is directly proportional to the number of nuclei being observed regardless of environment. NMR spectroscopy also offers detailed information regarding molecular structure. Using NMR spectroscopy as a tool in the profiling of natural product extracts therefore not only provides accurate and precise composition, but also structural evidence for each of the components. Since the NMR signal dependence on various factors is already well known, resonance positions and intensities are highly reproducible. These are important characteristics which give NMR a unique advantage over other analytical methods.
1D HNMR | One-dimensional 1H NMR |
2D COSY | Two-dimensional 1H-1H correlation spectroscopy |
2D JNMR | Two-dimensional 1H J-resolved spectroscopy |
2D HMBC | Two-dimensional 1H-13C heteronuclear multibond coherence |
2D HSQC | Two-dimensional, 1H-13C single quantum coherence |
2D TOCSY | Two-dimensional total correlation spectroscopy |
GC-MS | Gas chromatography-mass spectrometry |
HCA | Hierarchical cluster analysis |
LC-MS | Liquid chromatography-mass spectrometry |
MS | Mass spectrometry |
NMR | Nuclear magnetic resonance |
PCA | Principal components analysis |
PLS | Partial least squares |
PLS-DA | Partial least squares-discriminant analysis |
qNMR | Quantitative NMR |
qHNMR | Quantitative proton (1H) NMR |
qTLC | Quantitative thin layer chromatography |
Accenture company has conducted a survey on the implementation of BDI solutions in organizations. The survey outcome revealed that 92% of managers are happy with the results obtained from BDI solutions and 89% of managers agree that big data integration and analytics is very vital for their business planning to leverage competition. The Internet Trends Report from KPCB’s by Mary Meeker discovered the decreasing trends in the cost of hardware technology in the past twenty years, the cost of computing has been reduced by 33%, 38% storage cost reduction and 27% bandwidth costs reduction year after year. The major challenges faced in BDI processing are data selection, gathering, storing, communication, searching, visualization, ensuring privacy, and security of data.
The efficiency in handling big data drives effective decision making. The advancement in computing infrastructures, algorithms and innovative technologies have boosted the big data management and analytics domain and reduced the investment costs to deliver the best value for businesses.
The business experts have agreed that big data would mean big value. The digital transformation of business operations is enhancing customer experience and reducing costs. Consumers would like to access personalized data and carry out business on the go. Online processing of bigdata using analytical platforms in the organizations can make the information accurate, standardized, and actionable. Acquiring insights from big data leverage the companies to make more informed business decisions with improved efficiency, and to design more BDI applications. The revolution in computing and digitalization has also increased the potential of cyber-attacks. The cyber threats by hackers are ever increasing and becoming more and more complex day by day. ML and DL techniques have been significantly applied to design intelligent and secure BDI solutions for automating business processes. ML projects are receiving the maximum funding since 2019, compared to all other AI projects combined. Walmart corporation has implemented BDI solutions for acquiring business intelligence and taking real-time business decisions. Many leading fast-food based companies such as McDonald’s, KFC, Pizzahut are using BDI solutions for designing their marketing strategies to discover the hanging business trends. The Casinos are also utilizing the BDI solutions to enhance their revenues in the recent years and to attract and inspire customers for regular visits. The hotel industry uses BDI applications to predict customer behavior, food habits and demands. Tourists today are also using digital solutions to collect information on all issues related to tourism. BDI has been applied in the healthcare industry for rendering quality healthcare services, decreasing the wastage of money and time. The governments are using BDI for developing smart city public services. BDI has empowered e-commerce industries such as Amazon, Flipkart, etc. by providing data insights and analytical reports. The integration of AI, BDI and visualization tools helped meteorologists to predict weather conditions precisely. BDI solutions have been applied successfully in modern agriculture. BDI solutions have also empowered digital marketing for the success of every business. The above facts and applications have motivated the researchers to study the BDI in detail.
According to global forecasts BDI solutions market size is estimated to reach US$ 12.24 billion by 2022 at a Compound Annual Growth Rate (CAGR) of 13.7%. The market survey by Dresner Advisory Assc. in the year 2020 has revealed that 80% of organizations are considering BDI solutions as critical for decision-making activities and 60% of them prefer to deploy BDI solutions on cloud platforms. International Data Corporation (IDC) has predicted that the global data-sphere would be about 175 zettabytes by 2025. IDC has estimated that several billion IoT devices and embedded systems would generate, gather, communicate a wealth of IoT data and carryout analytics every day throughout the world. IDC has also predicted that by 2025 about six billion customers or 75% of the global population would be communicated by using online and real-time data every day. The share of real-time data would be about 30% in global data as estimated by IDC.
BDI is the process of consolidating data from multiple applications and creating a unified view of data assets. BDI is the main component of various mission-critical data management projects, such as building an enterprise data warehouse, migrating data from one or multiple databases to another, and synchronizing data among applications. BDI directs at furnishing an integrated and consistent view of data coming from external and internal data sources.
Big data consolidation is the process of consolidating or integrating data from various data sources to make a centralized data store or repository. This is an amalgamated data store used for diverse purposes, such as data analysis and reporting. It can also execute for downstream applications as a data source.
A Data Federation is a data integration technique. Data federation is used to integrate the data and simplify the approach for consuming by the users and front-end applications. In data federation, distributed data with various data models are combined into a unified data model that features a virtual database.
It is another technique for data integration. Data would be propagated from an enterprise data warehouse to different data marts after the needed transformations.
ETL is the best-known data integration technology. ETL is a process of data integration that includes extraction of data from a source system and it’s loading after transformation to a target destination.
This data integration technology is used to deliver curated data-sets on an on-demand basis. EII is a technology that admits developers and business users alike to treat a range of data sources as if they were one database and represent the incoming data in novel ways.
EDR is a real-time data consolidation method that includes moving data from one storage system to another. In its simplest form, having the same schema, EDR involves shifting a data-set from one database to another database.
Enterprise BDI tools provided by Adeptia may be utilized by other than technical business users. Adeptia Connect has an easy user interface to coordinate with all data interfaces and external connections. It also involves a no-code approach and self-service partner onboarding that allows partners and users to view, set up and coordinate data connections. The platform brags a suite of Cloud Services Integration and pre-built connections along with protocol support and B2B standards.
Alooma provides a data pipeline service that combines with prevalent data sources. The Alooma platform contains security from end-to-end level, which ascertains that every event is securely moved to a data warehouse (HIPAA, SOC2, and EU-US Privacy Shield certified). The solution reacts to the changes in data in real-time to ascertain that no such events have vanished. Users can select to carry out changes automatically or get notified and do on-demand changes. This tool also automatically reduces the data volume to make control customizable.
It is a Dell Technologies company’s Boomi’s flagship product. AtomSphere supports an integration process between cloud platforms, software-as-a-service applications, and on-prem systems. The visual interface is used by AtomSphere is used to configure application integrations. Wherever it is needed, the solution’s runtime tool, Boomi Atom, allows integrations to be deployed. Based on use case and functionality, the AtomSphere platform is also available in various editions.
Celigo company provides an Integration Platform called Integrator.io as a service product. The solution enables organizations to synchronize the data, connect applications, and automate processes. Celigo bundles an integration wizard that involves visual field mapping interface, an API assistant, and drop-down menus. This tool also provides integration templates which are reusable pre-configured on the integrator.io marketplace, permitting users to have their library of reusable, standalone flows.
The Cleo Integration Cloud accords organizations to connect to SaaS applications and enterprises with a range of connectors and APIs. This tool automatically accepts, transforms, orchestrates, connects and integrates all B2B data types from any source and to any target, and can be implemented via several different methods. Cleo Integration Cloud can also be engrafted for Information Services organizations or SaaS and can be used as an administered service to divest complex integrations to the vendor’s experts.
The Denodo Platform provides data virtualization for integrating multi-structured sources of data from database management systems, a wide variety of other big data, cloud, documents, and enterprise sources. Connectivity support involves legacy data, flat files, relational databases, packed applications, CML, and emerging data types including Hadoop. The only data virtualization solution, Denodo, is to be represented as a virtual image on AWS Marketplace of Amazon.
