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\n
1. Introduction
\n
The background of “set of strings” Framework (SSF) [1, 2, 3, 4], developed for adequate modeling of Big Data, is the representation of database as an updated Set of Strings, whose structure is defined by the current metadatabase (MDB), being a set of context-free generation rules in the sense of N. Chomsky [5].
\n
Augmented Post systems (APS), considered in this chapter, are result of deep integration in the one toolkit of two well-known formalisms—classical string-generating formal grammars and string-operating logical systems, proposed by E. Post [6].
\n
By this, the main features of APS are:
Deep connectivity with “Set of Strings” representation of databases.
Deductive capabilities, similar to those, which are inherent to various deductive extensions of the relational model of data, as well as to Prolog and Datalog [7, 8, 9, 10, 11, 12].
\n
The second feature provides selection of not only data, presenting in string databases (SDB) in the explicit form, but also data, which may be constructed (derived) from the explicit data by means of logical inference.
\n
This chapter is dedicated to the formal definition and substantial description of APS and their applications. The terminology and abbreviations are the same, as have been used when describing SSF.
\n
Section 2 is dedicated to the definition of syntax and mathematical semantics of APS, and Section 3 to their operational semantics. Procedural connection to APS is described in Section 4, and flow-processing APS (FAPS), based on the aforementioned connection, are described in Section 5. Implementation issues and applications of APS family are considered in Section 6. The conclusion contains proposals on further development of SSF.
\n
\n
\n
2. Syntax and mathematical semantics of the augmented Post systems
\n
The augmented Post system\n\nP\n=\n<\nS\n,\nD\n>\n\n is the composition of set S of the so-called augmented, or string (S-), productions, and set D of context-free generating rules, being metadatabase. Following the terminology of knowledge engineering, we shall call P also as APS-represented knowledge base (for short, APS KB).
\n
S-production\n\n\nσ\n=\n<\nq\n,\nd\n>\n\n is couple, the first component of which, named body, has the form of postproduction:
where \n\nm\n≥\n0\n\n and \n\n\ns\n0\n\n,\n\ns\n1\n\n,\n…\n,\n\ns\nm\n\n\n are terms.
\n
Term \n\n\ns\n0\n\n\n is called header, while terms \n\n\ns\n1\n\n,\n…\n,\n\ns\nm\n\n\n are called conditions. The set of conditions is unordered, i.e., S-productions:
where \n\n\n\ni\n1\n\n…\n\ni\nm\n\n\n=\n\n\nj\n1\n\n…\n\nj\nm\n\n\n=\n\n1\n…\nm\n\n\n are identical. The aforementioned set of conditions may be empty in general case, i.e., \n\nm\n=\n0\n\n. The S-production with the empty conditions set looking like
\n
\n\n<\n\ns\n0\n\n←\n,\nd\n>\n,\n\nE4
\n
is called S-axiom.
\n
Component \n\nd\n\n of S-production \n\nσ\n=\n<\nq\n,\nd\n>\n\n is set of rules\n\n\nγ\n→\nβ\n\n and is called here variables declaration.
\n
Rule \n\nγ\n→\nβ\n∈\nd\n\n is called variable declaration of\n\n\nγ\n\n, which defines \n\nV\n\nγ\nβ\n\n\n set of possible values (domain) of this variable:
where \n\nG\n\n is CF-grammar, corresponding to metadatabase \n\nD\n\n. It is essential that there is one and only one variable declaration \n\nγ\n→\nβ\n\n for every variable \n\nγ\n\n, having place in the body of S-production. S-production \n\nσ\n,\n\nwhich body \n\n\ns\n0\n\n←\n\ns\n1\n\n,\n…\n,\n\ns\nm\n\n\n has no any variables, i.e.,
\n
\n\n\ns\ni\n\n∈\n\nV\n∗\n\n,\n\nE6
\n
for all \n\ni\n=\n0\n,\n1\n,\n…\n,\nm\n\n, is called concrete S-production (for short CS-production). CS-production:
\n
\n\n<\n\ns\n0\n\n←\n,\nd\n>\n, \n\nE7
\n
such that \n\n\ns\n0\n\n∈\n\nV\n+\n\n,\nd\n=\n\n∅\n\n\n is called CS-axiom.
\n
S-production \n\nσ\n=\n<\nq\n,\nd\n>\n\ndefines set of CS-productions \n\n\nσ\n¯\n\n\n in the following way:
where \n\n\ns\ni\n\n\nδ\n\n\n are strings in terminal alphabet \n\nV\n\n, which are created by the replacement of variables \n\n\nγ\n1\n\n,\n…\n,\n\nγ\nk\n\n\n by strings \n\n\nu\n1\n\n,\n…\n,\n\nu\nk\n\n\n in the same alphabet, respectively; all occurrences of one and the same variable \n\n\nγ\ni\n\n\n in all terms \n\n\ns\n0\n\n,\n\ns\n1\n\n,\n…\n,\n\ns\nm\n\n\n are replaced by one and the same string \n\n\nu\ni\n\n\n.
then S-production \n\nσ\n∈\nS\n\n is correct to MDB \n\nD\n\n. This means that all terms of every CS-production from set \n\n\nσ\n¯\n\n\n are words of context-free language \n\nL\n\nG\n\n\n, and where G is context-free grammar with set of generating rules D.