A unified data integration platform, Diyotta, that combines with data warehousing environments and modern data lake. The native processing capabilities and drag-and-drop user interface to build this product. Diyotta enables faster data movement, shorter development times, and reusability all over the enterprise to simplify future development. Diyotta touts the industry’s first data integration software to leverage modern data processing platforms like Snowflake, Google BigQuery, Hadoop, and Amazon Redshift.
IBM provides several distinct data integration tools in both cloud and on-prem deployments, and for every enterprise use case virtually. Its on-prem data integration suite has tools for modern data integration synchronization, data virtualization) and traditional (replication and batch processing) requirements.
IBM also provides a range of connectors and pre-built functions. The mega-vendors cloud integration product is considered as one of the most excellent in the marketplace.
Informatica’s data integration tools portfolio covers both cloud deployments and on-prem for several enterprise use cases. The vendor integrates governance functionality and advanced hybrid integration with self-service business access for different analytical functions. Augmented integration is possible via a metadata-driven AI engine, and Informatica’s CLAIRE Engine, that enforces machine learning. Informatica touts interoperability in strong in nature.
The company’s SQL Server Integration Services (SSIS), traditional integration tools, is integrated inside the SQL Server DBMS platform. Microsoft also promotes two cloud SaaS products: Microsoft Flow and Azure Logic Apps. Flow is adhoc integrator-centric and integrated into the overarching Azure Logic Apps solution.
Oracle provides a full spectrum of data integration tools for modern ones as well as conventional use cases, in both cloud and on-prem deployments. The company’s product portfolio includes services and technologies that permit organizations for data enrichment and full lifecycle data movement. Oracle data integration allows permanent and uninterrupted access to data across heterogeneous systems via transformation, bidirectional replication, bulk data movement, data services, metadata management, and data quality for product and customer domains.
SAP provisions clouds and on-prem integration functionality by two primary channels. Traditional capabilities are provided through a data management platform, SAP Data Services, that gives capabilities for data cleansing, integration, and quality. SAP Cloud Platform provides Integration Platform as a Service features are existing in it. Integration of processes and data between cloud apps, third-party applications, and on-prem solutions are arranged through SAP’s Cloud Platform.
This chapter has been framed into seven sections. Section 1 explains the introduction, sub section 1.1 discusses the motivation and significance of the study. Sub section. 1.2 shows international market potential and 1.3 presents an overview of big data technologies and taxonomy. Sub section 1.4 Organization of the chapter, 1.5 summarizes the authors’ research contribution. 1.6 Illustrates the list of acronyms used. The review of recent literature is described in Section 2. Section 2 is further divided into four subsections. 2.1 subsection describes the papers reviewed from one technology domain. The highlights and findings from each paper are tabulated. Sub section 2.2 deals with a comparative analysis of survey papers with specific parameters. Section 3 shows the architecture of BDI. 4th section deals with the research issues, challenges. Section 5. Presents the case studies of BDI solutions from various organizations. Section 6. Outlines the findings and conclusion. Section 7 is the references of the papers reviewed.
This study has revealed that various technologies, systems, techniques, algorithms are applied for implementing business intelligence systems across the world. These papers have been further classified technology-wise and presented as a pie chart in Figure 1.
A table of acronyms is presented in Table 1
Figure 2 presents taxonomy of concepts applied in BDI techniques in various applications
The overview of the organization of this chapter, section-wise is shown Figure 3.
In each concept of taxonomy, the existing literature has been mapped to several issues as shown in Figure 4.
Research issues, challenges and future directions of BDI technologies are discussed
A case study of five BDI based solutions implemented healthcare, retail, finance and tourism domains are discussed
The set of a curated survey papers are compared with specific factors such as architecture, open issues and challenges, applications, taxonomy and security to understand the scope of coverage each paper and to understand the research gaps Tables 2 and 3
Bigdata integration platforms.
Acronym | Description | Acronym | Description |
---|---|---|---|
AI | Artificial Intelligence | BDI | Big Data Integration |
AWS | Amazon Web Services | AIG | American International Group |
B2B | Business to Business | CAGR | Compound Annual Growth Rate |
CCTV | Closed Circuit TV | ETL | Extract, Transform, Load |
EII | Enterprise Information Integration | EDR | Enterprise Data Replication |
HIPAA | Health Insurance Portability and Accountability Act | ICT | Information and Communication Technology |
IoT | Internet of Things | MDM | Master Data Management |
IoMT | Internet of Medical Things | ||
SOC 2 | Service organization control is an auditing procedure | API | Application Programming Interface |
List of acronyms used in this chapter.
Taxonomy of big data concepts.
Organization of the chapter.
Sector-wise reviewed papers.
This section shows a list of all the acronyms used in this chapter for easy reference is presented Table 1.
Authors have selected papers from highly reputed research journals from IEEE, Elsevier, Science Direct and Springer publications. About fifty-five papers covering big data integration concepts and applications are reviewed. This section presents the findings and highlights from each reviewed paper which are organized domain-wise.
Hasliza et al. analyzed the fundamental problems and difficulties encountered by the BDI solutions in the public sector [1]. The discovery of the right dimensions and factors are important to find the solutions to these problems. Zhang has reported the BDI solutions for professional procedure amalgamation in modern decorum [2]. The comparisons, experiments and questionnaires concerning the BDI concepts are discussed. Bansal has proposed the use of semantic technologies for the distribution of information in the contest of semantic ETL [3]. This information is open and the data was gathered from various sources. Zheng et al. have presented significant standards, classification of strategies and models of BDI process [4]. The real BDI issues are discussed using these models.
Authors have classified BDI techniques based on different combinations of strategies such as stage, feature and semantics. Munne has explained the technological trends for current social and economical status. This paper highlighted BDI technologies and applications in the public sector [5]. Table 2 presents a summary of the highlights of the papers reviewed the public sector.
Ref. No. | Methods Used | Results | Applications |
---|---|---|---|
1 | Interviewing of experts and content analysis approach was used as a qualitative technique for data gathering. | The principal problems identified during the study are the hardship of administration, the ineffectiveness of human resource, politics, standards and absence of executives | Data fusion solutions for public sector |
2 | Comparisons, experiments and questionnaires | The decorum status of high recognition of positive viewpoint is around 65%. Negative perspective has an absence of etiquette knowledge accounted for 56% | Talent refinement of college students |
3 | Semantic data model, Resource description system, SPARQL, semantic query language | Semantic extract transform load system produces semantic information that would possibly be distributed on the web as Web of data. | Innovative Big data applications in fuel economy, household transportation and vehicles |
4 | Exploration of BDI based on stage, features and semantic meaning | Big data problems are resolved by appropriate BDI methods. | Open cross-domain Big data |
5 | A study on analysis of industrial needs and potential applications of BDI in the public sector | A set of open research questions such as scalability of data required in real-time applications | Labor agency, Online gambling operations, Public Safety, and Predictive policing |
Public sector review summary.
The study by Camargo et al. revealed the possibility of incorporating and implementing BDI technologies to the needs of small and medium scale enterprises [6]. Some companies are offering open source BDI tools for organizations for intelligent business decision making. Stonebraker et al. discussed the difficulties in the scalability of BDI solutions today and in near future [7]. This analysis was carried out using the past five years’ data from large enterprises. The integration of data from heterogeneous sources in a distributed environment was explored by Sazontev et al. [8]. The authors have explained the process of BDI framework development and its methodology. Alsghaier et al. discussed BDI process in Hadoop platform for business organizations. Authors focused on the implementation and benefits of big data analytics in business organizations [9].
Alam et al. have reviewed the role of BDI in the business sector [10]. Table 3 presents a summary of highlights from the business sector review.
Ref. No. | Methods Used | Results | Applications |
---|---|---|---|
6 | Review of literature on BDI, Business Intelligence and Cloud Computing | It is possible to integrate technologies to the need of SMEs | Small and Medium-sized organizations |
7 | Developing a deployable data integration tool that handles technical issues. | Shortage of machine learning examples, the requirement of clarifying business owners’ outcomes and the expense of involving domain experts. | Scalable data integration challenges in the enterprise |
8 | The amalgamation of diverse sources in the Hadoop environment with HDFS. Spark computation model with a Hive database as a distributed data warehouse | A prototype of a data integration system | E-Commerce Domain |
9 | A study on data collection, analytics implementation, and benefits of BDI | BDI implementation in business organizations improves business performance | Performance improvement in business organization |
10 | Randomly selected articles on big data are reviewed to analyze the role of big data in business | As per the study, 63% of business reported that the implementation of big data is useful to business | Decision making in business |
Business sector review summary.