\n
APS KB \n\nP\n=\n<\nS\n,\nD\n>\n\n is correct, if all S-productions \n\nσ\n∈\nS\n\n are correct to metadatabase \n\nD\n\n.
\n
Notion of the APS KB extensional, i.e., set of facts, which may be derived from the knowledge base by means of logical inference, is defined as follows.
\n
Let\n\n\nP\n=\n<\nS\n,\nD\n>\n\nbe correct APS KB. Consider
and extensional \n\nEx\n\nP\n\nof\n\nAPS\n\nKB\n\nP\n=\n<\nS\n,\nD\n>\n\n, i.e., set of facts, defined by this knowledge base, is fixed point of the sequence \n\n\n\nW\n\n0\n\n\n,\n…\n,\n\nW\n\ni\n\n\n,\n\nW\n\n\ni\n+\n1\n\n\n\n,\n…\n\n, which is in general case infinite:
\n
\n\nEx\n\nP\n\n=\n\nW\n\n∞\n\n\n.\n\nE14
\n
Evidently, due to P correctness,
\n
\n\nEx\n\nP\n\n⊆\nL\n\nG\n\n, \n\nE15
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and \n\nEx\n\nP\n\n\n is finite, if their exists \n\ni\n\n such, that
If we consider the notion of extensional of APS KB from the linguistic point of view, then \n\nEx\n\nP\n\n\n is a language in alphabet \n\nV\n,\n\n which, according to (15), is sublanguage of \n\nL\n\nG\n\n\n. At the same time, from the point of view of knowledge engineering, \n\nEx\n\nP\n\n\n is the join of set of facts\n\n\nw\n∈\n\nW\n\n0\n\n\n\n, which are known explicitly (they are called lower ground facts), and set of facts, which are derived from ground facts and/or another derived facts. As it is easy to see, set of ground facts \n\n\nW\n\n0\n\n\n\n is nothing else, than SDB, while S-productions with nonempty sets of conditions form APS KB intensional, providing the aforementioned inference.
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Now we can define semantics of language of queries to APS KB.
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Set-theoretical (S-) semantics of this language is similar to S-semantics of SDB query languages:
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\n\nA\n=\n\nW\n¯\n\n∩\nI\n\nE17
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where \n\n\nW\n¯\n\n\n is APS KB extensional and \n\nI\n\n is set of facts, which actuality check is the purpose of the query.
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Here we shall use the simplest query language, being set of couples \n\n<\ns\n,\nd\n>\n\n, where \n\ns\n\n is term and \n\nd\n\n is its variable declaration.
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Mathematical (M-) semantics of this language is based on (14) and the following evident equation:
Here, \n\n<\n\n\n<\ns\n←\n\n\nd\n>\n\n\n,\nD\n>\n\n is correct APS KB, which one-element set of S-productions—selection criterion—contains S-axiom, defining set of facts, which may belong to \n\nEx\n\nP\n\n\n.
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Example 1. Consider metadatabase from Example 2 of the chapter of this book, describing SSF. We shall add to it the following three rules:
\n\n<\nfact\n>\n→\nSENSOR\n<\ni\n>\nLOCATED\n\nAT\n\nAREA\n<\nname of area\n>\n.\n\n
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Facts like \n\nSENSOR\n\nNN\n\nAT\n\n17.00\n−\nNORMAL\n\n contain information about sensors, which gather and send to the fusion center the data about the state of the atmosphere in the surrounding area. Facts like \n\nSENSOR\n\nNN\n\nLOCATED\n\nAT\n\nAREA\n\nY\n…\nY\n\n contain information about sensors’ location.
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Consider APS knowledge base \n\nP\n=\n<\nS\n,\nD\n>\n\n, where MDB \n\nD\n\n was described higher, and \n\nS\n\n contains following S-productions:
\n\n\nσ\n5\n\n:\n<\nSENSOR\n\nFY\n\nLOCATED\n\nAT\n\nAREA GREEN VALLEY\n←\n,\n\n∅\n\n>\n.\n\n
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As seen, \n\n\nσ\n2\n\n−\n\nσ\n5\n\n\n are concrete S-productions, and set \n\n\nW\n\n0\n\n\n\n, corresponding to this APS KB, consists of ground facts, being headers of these CS-productions.
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Query, whose purpose is to get information about smoked areas, may be as follows:
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\n\n<\nAREA\n\nz\n\nIS SMOKED\n\nAT\n\nt\n,\n\n\nz\n→\n<\nname of area\n>\n\n\nt\n→\n<\ntime\n>\n\n\n>\n,\n\n
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while query, whose purpose is to get information about sensor \n\nFY\n\n location, may look like
\n\n\nSENSOR\n\nFY\n\nLOCATED\n\nAT\n\nAREA GREEN VALLEY\n}\n\n.\n\n∎\n.\n\n
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3. Operational semantics of APS
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The background of operational (O-) semantics of the considered query language is the so-called S-unification, which is generalization of well-known unification, introduced by J. Robinson in [7] and used in the resolution procedures, developed for the first-order predicate logic. (In fact, S-unification is a strict generalized definition of heuristically described unification, which, as it was shown in [1], is incorrect in the case of multiple occurrences of at least one variable to one of the unified terms.)
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We shall consider basic concept of S-unification and then describe operational semantics of used query language, corresponding to (11–18) and defining main features of the controlled logical inference of answers to queries to APS KB.