Fikri et al. presented a BDI approach combined with distributed datasets of financial ontology and a real-time data stream [11]. This model was associated with classic ETL. This model was suitable for handling BDI in real-time. Bucea-Manea-Tonis illustrated the use of predictive logic in deductive frameworks to integrate different sets of data types [12]. Chen et al. have proposed a framework for managing data with heterogeneity problems [13]. This unified data model was adaptable to different data sources by setting up panoramic data. Hussain and Prieto discussed the analysis of industrial needs, constraints and potential applications of BDI to insurance and finance sectors [14]. Authors have mapped the requirements to research queries. The paper by Avi and Kamaruddin reported the role of BDI in insurance, finance and banking sectors [15]. Authors have highlighted the benefits of cutting-edge technologies associated with BDI in the financial sector. Table 4 presents a summary of highlights from the finance sector review.
Ref. No. | Methods Used | Results | Applications |
---|---|---|---|
11 | Combining distributed datasets of financial ontology and real-time stream | The data integration pipeline in real-time. The use of Apache Spark enhances short time frames for quality and availability reporting | Data integration in real-time, Financial reporting |
12 | Predicate logic in deductive systems | Integrates different kinds of data types | E-Commerce applications |
13 | Integrating heterogeneous data from multiple sources, Big data ETL in a distributed environment | Better performance in processing data integration from multiple sources | Power dispatching and control system |
14 | A review of industrial needs, constraints and application | Highlights the challenges in providing an effective technological solution | Manipulation recognition, threat management in finance and insurance sectors |
15 | A comprehensive review of the banking sector in terms of digital banking, analytics, mobile banking. Use cases of the latest technologies in the banking sector | Various business problems are solved by using the latest technological trends and big data analytics in the banking industry | Big data analytics for the banking industry |
Finance sector review summary.
BDI and data analytics concepts are emphasized by Nabrzyski et al. [16]. This proposed solution incorporates the execution of complex queries on various datasets. These data sets contain the layers of raster and geospatial data. Kim and Tam (2020) have proposed a data integration estimator [17]. This is a classification technique with non-parametric and overlapping units which recognizes and corrects misclassification errors. Saggi and Jain (2018) have reported a data analytics solution for organizations [18]. This solution performs an exhaustive realistic analysis. The components of BDI application platforms are discussed. Authors have thrown light on past, current research issues and future directions.
Ribarics (2016) explained the importance of big data in agricultural sector [19]. The author has highlighted the need for using technological innovations in farming. Sarker et al. (2020) discussed the impact of BDI in digital farming [20].
The study results showed that big data analytics helps the farmers in crop management and yield forecasting. This study also revealed that BDI in farming is not fully established. Table 5 presents a summary of highlights from the agriculture sector review.
Ref. No. | Methods Used | Results | Applications |
---|---|---|---|
16 | Data acquisition and semantic integration, statistical data analysis, data visualization, data query language, and geospatial data techniques | Data integration and big data analytics solution are discussed | Agriculture decision support system. Helps the policymakers to implement restoration strategies |
17 | Identifying overlapping units, matching variables, and classification methods | Estimation of the missing data stratum, independent probability of sample infinite population | Agricultural census data analysis of Australia for the year 2015–2016 |
18 | Characteristics of BDA, architecture, technologies, the relationship between value creation and BDA, applications | Big data analytics framework for value creation | Smart city, cybersecurity, agriculture and healthcare domains |
19 | Summary of Oracle’s strategic white paper on Big data applications | Big data analytics as technological innovation in farming | Farming and food production |
20 | The comprehensive review reveals the impact of big data infarming | Farming is not fully equipped with big data technologies. | Big data analytics helps the farmer in crop management and forecasting |
Agriculture sector review summary.
Kaur and Kushwaha (2018) were motivated by different applications of BDI and IoT integration in smart cities [21]. The earlier researchers reviewed the critical data analysis issues. Huang et al. (2014) have proposed HiperFuse solution for addressing BDI challenges and automating the BDI process [22]. Nuaimi et al. (2015) reviewed the prospects, issues and advantages of BDI in smart cities. This study discussed the BDI challenges faced in smart cities [23]. Gomes et al. (2016) demonstrated a smart city project model using BDI solutions in Brazil [24]. This project proposed a model that can be hosted in big data servers.
Alshawish et al. (2016) discussed the role and potential of BDI solutions in smart cities [25]. The authors have explained the complete process of BDI applications in smart cities.
This study has incorporated some real-world examples of smart city components. Table 6 presents a summary of highlights from the smart cities review.
Ref. No. | Methods Used | Results | Applications |
---|---|---|---|
21 | Various technologies for the handling of big data and IoT integration | A new data architecture that supports IoT and other data resources | Critical data analysis solution for IoT and Big Data |
22 | Data mixing planner, domain-specific data models, robust type inference, and declarative interface | Automates the data integration process and leverages key capabilities | Website visitors income analysis, retail business analytics |
23 | Literature survey on prospects, issues and advantages of big data technologies in smart cities | Big data applications for smart use of data and operations in smart cities | Effective management of smart city resources |
24 | Design of smart city project model using big data in Brazil | This software can be used in big data servers | Software for smart city project in Brazil |
25 | Collecting data from networks, processing data with various stages and visualization data | Big data-driven smart city improves smart city applications | Smart Energy, Smart public safety and Smart traffic systems. |
Smart cities sector review summary.
Ahmed et al. (2016) have proposed a Generating Attributes with Rolled Paths (GARP) algorithm that creates a mining table attributes from multiple data sources [26]. The experiments were carried out on the U.S. consumer electric retailer dataset and revealed that classification accuracy was improved by using GAPR. Bennani et al. (2014) have reported a guided BDI solution with Service Level Agreement (SLA) for querying data from multiple clouds [27]. The methodologies and algorithms designed are applied to energy utilization. Product planning, product design, manufacturing and maintenance process are reviewed in terms of concepts and applications. Qi and Tao (2018) provided a 360-degree review of big data in smart manufacturing [28]. Product planning, product design, manufacturing and maintenance processes are reviewed in terms of concepts and applications. Hufnagel et al. (2015) demonstrated a distributed integration model applicable to the manufacturing industry [29]. This research has created the user-oriented integration platform using a modular approach. O’Donovan et al. (2015) reported a detailed review of BDI implementation in the manufacturing sector. This study has provided a detailed review of big data research in manufacturing [30]. Table 7 presents a summary of highlights from the manufacturing sector review.
Ref. No. | Methods Used | Results | Applications |
---|---|---|---|
26 | Automatic generation of discriminant features, aggregation of information from multiple resources | Classification accuracy improvement and discriminant feature generation. Mitigates the impact of class imbalance | Consumer electronic retailer in Circuit City U.S. |
27 | The economic model of the cloud referred for lookup, aggregation and correlation in SLA data integration, handling SLA interoperability and collaboration | A distributed data as a service for SLA guided data aggregation framework | Energy consumption applications, data integration of political campaign and electronics |
28 | Compare and contrast of digital twin and big data. Product planning, product design, manufacturing and maintenance process are reviewed in terms of concepts and applications | Digital twin and big data have great significance in smart manufacturing | Smart manufacturing in workshop or factory |
29 | Featuring missing connection between successful business integration concept and proven graphical description | User-oriented integration platform using a modular approach | Workflows and product life cycles in the manufacturing industry |
30 | Captured the status of big data research in manufacturing, and compared the secondary research studies | Usage of big data technologies in manufacturing for maintenance and diagnosis | Various manufacturing domain |
Manufacturing sector review summary.
Hardiman has explored BDI methodologies for Omics data and network algorithm development [31]. The objective was to channel the gap between phenotype and genotype which were not applied earlier. These researchers used spectrometry permitted geneticists, deep sequencing technologies, biostatisticians and biologists. Bhandari et al. have explained HGBEnviroScreen in their paper [32]. This is an EJ mapping tool providing the key services online to local decision-makers and communities. This study has resulted in multiple risk factors leading to the largest vulnerability census tracts. These risk factors lead to natural disaster, social vulnerability and flooding. Shayne et al. have carried out a comprehensive study of integration solutions for big medical data [33]. This study has covered the applications, tools and technologies of BDI in the healthcare domain. Eftekhari et al. have proposed software as a service architecture [34]. This provides backend infrastructure for database access operations on data from different data sources. This methodology was approved with a proof-of-concept prototype developed on the OpenStack cloud architecture. Vidal et al. have presented a knowledge-driven framework [35]. This framework extracts knowledge from short text and unstructured data.