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Let \n\n<\ns\n,\nd\n>\n\n be a query and \n\n<\n\ns\n0\ni\n\n,\n\nd\ni\n\n>\n\n is couple, formed from S-production:
As may be seen easily, the answer to \n\n<\ns\n,\nd\n>\n\n may contain headers of the CS-productions, being concretizations of \n\n\nσ\ni\n\n\n, i.e., belonging to set \n\n\n\nσ\n¯\n\ni\n\n\n, if
(for correctness, sets of variables of terms \n\ns\n\n and \n\n\ns\no\ni\n\n\n would not intersect, that is, simply implemented by variables renaming, if this condition is not satisfied).
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Result of WECFL (21) solution is the new declaration of variables of S-production \n\n\nσ\ni\n\n\n, “concreted” in accordance with query \n\n<\ns\n,\nd\n>\n\n. This concretization will be denoted as \n\n\nd\ni\n\n\n\n\ns\no\ni\n\n\ns\n,\nd\n\n\n\n;\n\n it is obtained by replacing variable declarations \n\n\nβ\nj\ni\n\n\n of term\n\n\n\ns\n0\ni\n\n\n by new ones \n\n\n\nβ\n¯\n\nj\ni\n\n\n, which are more informative (“concrete,” “precise”), regarding initial declarations in \n\n\nd\ni\n\n.\n\n This concretization is adequate to query \n\n<\ns\n,\nd\n>\n\n in such a way, that CS-productions, whose headers may enter the answer to this query, may be created at the following steps of inference.
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Logics of S-unification are illustrated in Figure 1, followed by Example 2.
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Figure 1.
Graphical illustration of S-unification.
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Example 2. Consider query \n\n<\ns\n,\nd\n>\n\n, where
\n\n=\n{\na\n→\n<\nname of area\n>\n,\ns\n→\nNORMAL\n,\ne\n→\n<\ni\n>\n,\n\n
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\n\n\nt\n→\n<\ntime\n>\n}\n\n.\n\n∎\n\n
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S-unification provides opportunity of answer derivation by controlled logical inference, which is implemented by the axiomatic system, which contains only three inference rules—top-down successful S-resolution, top-down unsuccessful S-resolution, and bottom-up S-resolution [1, 2]. Here we shall describe more natural and understandable procedural representation of the considered O-semantics, which fully corresponds to the aforementioned axiomatics.
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This representation contains two recursively interconnected algorithmically defined functions.
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The first of them \n\nQ\n\n has two input variables \n\ns\n\n and \n\nd\n\n, which values represent query \n\n<\ns\n,\nd\n>\n\n, and one output variable A, which value, returned after \n\nQ\n\n termination, is the answer to this query, as defined by (17).
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The second function \n\nRB\n\n has also two input variables, \n\nφ\n\n and \n\nd\n\n, which values represent, respectively, the so-called residual body \n\nRB\n\n of S-production (subset of body of S-production, including terms, which until current call were not used for the derivation of new queries) and variable declaration, obtained after previous steps of logical inference. Function \n\nRB\n\n returns set \n\nD\n\nof variable declarations, each corresponding to one element of answer to the query \n\n<\ns\n,\nd\n>\n\n, where \n\ns\n∈\nφ\n\n is the next term, extracted from \n\nRB\n\n. By this, input variables declaration \n\nd\n\n is made “more precise.”
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Text of function \n\nQ\n\n is as follows:
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\n\n\n\n\n1\n\nQ\n:\nfunction\n\ns\nd\n\n\nreturns\n\n\nA\n\n;\n\n\n\n\n2\n\nvariables\n(\n\ns\n\ns\n0\n\n\n\nterm\n,\n\nd\n,\n\nd\n′\n\nδ\n\nδ\n′\n\n\n\ndeclaration\n,\nφ\n\nset\n\nof terms\n,\n\n\n\n\n3\n\n\n\nA\n\nset\n\nof words initial\n\n\n∅\n\n\n)\n\nlocal\n;\n\n\n\n\n4\n\nvariables\n\n\nS\n\nset\n\nof productions\n\n\nD\n\nset\n\nof context\n−\nfree rules\n\n\n\nglobal\n;\n\n\n\n\n5\n\ndo\n<\n\ns\n0\n\n,\nφ\n,\n\nd\n′\n\n>\n∈\nS\n;\n\n\n\n\n6\n\n\nδ\n≔\n\nd\n′\n\n\n\n\ns\n0\n\n\ns\n,\nd\n\n\n\n;\n\n\n\n\n7\n\n\nif\n\nδ\n≠\n∇\n\n\n\n\n8\n\n\nthen\n\ndo\n\n\nδ\n′\n\n∈\nRB\n\nφ\nδ\n\n;\n\n\n\n\n9\n\n\n\nA\n:\n∪\n\n\n\ns\n0\n\n\n\nδ\n′\n\n\n\n\n;\n\n\n\n\n10\n\n\n\nend\n\n\nδ\n′\n\n;\n\n\n\n\n11\n\nend\n\nS\n;\n\n\n\n\n12\n\nend\n\nQ\n\n\n\n\n
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As seen, lines 2–4 contain declarations of variables, used lower in the function body. It is essential that there are two variables declared as global, namely, S (set of S-productions) and D (metadatabase). Nevertheless the last one is not used in the body, it is presumed that MDB is used inside S-unification (line 6). The initial value of local variable \n\nA\n\n is an empty set.
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The main part of Q is loop (lines 5–11) on S-productions, entering set S and represented in the form of triples \n\n<\n\ns\n0\n\n,\nφ\n,\n\nd\n′\n\n>\n\n, where \n\n\ns\n0\n\n\n is header; \n\nφ\n\n is body of S-production, being set of terms; and \n\n\nd\n′\n\n\n is its variable declaration. The first operator inside loop (line 6) implements S-unification of query \n\n<\ns\n,\nd\n>\n\n and couple \n\n<\n\ns\n0\n\n,\n\nd\n′\n\n>\n\n. If this S-unification is successful (i.e., its result is not a special value \n\n∇\n\n), then loop on elements of set of variable declarations, obtained by function RB, is executed (lines 8–10). The values of input variables of function RB are \n\nφ\n\n (set of terms, entering body of the current S-production) and \n\nδ\n\n (result of the aforementioned S-unification). Each \n\n\nδ\n′\n\n\n, being set of variable declarations of the form \n\nγ\n→\nu\n\n, where \n\nu\n∈\n\nV\n∗\n\n\n, is used for making facts \n\n\ns\n0\n\n\n\nδ\n′\n\n\n\n by substitution right parts of those declarations to term \n\n\ns\n0\n\n\n, and all such facts are joined to the output set \n\nA\n\n (line 9).
Here lines 2–4, as in the text of Q, are declarations of variables, used in the RB body. RB execution begins by check, whether set \n\nφ\n\n is empty (line 5): if so, search of terms of S-production body is already finished (or, as partial case, it is empty because S-production is axiom), and the returned value is one-element set \n\n\nd\n\n\n, where \n\nd\n\n is the same, as input value. Otherwise loop on terms, entering residual body, is executed (lines 7–11). Every selected term \n\ns\n\n is used in query \n\n<\ns\n,\nd\n>\n\n to KB <S,D>, which is processed by recursive call Q (s,d), and loop on all facts, entering answer to this query, is executed (lines 8–10). The body of this loop contains single operator (line 9), providing accumulation of set \n\nD\n\n as a result of recursive call of the same function RB with the first input variable being \n\nφ\n−\n\ns\n\n\n(i.e., set of terms of the body of the processed S-production, reduced by extraction of the processed term \n\ns\n\n) and the second input variable value being \n\nd\n\n\ns\n\nw\n,\n\n∅\n\n\n\n\n\n (i.e., S-production variables declaration, made “more precise” according to result of S-unification of query \n\n<\ns\n,\nd\n>\n\n and fact \n\nw\n\n).
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As it is easy to see, execution of loop \n\ns\n∈\nφ\n\n provides generation of \n\nn\n!\n\n sequences of terms of body of S-production, i.e., all possible permutations of these terms.
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Let \n\nQ\n\ns\nd\n\n\n be finite result of function Q application to the query \n\n<\ns\n,\nd\n>\n\n and APS KB <S,D>. There is theorem on the correctness of the described operational semantics of the augmented Post systems [2].
In fact, Q and RB collaborative operation provides top-down derivation of the set of CS-productions, involved in creation of non-ground facts, which enter the answer to the query \n\n<\ns\n,\nd\n>\n\n, in full accordance with bottom-up definition of M-semantics of APS. (Ground facts are simply selected by one-step application of Q and RB.)
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As seen from this description, logical inference is implemented in a wavelike manner. “Direct waves,” including generated queries, which contain “more and more concrete” variables declarations, are “spreading” until S-axioms are reached. This leads to the creation of CS-productions, and “back waves” start until a new set of facts, being answer to some intermediate (or, finally, initial) query, is assembled. Both direct and back waves meet one another and “interfere,” producing new queries. As may be seen, APS operational semantics corresponds to the universal mode of inference (all facts, being goal of the query, are derived), unlike existential mode (anyone fact from the possibly multifact set), which is inherent to known resolution procedures. By this, full accordance of APS O-semantics to its S- and M-semantics is achieved.
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4. Procedural connection to APS KB
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One of the most important features of any knowledge representation model are embedded tools of the procedural connection, providing interaction of “rigid” (non-updated by knowledge engineers) software modules with inference process and data exchange between inference engine and the aforementioned modules.
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Background of the procedural connection to APS knowledge bases are program (P-) productions.
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P-production is couple \n\n<\n\ns\n0\n\n⇐\np\n,\nd\n>\n,\n\n where term \n\n\ns\n0\n\n\n is header and set \n\nd\n\n variable declaration; \n\n\ns\n0\n\n\n and\n\n\nd\n\n are declarative component of the procedural connection. Symbol \n\n"\n⇐\n"\n\n is the divider, distinct from \n\n"\n←\n"\n\n, and \n\np\n\n is the name of the connected program (software module), which has its own extensional \n\n\nW\np\n\n⊆\nEx\n\n\n<\n\n\n<\n\ns\n0\n\n←\n\n\nd\n>\n\n\n\n\nD\n>\n\n\n⊆\nL\n\nG\n\n\n.
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Couple \n\n<\n\ns\n0\n\n,\nd\n>\n\n is called cover of the connected program\n\np\n\n. In general case one and the same program may be connected to APS KB by several P-productions with different covers.