This framework used controlled vocabularies and ontologies to clarify the extracted entities and relations. Husain et al. have reported SOCR data dashboard design, implementation, and testing. SOCR does exploratory questioning of multi-source and heterogeneous and datasets [36]. Table 8 presents a summary of highlights from the healthcare sector review.
Ref. No. | Methods Used | Results | Applications |
---|---|---|---|
31 | Network algorithms and Gene ontology path are followed | Chanel the gap between phenotype and genotype on a scale using high throughput techniques | Biomedical, clinical and Omics data integration |
32 | Five domains data collected at HGB region for the year 1990 and designed EJ mapping tool for community online services | Online services for decision-makers and community by EJ mapping tool | Usage of result in a community action plan by community partners |
33 | Usage of various tools, techniques and applications of data integration in the healthcare domain. Analysis of integration techniques abilities to handle speed, variety and uncertainty. | Strength and weaknesses of various solutions, and its findings | Healthcare big data integration |
34 | Designing Big data store by collecting data from multiple sources. Web interface and RESTful APIs for the integration of RDBMSs with non-relational databases. The queries on such remote databases by proof of concept. | SaaS framework for integrating multiple data sources performing operations such as data access, querying and visualization | Ad-hoc querying of health care datasets |
35 | Data integration of multiple data resources, Knowledge-driven framework for data description that uses knowledge graph | Ontologies and unified schema as a knowledge graph for describing integrated data | Discovery of interactions among drugs in treatments with much faster running time prescribed to lung cancer patients |
36 | Human-machine interface for integration of data from heterogeneous resources in a secure and scalable way | Human-machine interactions customization | Service-oriented infrastructure for healthcare data. |
Healthcare sector review summary.
Cheng et al. proposed a remote sensing data management system [37]. This system is distributed multisource and followed the MongoDB model. The remote sensing, data integration and access are examined by designing a set of experiments.
Wang et al. have described the major aspects of BDI such as characteristics, advantages, platform architecture, and application areas in telecommunication [38]. This research can be extended by improving multiple levels of protection technologies in the big data platform. Yayah et al. explained a few use cases of machine learning implementation in big data platforms [39]. Scalability and extensibility are the parameters used for the evaluation of BDI technologies. Nwanga et al. studied the impact of big data analytics in mobile phone industry [40]. This study has revealed that BDI solutions and big data analytics has an impact on the growth of the telecommunication industry by adding huge data insights. Table 9 presents a summary of highlights from the communication sector review.
Ref. No. | Methods Used | Results | Applications |
---|---|---|---|
37 | Multi-Source BDI framework, Spatial Segmentation Indexing Model, integration based on distributed storage | Scalable storage data integration architecture, latest technical support and development | Professional remote sensing big data |
38 | Introduced and reviewed major aspects of big data such as characteristics, advantages, platform architecture, and application areas in telecommunication | Internal data applications enhance the efficiency of big data applications. External cooperation provides better services. | Development in telecommunication organization |
39 | Integrating machine learning tools in the Hadoop platform | Adoption and improvement of big data in the telecommunication industry | Telco, Retail, Financial Services and Energy sector |
40 | Comprehensive study and analysis of the impact of big data in the mobile industry | Big data analytics has impact on the growth of the telecommunication industry | Growth of the telecommunication industry with help of big data |
Communication sector review summary.
Antonio et al. presented a literature survey of simulation techniques in supply chain risks [41]. The authors have highlighted the significance of BDI in supply chain systems. This analysis has concluded that the problem at hand is simplified without complexity in modeling. This study has complied with industry 4.0 standards. Ostrowski et al. have explored the potential of semantic web technologies by demonstrating a case study in the supply chain [42]. Authors have identified the system for supporting data from multiple sources. This study was carried out by semi-automated mapping using shared domain ontology. Awwad et al. provided a review on applications, advantages and issues of BDI technologies for the supply chain management [43]. The supply chain risk management was carried out using data analytics by making a proactive decision. Lia and Liu have illustrated a data-driven framework for supply chain management [44]. The various circumstances of the supply chain are accommodated by enabling multiple working modes. Benabdellah et al. discussed the impact of big data on supply chain management [45].
The survey of various supply chain operation reference model presented their applications and challenges. Table 10 presents a summary of highlights from the supply chain sector review.
Ref. No. | Methods Used | Results | Applications |
---|---|---|---|
41 | Literature survey of simulation techniques for the analysis and synthesizing risks in supply chains | Analyzed the impact of risks in supply chains | Supply chain management |
42 | Semantics with annotation in Ontology federation process, Integration of data from multiple resources using shared domain ontology | Integration of data from multiple resources using semi-automated mapping | Supply chain risk detection |
43 | Detailed review on applications, advantages and issues of big data technologies for supply chain management | Infrastructure and human skillset need to be improved, new and effective techniques need to be developed | Supply chain in manufacturing and logistics |
44 | Design and development of a data-driven framework for supply chain management | Multiple working modes of big data in supply chain management | Power split device in hybrid vehicles |
45 | A detailed survey of various supply chain operations reference model with opportunities and challenges. | Studies revealed that the supply chain process is having higher importance | The supply chain and manufacturing products |
Supply chain sector review summary.
A review by Li presented BDI technology applications for the analysis of Chinese and Russian dance components with modern features [46]. Arputhamary and Arockiam presented the prominence of BDI by identifying the open problems and the same is extended to proceed with future research in the big data environment [47]. Kadadi et al. have surveyed BDI methods and their interoperability [48]. Authors have also explored its usage in big data setup and the corresponding challenges. Ostrowski and Kim have presented a BDI strategy based on ontology [49]. BDI strategy was implemented in Apache Spark prototyping environment that generates ontology versions using rule-based translations. Sottovia et al. have described the Research Alps project pipeline. This project was funded by the EU Commission [50]. They have created an open dataset providing Alpine area research centre details. Portugal et al. have presented a high-level spatial–temporal architectural framework for massive data integration, analysis and provenance management [51]. This methodology was applied for BDI analysis. Table 11 presents a summary of the highlights of the research review.
Ref. No. | Methods Used | Results | Applications |
---|---|---|---|
46 | Big Data Technology | Combination of Chinese and Russian cultural and modern features | Dance elements |
47 | Importance of BDI issues and challenges are identified | The existing techniques and approaches are inefficient to handle the problems. | Possible research directions |
48 | Addressing challenges of BDI such as Data accommodation, Data irregularity, Query optimization, Extensibility, ETL processing | Big data integration architecture | BDI within the organization and inter organizations |
49 | Ontology-based data integration, creation of new ontology versions by using rule-based translation | Multiple data sources ‘Semi-automated mapping | Large scale Big data applications |
50 | M-STEP and entity matching method and functional framework to deal with hierarchical data instances | Open dataset providing Alpine area research centres details | Research Alps project funded by EU commission |
51 | Domain experts focusing on appropriate analysis steps, high-level models linked with code produces middleware | Model-driven techniques resulting in data integration and analysis | Provenance information |
Research domain review summary.
Large scale implementation of BDI solutions is a very complex and difficult process than automating data transformation processes. To reduce the complexity, the organizations should implement the procedures for data discovery, semantic or business comprehension of data, metadata management, structured and unstructured data management, and transformation. Integrating unstructured and semi-structured data enables organizations to manage modern data sources containing text, images, and video. A survey was conducted by AtScale Inc. in collaboration with Cloudera and ODPi.org reveals that most of the organizations are selecting multi-cloud strategies for BDI implementation. Data virtualization and data governance are their top priorities [52]. This survey has collected data from 150 data practitioners where the respondents are from multiple industries around the world. The online magazine “Smarter with Gartner” has reported that top ten technology trends in data analytics require essential investments [53].