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Example 3. Program named MULT for multiplication of integer numbers may be connected to knowledge base by P-production:
MULT extensional is an infinite set containing strings like \n\n1\n∗\n1\n=\n1\n,\n0\n∗\n5\n=\n0\n,\n311\n∗\n−\n1\n=\n−\n311\n\n, etc.\n\n∎\n\n
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Let \n\nΠ\n\n be the set of names of programs, connected to APS KB. The extensional of this KB will differ from (12) to (14) by the only feature; instead of (12) the following is used:
i.e., \n\n\nW\n\n0\n\n\n\n is a join of extensionals of all connected programs and set of ground facts, being heads of CS-axioms. Until general case will be discussed, we shall consider programs with the so-called static extentionals, whose basic feature is \n\n\nW\np\n\n=\nconst\n\n for all period of query processing.
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Concerning operational semantics, it is sufficient to extend function Q by operators, providing call of the “perspective” connected programs via unified application programs interface and joining resulting sets to the accumulated answers. The extension is as follows:
As seen, after search on S-production set (lines 5–11), similar search on P-production set would be executed (lines 12–16). If S-unification of query \n\n<\ns\n,\nd\n>\n\n and program \n\np\n\n cover \n\n<\n\ns\n0\n\n,\n\nd\n′\n\n>\n\n is successful, program call is executed, and its result—set of words, denoted \n\np\n\n\ns\n0\n\nδ\n\n\n—is joined to the accumulated set \n\nA\n\n. This fully corresponds to (22).
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As seen, search on the set \n\nS\n\n is nondeterministic (there is no any predefined order on this set, except S-productions processing before P-productions; but in general case, this ordering, of course, is not mandatory). This obstacle creates some difficulties in implementation of calls of the connected programs, when input data are less informative, than it is necessary for computation (e.g., \n\nMULT\n\n\na\n∗\nb\n=\nc\n\n\n{\na\n→\n<\ninteger\n>\n\n\nb\n→\n4\n\n\nc\n→\n<\ninteger\n>\n}\n\n\n\n. By this, result of such call may be infinite set, even if there would be an opportunity to implement this operation using tools for N-facts processing, developed in [1, 2, 3, 4].
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To avoid such difficulty, it would be reasonable to implant to S- and P-productions some information, which may cut off “dangerous” branches.
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There is a simple tool for logical inference control within APS knowledge representation. Namely, variables, which the declarations after S-unification would have right parts, consisting only of terminal symbols (i.e., there would be no incomplete information, associated with non-terminals, inside these declarations), are marked by point over arrow in the initial descriptions, having place in P- and S-productions. Such variables are called complete. In example 3 declarations of variables \n\na\n\n and \n\nb\n\n would be \n\na\n\n→\ṅ\n\n<\ninteger\n>\n,\nb\n\n→\ṅ\n\n<\ninteger\n>\n\n, so query
while inference will result in program MULT call, while query
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\n\n\n<\nx\n∗\na\n=\ns\n,\n\n\nx\n→\n<\ninteger\n>\n\n\na\n→\n135\n\n\ns\n→\n<\ninteger\n>\n\n\n>\n\nwill not, because of information incompleteness of the declaration of \n\nx\n\n, which is a complete variable. Negative result of S-unification in the case of such incompleteness is, as higher, denoted by symbol ∇.
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Let us consider now the general case, where extensionals of programs, connected to APS KB, are not static, i.e., may vary while answer derivation. The simplest examples of such kind of programs are database management systems.
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To connect DBMS, operating key-addressed databases, it is sufficient to join to APS KB P-production:
for update operations (both variables \n\nk\n\n and \n\nd\n\n, corresponding to key and data, are complete).
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DBMS, operating databases with symmetric access, may be connected in a following way. In order to minimize redundant search while queries processing, some substrings of facts may be declared indexed (used for creation and maintenance of dynamic search trees, as it is described in [2, 3]). So some covers of the relational DBMS, corresponding to some subsets of DB, may contain complete variables, with declarations like \n\nγ\n\n→\ṅ\n\nu\n\n, where → u defines domain of the indexed attribute of the relation. Covers, providing inclusion to SDB, contain only complete variables. (If DBMS operate SDB with incomplete information, any variable may be incomplete.)
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5. Flow-processing APS
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Described approach to the procedural connection makes easy integration of the distributed hardware components, linked by the computer network, into a single system with the single operation process. This may be done by procedural connection of the corresponding hardware drivers and implementation of the so-called flow-processing augmented Post systems.
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FAPS KB includes along with S- and P-productions also the so-called flow (F-) productions having the form of
where terms \n\n\ns\n0\n\n\n, called activator, and \n\n\ns\n\nm\n+\n1\n\n\n\n, called actor, contain only complete variables and symbol “\n\n→\n\n” (inverse to “\n\n←\n\n”, used in S-production) divides activator and body, including, along with actor, conditions \n\n\ns\n1\n\n,\n…\n,\n\ns\nm\n\n\n. This structure is rather usual as well as operational semantics of F-productions, which is defined by function F, similar to Q and RB, and interconnected with them into integrated algorithmics.
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We assume that F-productions are represented in the knowledge base as couples \n\n<\n\ns\n0\n\n,\nφ\n,\ns\n,\nd\n>\n\n, where \n\n\ns\n0\n\n\n is activator, \n\nφ\n\n is set of terms-conditions, and \n\ns\n\n is actor, while \n\nd\n\n is variable declaration. By this, APS KB contains three types of objects, corresponding to three types of productions:
\n\n\n<\n\ns\n0\n\n,\nφ\n,\nd\n>\n\n, where\n\nφ\n\n is set of terms (S-productions);
\n\n\n<\n\ns\n0\n\n,\np\n,\nd\n>\n\n, where \n\np\n\n is string, being name of the connected program (P-productions);
As seen, the search on F-productions set (lines 4–10) provides selection of such F-productions, which are activated by input message \n\nw\n\n. The set of conditions of every activated F-production is interpreted as residual body of S-production (lines 7–9). Every variable declaration \n\n\nδ\n′\n\n\n, obtained as a result of this interpretation, is used for the creation of new message by substitution of \n\n\nδ\n′\n\n\n to actor \n\ns\n\n. This message is used as input value for function F recursive application. So the wave of messages, triggered by initial message, is generated, modeling well-known blackboard architecture. This wave propagation of any programs (including DBMS, software/hardware drivers, providing activation and operation of various devices, as well as networking middleware) may be applied. Operator terminate (line 11) stops F execution, so no return to the parent call is performed.
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Various practice-oriented dialects of APS, providing various effective technologies of non-procedural programming (multiactivating, triggers, terms with negators, decision tables, etc.), were developed; also described operational semantics of APS and its extensions were redefined for highly parallel hardware environment [1, 2]. Taking into account nondeterministic nature of operational semantics of APS family, some additional tools for logical inference control were introduced in [1, 2].
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Let us consider now the main features of APS application to the most advanced areas—Big Data, Internet of Things, cyberphysical industry, and cybersecurity—forming the future digital economy information infrastructure (DEII) [13, 14]. In all aforementioned applications, APS knowledge representation plays interconnecting and flexifying role, providing fast integration of various heterogeneous systems and fast adaptation of their operation logic to the highly volatile environment. In fact, APS simplify to the maximally possible level the most complicated problem of interoperability of any a priori developed systems; APS KB is nothing but “glue,” which in the simplest regular way integrates them together.
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6. Implementational issues and applications of APS
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DEII is the result of integration of three basic paradigms: network-centricity, Big Data, and Internet of Things (Figure 2).
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Figure 2.
Basic paradigms of digital economy information infrastructure.
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Network-centricity provides interconnection of any subjects of digital economy (DE), integrating human society and technosphere into global technosocium, operating as a global megasystem for the mankind wealth and prosperity. Big Data provides storage of great amounts of data from multiple heterogeneous sources and use of these data for rational everyday operation of the aforementioned megasystem and its permanent improvement. The Internet of Things, whose more precise and correct name would be Internet of Devices, provides creation, development, and operation of the aforementioned future global technosphere, including cyberphysical industry, based on additive manufacturing technologies, deeply robotized smart logistics, smart energy generation and delivery systems, and life resources infrastructure (food, water, living houses, etc.), as well as digital banking and finance infrastructure. All these segments, containing many billions of interconnected devices, provide implementation of such ambitious initiatives as Smart City and Smart Nation, inevitably leading to the Smart World as a well-understood and achievable goal of mankind evolution.
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The most complicated problem to be solved for DEII creation and development is providing its flexibility, i.e., rapid correction of operation logic of various DEII elements and sets of elements to the constantly occurring changes of the environment, as well as to changes in our knowledge about nature, human society, and technosphere. It is evident that sufficient flexibility of DEII may be achieved only on the background of knowledge engineering, leading to the knowledge-based digital economy.
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As shown in Figure 3, every subject of the DEII (no matter, human, or device), being associated with unique address of the address space of global information infrastructure, is supported by local knowledge base (LKB), applied by knowledge interpreter-corrector (KIC) for processing of input information flow, as well as local database, used and updated while aforementioned processing. Here LKB may contain not only “soft” component, i.e., rules, defining logic of input messages processing, but also “firm” component, i.e., “rigid” (nonmodified) software modules and systems (up to DBMS clients and servers), connected to LKB by the common interface, and called while rules interpretation (their sets are marked CP, e.g. Connected Programs, with lower indices).
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Figure 3.
Linearized representation of the knowledge-based digital economy information infrastructure.
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The described approach may be effectively implemented, if there would be some unified data item, providing unified representation of operation logic of any system, joining any set of DE subjects.
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The possible practical outcome of the SSF is the creation of toolkit, providing the described higher approach to DEII implementation upon string as the aforementioned unified data item. This outcome in the integrated form is placed in Figure 4, where APS family of string-operation knowledge representation models is explicated.
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Figure 4.
Integrated representation of APS family of knowledge representation models.
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The operational semantics of the simplest practically used model from this family, whose background is FAPS, is shown in Figure 5.
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Figure 5.
Basic operation semantics of multiactivated flow APS.
\n
As seen, input messages, which are transported to the local subject of DEII by means of network infrastructure, are recorded on the external blackboard (in fact, there are two blackboards used: external, for messages from the external sources, and internal, for messages generated while current external message processing). Every next message (string) \n\nw\n\n is navigated by special associative index (binary/ternary tree) to the activators \n\n\ns\no\ni\n\n\n. Selected activators are used for solving the corresponding WECFL (to simplify variable declarations and accelerate messages parsing, the so-called ultragrammars were introduced in [1, 2]), and if solutions do exist, they are transferred to the rest part of the F-production, in which the terms may provide access to databases with symmetric access (DBSA) or key-addressed databases (KADB) SQL-like or NoSQL queries and updates; check database integrity before updates (by applying constraints defined by the corresponding sets of productions); activate devices by their drivers, connected to LKB by proper P-productions; perform some nontrivial processing by connected “firm” software products; and also send messages, which are created strings to the internal blackboard or, by lower-level communication software, to another local subject (i.e., their external blackboards).