This article revealed that the combination of machine learning algorithms and data technologies could help the medical and public health experts to discover new possible treatments. The article entitled “2020 CRN Big Data 100” published in “Data Integration Solutions Review” enlists the emerging big data tool vendors [54]. This list provides the details of data integration software, tools, platforms and vendors. A data-driven technique for a hybrid BDI using multilayer perceptron was discussed in this research [55]. A customized multilayer perceptron model was constructed using time-based parameters. The fields applied in optimization analysis are also used in the error matrix through additional neural network model. Research results revealed that this solution captures the variations in state variables. BDI project implementation for COVID-19 analytics was discussed [56]. This project was funded by the European Union research fund. This platform combines information from multiple sources such as world news, social media, published science and health data from healthcare institutions. The project design was co-created with industry, academia, health professionals, and policymakers to align with innovative technologies. This project successfully provides useful and actionable information to public health authorities.
Authors have selected fifteen BDI survey papers for comparative study. The specific feature parameters such as 1. Architecture, 2. Applications, 3. Open Issues and Challenges 4. Taxonomy, 5. Security, 6. Future Directions are used for comparing these survey papers. The Comparative analysis results are shown in Table 12.
Authors | Year | Study Objectives | Advantages | Disadvantages | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|---|---|---|---|
Fikri et al | 2019 | To get solutions for financial data real-time integration issues and interpretation of data | This real-time data integration solution resolves earlier issues of classic ETL tools | This solution cannot be integrated with a hot production setting | Y | Y | Y | Y | N | Y |
Cheng et al | 2020 | To design BDI distributed architecture for remote sensing data, where these data from multiple sources | Improvement in performance with a distributed architecture for remote sensing data | Time and resource complexity to handle various pre-processing steps in data integration | Y | Y | Y | N | N | Y |
Authors | Year | Study Objectives | Advantages | Disadvantages | 1 | 2 | 3 | 4 | 5 | 6 |
Bhandari et al | 2020 | To develop EJ screening, an adaptable and community-based tool for the region Houston Galveston Brazoria | Risk factor identification and understanding among the communities | reducing environmental disparities and improving their health and well-being | Y | Y | N | N | N | Y |
Vieira et al | 2020 | To conduct a literature survey of simulation methods used for handling risks in the supply chain with an emphasis on data integration | Simplification of the problem in the absence of complex modeling | It is required to focus on supply chain real cases | N | Y | Y | Y | N | Y |
Stonebraker et al | 2018 | To explore issues of BDI related to scalability in enterprises at Tamr region | Automation by machine learning and rule-based approach for augmenting | Involves high cost for domain experts, shortage of training data | N | Y | Y | Y | N | Y |
Ahmed et al | 2016 | To aggregate data from local and external resources, to generate mining table from these, automatic generation of potential discriminant features | Classification accuracy improvement and thus mitigates the impact of class imbalance | Time complexity is linear and it is required to reduce computation time with an efficient method | Y | Y | N | Y | N | Y |
Bansal | 2014 | BDI by designing a Semantic Extract-Transform-Load architecture | Publishing semantic data on the internet and thus contribute to the web of data | It is required to understand the heterogeneity of data i.e. ontology engineering | Y | Y | Y | N | N | Y |
Dhayne et al | 2019 | To study healthcare data integration methods, tools, and applications | Wide range of healthcare data integration concepts, techniques and tools are covered | Data integration in the healthcare sector could not be done efficiently using traditional way | Y | Y | Y | Y | Y | Y |
Sazontev et al | 2019 | To develop a prototype of a big data integration system | Useful for e-commerce data integration domain | Lacks in methods for schema alignment | Y | Y | N | N | N | Y |
Chen et al | 2015 | To accomplish data integration of back-end datasets in a complete manner | Data movement is faster than that of Spark thus achieved optimization | Integration of more Spark modules is not supported | Y | Y | N | N | N | Y |
Zheng et al | 2015 | To summarize categories and its subcategories of data integration techniques | Extensive details of big data integration solutions for communities | Since BDI methods behave differently in different applications it’s difficult to select the best data fusion technique | Y | Y | Y | Y | N | Y |
Huang et al. | 2014 | To automate the data integration process and leverage key capabilities | A more agile process for compelled analysis by generating a subset of data | HiperFuse modules are implemented separately yet to be integrated | Y | Y | N | N | N | Y |
Portugal et al | 2016 | To perform spatial and temporal data analysis for assisting domain experts | High-level representations by domain specific languages, data analysis and integration by model-driven techniques | provenance technologies need to be used in related spatial–temporal approaches | Y | Y | Y | N | N | Y |
Saggi et al | 2018 | To bridge the gap by big data processing and analytics | A comprehensive review of big data projects in terms of analytics, management, and machine learning | It is required to carry out empirical research based on qualitative and quantitative methods | Y | Y | N | Y | N | Y |
Kim et al | 2020 | Survey sample data approach to handle big data integration | Recognition of overlapping units and correction of misclassification errors | Statistical inference variance estimation with non-parametric propensity score tuning is not covered | Y | Y | N | Y | N | Y |
Comparative Analysis of curated survey papers with specific parameters.
The outline architecture of the BDI ecosystem is shown in Figure 5. This architecture has four major components. These components are Data Sources, Data Operations, Virtual Databases and Business Intelligence. This architecture also shows the operations performed by each of these components. The business Big data would be collected from various distributed sources in different formats and sizes in Data Sources component. The Data Operations component shows the different operations which are performed on this heterogeneous Big data.
The architecture of the BDI ecosystem.
The Big data gathered from various types of physically distributed databases are integrated to form a unified logical virtual database. Business intelligence information is extracted from this virtual data source by performing the operations stated in Business Intelligence Component. This intelligent information would be used for real-time intelligent business decision-making process across the organization.
The research issues, challenges and future directions related to BDI implementation are discussed in the following sections.
Scalability - Scalable architectures for parallel big data processing
Real-time big data analytics - Stream big data processing of text, image, and video
Deployment of the IoMT, IoT and CCTVs systems in smart environments would capture big data continuously. Processing multimedia big data in real-time with low latency and high accuracy
The balancing of big data processing load at the edges and distributed to the hybrid cloud securely
Implementing real-time, complex big data analytics in the cloud by reducing the cost of operations
Ensuring authorization, authentication, security and privacy at the edges and cloud.
Efficient storage and transfer of big data in real-time
Efficient modeling of uncertainty with unlabeled big data
Management of graphical big databases
Social media analytics using efficient graphical processing.
Quantum computing for big data analytics
Building context-sensitive large scale systems
Extracting actionable information from BDI solutions
Synchronization of data across heterogeneous data sources
Lack of comprehension and management of uncertainty
Effective anonymization of sensitive fields in the largescale data systems
Support for scalable privacy preservation during BDI processing
Generating process models that learn with a smaller number of data samples
Building context-sensitive large scale systems:
BDI Talent shortage
Everyone is adopting Software as a Service (SAAS)
Self-service has evolved to self-sufficiency
Shared data, visualizations and storytelling are consumed by all
Now constant updating of business-ready data is very vitaa
Support for advanced analytics with different perspectives
It is critical to gather and create alternative big data
Every business is undergoing re-engineering process
The measures for competition, surveillance and security are constantly redefined
Collaboration has to coalesce earlier in the chain
The great digital switch may force a generational shift in analytics.
Improved e-commerce sales and operations efficiency
Creating efficient marketing strategies
Increased security enforcement
Improving fraud prevention;
Enhancing user experience
Increased profits
Authors have presented a set of real-life case studies of BDI solutions implemented successfully across the business domains in organizations. Figure 6 shows the domains and tolls used in that domain for illustration.
Case study domains.
It is a global leader in retail and wholesale pharmacy business operating in the U.S. and Europe and has more than 170 successful business years of serving humanity. Walgreens Boots Alliance, Inc., declared its IT collaboration with Microsoft and Adobe to introduce a world’s best digital platform for enhanced customer experience and data insights to offer truly customized healthcare, adhere to their healthcare plans and shopping services as stated by their global chief marketing officer Vineet Mehra. The BDI systems can manage 7.5 billion medical transactions 100 million citizens providing a singular, unified view of the customer information about demographics, registration, diagnoses, procedures, and data from managed-care plans.
This BDI digital platform helps the customers to access key services of pharmacy, beauty and other categories on daily basis. Data security and privacy are important principles in the design of Microsoft’s trusted cloud platform. Walgreens has introduced personally customized prescription understanding for patients at Walgreens, Dynamics 365 Customer Insights would serve as WBA’s Customer Data Platform (CDP) provided by Microsoft. CDP provides a unified, 360-degree perceptions of the customer and reveals the details to leverage personal experience. Adobe’s Customer Experience Management (CXM) solutions leverage Walgreens to offer supreme customer experience, with end-to-end platform for analysis, managing content, customization, campaign composition and many more. Walgreens Company also extends collaboration with Tata Consultancy Services to build highly scalable, maintainable and world-class unified IT operating platform to enable digital transformation, innovation and automation of services offerings. Walgreens Boots Alliance also collaborates with Hortonworks to offer excellent customer satisfaction.
AIG Data Services Pvt. Ltd. is a 100% owned subsidiary of American International Group Inc. It is a Fortune 500 company with revenues of the US $70 billion. AIG drives the best decision-making through BDI solution sutilizing business and customer big data across 130+ countries and 64,000 employees which is ever-growing. AIG has implemented sophisticated prediction models with 115 variables to analyze the past business transactions to forecast the potential trends. AIG identified 24% accounts in the Australian market that are about close in next four-month time. AIG has applied BDI tools and visualization systems to discover the frauds by detecting the false claims and adjuster handwritten notes to detect probable frauds. These tools offer insights into insurance claims and enhance machine learning algorithms. AIG creates data profiles and assesses vital data elements against pre-defined data quality standards on important business data for important applications. Today big data is distributed across the globe and facts are available across multiple sources. The team responsible for data sourcing uses ETL tools to provide a unified virtual version of these facts collected from various data sources. AIG has implemented Netezza and data virtualization technologies on Cisco Information Server. AIG also utilizes Hadoop, R, Python, SAS and other open-sourced/licensed tools to implement BDI solutions to beat the competitors. AIG uses tools such as QlikView, Tableau, Cognos and Micro strategy for data visualization.
These solutions are implemented using the latest techniques, algorithms, procedures and applications. The Kroger company gathers and processes the data from about 770 million consumers. Kroger has implemented BDI solutions for extracting more actionable information for profitability, customer loyalty. Kroger claims that 95% of sales are from the loyalty card. Kroger achieved about $12 billion in revenues by BDI implementing and analytics solutions since 2005.
This company has encashed on customer loyalty and relationships by providing boundless service through social channels and other data exchange mechanisms. Southwest utilizes speech analytics to help and enhance the exchanges between service professionals and customers. Southwest applied BDI solutions to understand customer online behavior and activities, increasing offers for customers and driving growth in customer satisfaction year after year. Delta has applied BDI solutions to support most painful travel condition that results in lost baggage. This company tracks the data about baggage and became the first airline company to permit customers to trace their baggage from smartphones. This company checks about 130 million baggage every. Delta is branding its self as a customer friendly services by permitting customers to download their apps over 11 million times and provides best customers with baggage secure services company.
This company has become number one online news site in the United States. According to this report, the company’s leadership believes in running the business based on big data. This involves enhancing the user experience in real-time through recommendations, moderation, social trends, and personalization. This company optimizes its portal in many ways, and its analytics platform powers the entire analytical process. Huffington Post utilizes data to comprehend and serve the customers well, make targeted advertising, and design innovative products based on information gathered. Their CEO informed that BDI solutions have transformed its business by intelligent and real-time decision making. This company utilized many data points to enhance relevance in their communications, analyze customer content preferences, and personalize the content all to keep traffic and visitors always. The BDI also benefits the company to comprehend the time of day consumption based on both mobile channels and PC.
This chapter reviewed the literature on BDI tools and applications in diverse industries and presented the highlights from each domain. All most all organizations are gathering a huge quantity of big data in real-time, Online and offline modes. Managing, real-time processing this big data to extract useful business information for intelligent decision making is the real challenge. The big data processing systems are empowered by big data integration and analytics platforms. BDI systems are facing the challenges in integrating and synchronization of heterogeneous big data from multiple distributed sources. The lack of comprehension and management of uncertainty in big data is another challenge faced in the big data processing. BDI processing should ensure context-sensitivity and extracting the semantics in the distributed data processing. Research on designing effective machine and deep learning algorithms is going on in the BDI domain. BDI Processing uses Hadoop environment with HDFS, Spark computation model with a Hive database as a distributed data warehouse. The use of Apache Spark enhances short time frames for quality and availability reporting. BDI processing involves data acquisition, semantic integration, statistical data analysis, data visualization, data query language, geospatial data techniques. Big data analytics framework enables us to create business value.
The economic model of the cloud promotes BDI processing by providing online services for decision-makers and the business community. Usage of various tools, techniques and applications of BDI leverages the ability to handle speed, variety and uncertainty. Knowledge-driven framework for BDI describes a knowledge graph. Human-machine interface for BDI integrates data from heterogeneous resources in a secure and scalable way. Ontologies and unified schema as a knowledge graph for describing integrated data. Multi-Source BDI is a framework for integrating data in the distributed storage environment. Authors have discussed BDI research issues and challenge data accommodation, data irregularity, query optimization, extensibility, ETL processing. Remote sensing data. Real-time big data analytics processes stream of big text, image, and videos generated from IoMT, IoT and CCTVs systems. Implementing real-time, complex big data analytics in the cloud using BDI process reduces the cost of operations. This paper discussed five case studies of BDI applications implemeneted in the in world-class organizations.
This chapter discussed the importance of BDI process implemented in diverse organizations for providing valuable insights into business data. These insights into the data enable the manager to take intelligent and well-informed rational decisions. An extensive study of literature on BDI applications deployed in diverse domains across the world was carried out and highlights are discussed. The intelligent and autonomous BDI systems are designed using AI, Blockchain, Big data, 5G, Fog and cloud technologies. The comparative analysis of specific parameters was carriedout on curated to survey papers to identify the research gaps and future opportunities in the BDI domain. The five case studies from fortune 500 companies have discussed the insights about how BDI is empowering business decision making leveraging quality, trust, security, flexibility, efficiency and also reduce the cost of operations. The authors attempted to provide a holistic view of BDI concepts and applications. Authors concluded that BDI plays a vital role in the diverse organizations at present and in near future also.