\n
As seen, described dialect of APS family provides flexible implementation of operations, necessary for Big Data and Internet of Things, as well as its cyberphysical manufacturing segment, i.e., industrial Internet of Things. In the simplest case, strings, being transferred to 3D printers by their driver’s calls, may be STL files containing layer descriptions of the printed material objects [15].
\n
The main feature, which is, in fact, core for all APS family, is “additivity” of the local knowledge bases. Namely, the occurrence of any new device or human, generating earlier unknown and unprocessed messages, is supported by addition to LKB of subjects (again devices or humans), communicating with source of such messages, new elements with activators, providing initiation of their processing. Such occurrence results, finally, in creation and sending new messages to another subjects. Such “additivity” may be called “vertical” (new types of messages are supported by the addition of new F-productions with new activators to LKB). However, horizontal “additivity” is also supported, when new F-productions with proper activators are added for internal messages, which are sent by some modules of LKB while processing external messages. By this, internal messages processing becomes “deeper.” As seen, FAPS provide sufficiently regular and refined technology of the distributed system software development and debugging.
\n
Let us underline that unlike well-known tools from the object programming area, which also support interconnection between modules by messages and use blackboard for such processing, in the case of flow APS, new message receivers are not known, and thus, there is no opportunity for its determination in the text of the program module. By this, APS-based knowledge engineering approach to software making principally differs from object programming as well as any other approaches from the more or less procedural programming.
\n
Let us pay some attention to such important area of flow APS application as cybersecurity, which is critical for DEII normal operation.
\n
Dynamically extended spectrum and complexity of cyberattacks, implementing today advanced persistent threats (APT), have led to the necessity of development of more and more sophisticated tools for early recognition and prevention of APT. The most efficient of such tools are based on the security information and event management (SIEM) paradigm [16, 17, 18, 19], whose background technologies are deep packets inspection/deep packets processing (DPI/DPP) [20, 21] and data leakage prevention (DLP) [22, 23]. The first operates flows of bit packets, providing their fusion in order to recognize elaborately covered signatures of known cyberattacks, while the second operates usually traffic from the application level of the OSI model. However, both operate strings (no matter, bit, symbol, or combined), and by this reason the described higher FAPS are the perfect tool for the SIEM implementation, especially at security operation centers (SOC), providing fusion of the primary data, passing to SOC from large amount of software and hardware traffic sensors from the security perimeter of the protected system. (Such sensors may be also effectively implemented on the FAPS background.) It is very important that SIEM is solidly based on three pillars: Big Data, which are stored fragments of the network traffic, caught at many network links in a 24 × 7 regime; data mining; and knowledge engineering. This area is the hardest known case of data mining and knowledge-based technologies application: cybersecurity knowledge engineers, by analyzing accumulated enormous volumes of primary data items, must quickly understand signatures of the prepared or already performed cyberattacks, developed by the smartest people from the global cybercrime and states special services teams; and rapidly correct LKBs or extend them by new sets of productions, providing early recognition and neutralizing such attacks. This combination of very large volumes of data and knowledge with hard real-time regime of SOC operation (e.g., SOC of the Russia largest financial group Sberbank neutralizes daily over 14,000 cyberattacks from all over the world [24]) makes cybersecurity application of data and knowledge engineering the most important from both practical and theoretical points of view.
\n
\n
\n
7. Conclusion
\n
The described “Set of Strings” Framework is based on integration of three research areas—knowledge/data engineering, theory of grammars, and information theory—which until now are very close but rather isolated from one another. Synergetic effect, which may be the main result of such integration, would be useful for the development of the unified theory, necessary for convergence of Big Data, data mining, artificial intelligence, and Internet of Things. Such convergence is extremely needed for solution of all spectrum of problems of digital economy.
\n
The most valuable directions of the SSF future development may be the following:
Metainference and machine learning in APS KB.
N-facts concretization in SDB with incomplete information (SDBI) and APS KB, using functional dependencies, associated usually with the relational data model.
Contradictory SDBI and APS KB management.
SDBI and APS KB with metadatabases, describing two- and three-dimensional objects.
SSF ideology and techniques transfer to the numeric data with the help of the SSF-associated theory of recursive multisets and multiset grammars/metagrammars, developed for the solution of problems of planning and scheduling in digital economy [25, 26, 27, 28].
Hardware implementation of key elements of SSF in the data flow and other high-parallel computer environments.
\n
The author is highly interested in the cooperation with computer scientists and engineers, who may spend some research effort participating in the development of the listed directions and the SSF at all.
\n
\n
Acknowledgments
\n
The author expresses gratitude to the editor for useful advices. The author is grateful to Prof. Noam Chomsky for words of support to the development of the “Set of Strings” Framework. The author is also thankful to Prof. Jeffrey Ullman for useful remarks on the current state of the theory of grammars.