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Zaharia"},{id:"55977",title:"Dr.",name:"Gabriela",middleName:null,surname:"Ioana-Toroimac",slug:"gabriela-ioana-toroimac",fullName:"Gabriela Ioana-Toroimac"},{id:"91185",title:"Prof.",name:"Grecu",middleName:null,surname:"Florina",slug:"grecu-florina",fullName:"Grecu Florina"},{id:"91186",title:"Dr.",name:"Gianina",middleName:null,surname:"Neculau",slug:"gianina-neculau",fullName:"Gianina Neculau"}]},{id:"45249",doi:"10.5772/56414",title:"Challenges and New Advances in Ocean Color Remote Sensing of Coastal Waters",slug:"challenges-and-new-advances-in-ocean-color-remote-sensing-of-coastal-waters",totalDownloads:3285,totalCrossrefCites:11,totalDimensionsCites:27,abstract:null,book:{id:"3517",slug:"topics-in-oceanography",title:"Topics in Oceanography",fullTitle:"Topics in Oceanography"},signatures:"Hubert Loisel, Vincent Vantrepotte, Cédric Jamet and Dinh Ngoc\nDat",authors:[{id:"66224",title:"Prof.",name:"Hubert",middleName:null,surname:"Loisel",slug:"hubert-loisel",fullName:"Hubert Loisel"},{id:"99752",title:"Dr.",name:"Cédric",middleName:null,surname:"Jamet",slug:"cedric-jamet",fullName:"Cédric Jamet"},{id:"163462",title:"Dr.",name:"Vincent",middleName:null,surname:"Vantrepotte",slug:"vincent-vantrepotte",fullName:"Vincent Vantrepotte"},{id:"167707",title:"MSc.",name:"Dat",middleName:null,surname:"Dinh Ngoc",slug:"dat-dinh-ngoc",fullName:"Dat Dinh Ngoc"}]},{id:"66461",doi:"10.5772/intechopen.85139",title:"Fish Sperm Physiology: Structure, Factors Regulating Motility, and Motility Evaluation",slug:"fish-sperm-physiology-structure-factors-regulating-motility-and-motility-evaluation",totalDownloads:1898,totalCrossrefCites:11,totalDimensionsCites:26,abstract:"For reproduction, most fish species adopt external fertilization: their spermatozoa are delivered in the external milieu (marine- or freshwater) that represents both a drastic environment and a source of signals that control the motility function. This chapter is an updated overview of the signaling pathways going from external signals such as osmolarity and ionic concentration and their membrane reception to their transduction through the membrane and their final reception at the flagellar axoneme level. Additional factors such as energy management will be addressed as they constitute a limiting factor of the motility period of fish spermatozoa. Modern technologies used nowadays for quantitative description of fish sperm flagella in movement will be briefly described as they are more and more needed for prediction of the quality of sperm used for artificial propagation of many fish species used in aquaculture. The chapter will present some applications of these technologies and the information to which they allow access in some aquaculture species.",book:{id:"7912",slug:"biological-research-in-aquatic-science",title:"Biological Research in Aquatic Science",fullTitle:"Biological Research in Aquatic Science"},signatures:"Jacky Cosson",authors:[{id:"188281",title:"Dr.",name:"Jacky",middleName:null,surname:"Cosson",slug:"jacky-cosson",fullName:"Jacky Cosson"}]},{id:"20911",doi:"10.5772/19948",title:"The Significance of Suspended Sediment Transport Determination on the Amazonian Hydrological Scenario",slug:"the-significance-of-suspended-sediment-transport-determination-on-the-amazonian-hydrological-scenari",totalDownloads:4176,totalCrossrefCites:13,totalDimensionsCites:24,abstract:null,book:{id:"304",slug:"sediment-transport-in-aquatic-environments",title:"Sediment Transport in Aquatic Environments",fullTitle:"Sediment Transport in Aquatic Environments"},signatures:"Naziano Filizola, Jean-Loup Guyot, Hella Wittmann, Jean-Michel Martinez and Eurides de Oliveira",authors:[{id:"36890",title:"Dr.",name:"Naziano",middleName:null,surname:"Filizola",slug:"naziano-filizola",fullName:"Naziano Filizola"},{id:"60004",title:"Dr.",name:"Jean-Michel",middleName:null,surname:"Martinez",slug:"jean-michel-martinez",fullName:"Jean-Michel Martinez"},{id:"60005",title:"Dr.",name:"Jean-Loup",middleName:null,surname:"Guyot",slug:"jean-loup-guyot",fullName:"Jean-Loup Guyot"},{id:"102592",title:"Dr.",name:"Hella",middleName:null,surname:"Wittmann",slug:"hella-wittmann",fullName:"Hella Wittmann"},{id:"102593",title:"Mr.",name:"Eurides",middleName:null,surname:"De Oliveira",slug:"eurides-de-oliveira",fullName:"Eurides De Oliveira"}]},{id:"60698",doi:"10.5772/intechopen.74923",title:"Overview on Mediterranean Shark’s Fisheries: Impact on the Biodiversity",slug:"overview-on-mediterranean-shark-s-fisheries-impact-on-the-biodiversity",totalDownloads:1117,totalCrossrefCites:14,totalDimensionsCites:19,abstract:"Bibliographic analysis shows that the Mediterranean Sea is a hot spot for cartilaginous species biodiversity, including sharks, rays, and chimaeras; 49 sharks and 36 rays were recorded in this region. However, they are by far the most endangered group of marine fish in the Mediterranean Sea. The IUCN Red List shows clearly the vulnerability of elasmobranchs and the lack of data; 39 species (53% of 73 assessed species) are critically endangered, endangered, or vulnerable. The biological characteristics of elasmobranchs (low fecundity, late maturity, and slow growth) make them more vulnerable to fishing pressure than most teleost fish. Overfishing, the wide use of nonselective fishing practices, and habitat degradation are leading to dramatic declines of these species in the Mediterranean Sea. In general, elasmobranchs are not targeted but are caught incidentally. In many fisheries, they are, however, often landed and marketed. A decline in cartilaginous fish species landings has been observed while fishing effort has generally increased. Better understanding of the composition of incidental and targeted catches of sharks by commercial fisheries are fundamentally important for the conservation of these populations. Moreover, problems encountered by elasmobranchs in the area are highlighted, and conservation measures are suggested.",book:{id:"6266",slug:"marine-ecology-biotic-and-abiotic-interactions",title:"Marine Ecology",fullTitle:"Marine Ecology - Biotic and Abiotic Interactions"},signatures:"Mohamed Nejmeddine Bradai, Bechir Saidi and Samira Enajjar",authors:null}],mostDownloadedChaptersLast30Days:[{id:"60368",title:"Biological and Medicinal Importance of Sponge",slug:"biological-and-medicinal-importance-of-sponge",totalDownloads:2585,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Sponges are multicellular, heterotrophic parazoan organisms, characterized by the possession of unique feeding system among the animals. They are the most primitive types of animals in existence, featuring a cell-based organization where different cells have different tasks, but do not form tissues. Sponges (Porifera) are a predominantly marine phylum living from the intertidal to the abyssal (deepest ocean) zone. There are approximately 8500 described species of sponges worldwide with a prominent role in many reef coral communities. Several ecological studies reported have shown that secondary metabolites isolated from sponges often serve defensive purposes to protect them from threats such as predator attacks, biofouling, microbial infections, and overgrowth by other sessile organisms. In the recent years, interest in marine sponges has risen considerably due to presence of high number of interesting biologically active natural products. More than 5300 different natural products are known from sponges and their associated microorganisms, and every year hundreds of new substances are discovered. In addition to the unusual nucleosides, other classes of substances such as bioactive terpenes, sterols, fatty acids, alkaloids, cyclic peptides, peroxides, and amino acid derivatives (which are frequently halogenated) have been described from sponges or from their associated microorganisms. Many of these natural products from sponges have shown a wide range of pharmacological activities such as anticancer, antifungal, antiviral, anthelmintic, antiprotozoal, anti-inflammatory, immunosuppressive, neurosuppressive, and antifouling activities. This chapter covers extensive work published regarding new compounds isolated from marine sponges and biological activities associated with them.",book:{id:"6344",slug:"biological-resources-of-water",title:"Biological Resources of Water",fullTitle:"Biological Resources of Water"},signatures:"Musarat Amina and Nawal M. Al Musayeib",authors:[{id:"213049",title:"Dr.",name:"Musarat",middleName:null,surname:"Amina",slug:"musarat-amina",fullName:"Musarat Amina"},{id:"213050",title:"Dr.",name:"Nawal",middleName:null,surname:"M. Al Musayeib",slug:"nawal-m.-al-musayeib",fullName:"Nawal M. Al Musayeib"}]},{id:"59865",title:"Marine Fisheries in Nigeria: A Review",slug:"marine-fisheries-in-nigeria-a-review",totalDownloads:3937,totalCrossrefCites:9,totalDimensionsCites:11,abstract:"Fisheries production especially from marine is important for the socio-economic development of Nigerians and its contribution to the nation’s economic growth through the Gross Domestic Product (GDP). Nigeria is blessed with enough marine fisheries resources that could enhance increased fish production. Yet, fish supply from domestic production is far below the fish demand of her citizens. This chapter is therefore focused on marine fisheries in Nigeria. We adopted a desk review approach. This chapter is divided into different sections such as the Nigerian fisheries sector, marine fisheries resources in Nigeria, status of marine fisheries production in Nigeria, marine fisheries regulations, and constraints to optimal marine fisheries production in Nigeria. We concluded that the contribution of aquaculture to marine fisheries production has been low, compared to the marine capture fisheries production. Also, we noted that despite the availability of regulations, noncompliance by fisher folks has not helped to optimize marine fisheries production. We therefore recommended that the culture of marine fishes should be intensified. Marine waters should also be protected against destruction and pollution as a result of human activities. Available marine fisheries regulations should be enforced and violators of the regulations should be punished as stipulated in the regulations.",book:{id:"6266",slug:"marine-ecology-biotic-and-abiotic-interactions",title:"Marine Ecology",fullTitle:"Marine Ecology - Biotic and Abiotic Interactions"},signatures:"Olalekan Jacob Olaoye and Wahab Gbenga Ojebiyi",authors:null},{id:"57327",title:"Closed Aquaculture System: Zero Water Discharge for Shrimp and Prawn Farming in Indonesia",slug:"closed-aquaculture-system-zero-water-discharge-for-shrimp-and-prawn-farming-in-indonesia",totalDownloads:2527,totalCrossrefCites:2,totalDimensionsCites:4,abstract:"This chapter focuses on the development and application of zero water discharge (ZWD) system, which has become an alternative solution to conventional methods of aquaculture production. With this system, it is expected to answer many issues in aquaculture cultivation, such as environmental damage, disease outbreak, and land-use change, and to create a sustainable aquaculture cultivation system. ZWD system is an improved batch system with an emphasis on microbial manipulation in rearing tank. The principle of microbial selection is based on the role of each microbial component in nutrient cycle in the rearing tank. This chapter contains in detail how methods and stages are performed in order to conduct this system, including design of construction system, cultivation of microbial components, initial conditioning of this system, and microbial manipulation. The performance of the system was tested in crustacean culture such as white shrimp and giant freshwater prawns, and it showed that the system can increase the average survival rate of 10–20%. In addition, the technical and economic feasibility of this system was evaluated to illustrate the production efficiency upon the application of this system in the industry.",book:{id:"6344",slug:"biological-resources-of-water",title:"Biological Resources of Water",fullTitle:"Biological Resources of Water"},signatures:"Gede Suantika, Magdalena Lenny Situmorang, Pingkan Aditiawati,\nDea Indriani Astuti, Fahma Fiqhiyyah Nur Azizah and Harish\nMuhammad",authors:[{id:"216920",title:"Dr.",name:"Gede",middleName:null,surname:"Suantika",slug:"gede-suantika",fullName:"Gede Suantika"},{id:"220079",title:"Dr.",name:"Magdalena Lenny",middleName:null,surname:"Situmorang",slug:"magdalena-lenny-situmorang",fullName:"Magdalena Lenny Situmorang"},{id:"220081",title:"Dr.",name:"Pingkan",middleName:null,surname:"Aditiawati",slug:"pingkan-aditiawati",fullName:"Pingkan Aditiawati"},{id:"220082",title:"Dr.",name:"Dea Indriani",middleName:null,surname:"Astuti",slug:"dea-indriani-astuti",fullName:"Dea Indriani Astuti"},{id:"220083",title:"MSc.",name:"Fahma Fiqhiyyah Nur",middleName:null,surname:"Azizah",slug:"fahma-fiqhiyyah-nur-azizah",fullName:"Fahma Fiqhiyyah Nur Azizah"}]},{id:"59973",title:"Genetic Applications in the Conservation of Neotropical Freshwater Fish",slug:"genetic-applications-in-the-conservation-of-neotropical-freshwater-fish",totalDownloads:1716,totalCrossrefCites:3,totalDimensionsCites:9,abstract:"Neotropical fish correspond to approximately 30% of all fish species worldwide. The diversity of fish species found in Neotropical basins reflects variations in life-history strategies and exhibition of particular morphological, physiological and ecological attributes. These attributes are mainly related to different forms of feeding, life maintenance and reproduction. Today, fish populations are being threatened by anthropogenic actions that are having a visible impact on the natural state of continental aquatic ecosystems. The main causes are overfishing, non-native species introduction, reservoir-dam systems, mining, pollution and deforestation. The biology and population dynamics of the species are still unclear due to lack of research. Genetic tools can be useful resources for the conservation of Neotropical fish species in several ways. Molecular genetic markers are considered powerful tools to identify cryptic and hybrid fish and also allow the evaluation of the genetic variability and structure of populations of Neotropical ichthyofauna. Several analyses of molecular markers have been performed on Neotropical fish, including allozyme analysis, restriction fragment length polymorphisms in regions of DNA (RFLP), randomly amplified polymorphic DNA (AFLP), randomly amplified polymorphic DNA (RAPD), microsatellites, single nucleotide polymorphisms (SNPs) and mitochondrial DNA (mtDNA) markers. In order to analyse a high number of markers, next generation sequencing has allowed researchers to generate a large amount of genomic information that can be applied to the conservation of Neotropical fish.",book:{id:"6344",slug:"biological-resources-of-water",title:"Biological Resources of Water",fullTitle:"Biological Resources of Water"},signatures:"Vito Antonio Mastrochirico Filho, Milena V. Freitas, Raquel B.\nAriede, Lieschen V.G. Lira, Natália J. Mendes and Diogo T.\nHashimoto",authors:[{id:"215385",title:"Dr.",name:"Diogo",middleName:null,surname:"Hashimoto",slug:"diogo-hashimoto",fullName:"Diogo Hashimoto"},{id:"226741",title:"MSc.",name:"Vito",middleName:null,surname:"Matrochirico-Filho",slug:"vito-matrochirico-filho",fullName:"Vito Matrochirico-Filho"},{id:"226743",title:"MSc.",name:"Milena",middleName:null,surname:"Freitas",slug:"milena-freitas",fullName:"Milena Freitas"},{id:"226744",title:"MSc.",name:"Raquel",middleName:null,surname:"Ariede",slug:"raquel-ariede",fullName:"Raquel Ariede"},{id:"226745",title:"MSc.",name:"Natália",middleName:null,surname:"Mendes",slug:"natalia-mendes",fullName:"Natália Mendes"},{id:"226746",title:"MSc.",name:"Lieschen",middleName:null,surname:"Lira",slug:"lieschen-lira",fullName:"Lieschen Lira"}]},{id:"62582",title:"Mangrove Species Distribution and Composition, Adaptive Strategies and Ecosystem Services in the Niger River Delta, Nigeria",slug:"mangrove-species-distribution-and-composition-adaptive-strategies-and-ecosystem-services-in-the-nige",totalDownloads:2198,totalCrossrefCites:5,totalDimensionsCites:15,abstract:"Mangroves of the Niger River Delta grade into several plant communities from land to sea. This mangrove is a biodiversity hot spot, and one of the richest in ecosystem services in the world, but due to lack of data it is often not mentioned in many global mangrove studies. Inland areas are sandy and mostly inhabited by button wood mangroves (Conocarpus erectus) and grass species while seaward areas are mostly inhabited by red (Rhizophora racemosa), black (Laguncularia racemosa) and white (Avicennia germinans) mangroves species. Anthropogenic activities such as oil and gas exploration, deforestation, dredging, urbanization and invasive nypa palms had changed the soil type from swampy to sandy mud soil. Muddy soil supports nypa palms while sandy soil supports different grass species, core mangrove soil supports red mangroves (R. racemosa), which are the most dominant of all species, with importance value (Iv) of 52.02. The red mangroves are adapted to the swampy soils. They possess long root system (i.e. 10 m) that originates from the tree stem to the ground, to provide extra support. The red mangrove trees are economically most viable as the main source of fire wood for cooking, medicinal herbs and dyes for clothes.",book:{id:"6411",slug:"mangrove-ecosystem-ecology-and-function",title:"Mangrove Ecosystem Ecology and Function",fullTitle:"Mangrove Ecosystem Ecology and Function"},signatures:"Aroloye O. Numbere",authors:[{id:"215285",title:"Dr.",name:"Aroloye O.",middleName:null,surname:"Numbere",slug:"aroloye-o.-numbere",fullName:"Aroloye O. Numbere"}]}],onlineFirstChaptersFilter:{topicId:"105",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:122,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:33,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,annualVolume:11410,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,annualVolume:11411,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,annualVolume:11413,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,annualVolume:11414,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:20,paginationItems:[{id:"82526",title:"Deep Multiagent Reinforcement Learning Methods Addressing the Scalability Challenge",doi:"10.5772/intechopen.105627",signatures:"Theocharis Kravaris and George A. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. 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He also obtained an MSc in Molecular and Genetic Medicine, and a Ph.D. in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also completed a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital, England. Dr. Rezaei is a Full Professor of Immunology and Vice Dean of International Affairs and Research, at the School of Medicine, Tehran University of Medical Sciences, and the co-founder and head of the Research Center for Immunodeficiencies. He is also the founding president of the Universal Scientific Education and Research Network (USERN). Dr. Rezaei has directed more than 100 research projects and has designed and participated in several international collaborative projects. He is an editor, editorial assistant, or editorial board member of more than forty international journals. He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. 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Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. 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Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. 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His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. 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