\n
\n',keywords:"“Set of Strings” framework, augmented Post systems, string data bases, data and knowledge engineering, Big Data, Internet of Things, cybersecurity",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/67000.pdf",chapterXML:"https://mts.intechopen.com/source/xml/67000.xml",downloadPdfUrl:"/chapter/pdf-download/67000",previewPdfUrl:"/chapter/pdf-preview/67000",totalDownloads:906,totalViews:0,totalCrossrefCites:0,dateSubmitted:null,dateReviewed:"April 4th 2019",datePrePublished:"May 6th 2019",datePublished:"March 25th 2020",dateFinished:"May 6th 2019",readingETA:"0",abstract:"Augmented Post systems (APS) are string-operating Prolog-like knowledge representation, affiliated with the “Set of Strings” Framework (SSF). APS descriptive and logical inference capabilities provide natural integration of Big Data with online analytic processing. 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Introduction",level:"1"},{id:"sec_2",title:"2. Syntax and mathematical semantics of the augmented Post systems",level:"1"},{id:"sec_3",title:"3. Operational semantics of APS",level:"1"},{id:"sec_4",title:"4. Procedural connection to APS KB",level:"1"},{id:"sec_5",title:"5. Flow-processing APS",level:"1"},{id:"sec_6",title:"6. Implementational issues and applications of APS",level:"1"},{id:"sec_7",title:"7. Conclusion",level:"1"},{id:"sec_8",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Sheremet IA. Intelligent Software Environments for Information Processing Systems. Moscow: Nauka; 1994. p. 544 (In Russian)\n'},{id:"B2",body:'Sheremet IA. Augmented Post Systems: The Mathematical Framework for Data and Knowledge Engineering in Network-Centric Environment. Berlin: EANS; 2013. p. 395\n'},{id:"B3",body:'Sheremet I. Data and knowledge bases with incomplete information in a “Set of Strings” framework. International Journal of Engineering and Applied Sciences. 2016;3(3):90-103\n'},{id:"B4",body:'Sheremet IA. Augmented Post systems: String-operating knowledge representation for Big Data and Internet of Things applications. Geoinformatics Research Papers. 2017;5:BS1002. DOI: 10.2205/CODATA 2017\n'},{id:"B5",body:'Chomsky N. Syntactic Structures. 2nd ed. Berlin–New York: Mouton de Gruyter; 2002. p. 117\n'},{id:"B6",body:'Post EL. Formal reductions of the general combinatorial decision problem. American Journal of Mathematics. 1943;65:197-215\n'},{id:"B7",body:'Robinson JA. A machine-oriented logic based on the resolution principle. Journal of the ACM. 1965;12:23-41\n'},{id:"B8",body:'Kowalski RA. Algorithm = Logic + Control. Communications of the ACM. 1979;22(7):424-436\n'},{id:"B9",body:'Nilsson U, Maluszynski J. Logic, Programming and Prolog. 2nd ed. John Wiley & Sons; 2000. p. 282\n'},{id:"B10",body:'Cali A, Gottlob G, Lukasiewicz T. A general datalog-based framework for tructable query answering over ontologies. Journal of Web Semantics. 2012;14(1):57-83\n'},{id:"B11",body:'Miller D, Nadathur G. Programming with Higher-Order Logic. Cambridge University Press; 2012. p. 322\n'},{id:"B12",body:'Gallaire H, Minker J, Nicolas J-M. Logic and databases: A deductive approach. ACM Computing Surveys. 1984;16(2):153-185\n'},{id:"B13",body:'The Global Information Technology Report 2016. In: Baller S, Dutta S, Lanvin B, eds. Innovating in the Digital Economy. INSEAD, 2016. p. 62\n'},{id:"B14",body:'Lee J, Bagheri B, Hung-An. A cyber-physical systems architecture for industry 4.0: Based manufacturing systems. Manufacturing Letters. 2015;3:18-23. DOI: 10.1016/j.mfglet/2014.12.001\n'},{id:"B15",body:'Eyers DR, Potter AT. Industrial additive manufacturing: A manufacturing systems perspective. Computers in Industry. 2017;92-93:208-218. DOI: 10.1016/j.compind.2017.08.002\n'},{id:"B16",body:'Miller DR, Harris S, Harper A, Vandyke S. Security Information and Event Management (SIEM) Implementation. McGraw Hill; 2010. p. 464\n'},{id:"B17",body:'Miller DR, Harris S, Harper A, Vandyke S, Blask C. Security Information and Event Management (SIEM) Implementation. McGraw Hill; 2011. p. 465\n'},{id:"B18",body:'Sweeny J. Creating Your Own SIEM and Incident Response Toolkit Using Open Source Tools. SANS; 2011. p. 24. Available at: //sans.org/\n'},{id:"B19",body:'Limacher M, Kurz U. Security operation centre in action. CryptoMagazine. 2013;(3):10-12\n'},{id:"B20",body:'Fuchs C. Implications of Deep Packet Inspection (DPI) Internet Surveillance for Society. Research Paper. Uppsala University. 2012. p. 125\n'},{id:"B21",body:'Bass T. Intrusion detection systems and multisensor data fusion. Communications of the ACM. 2000;43(4):99-105\n'},{id:"B22",body:'Papadimitriou P, Garcia-Molina H. Data leakage detection. IEEE Transactions on Knowledge and Data Engineering. 2011;23:51-63\n'},{id:"B23",body:'Shabtai I, Elovici Y, Rokach L. A Survey of Data Leakage Detection and Prevention Solutions. NY: Springer-Verlag; 2012. p. 92\n'},{id:"B24",body:'Sheremet IA. Digital economy and cybersecurity of its financial sector. Proceedings of Free Economical Society. 2018;210:23-34 (In Russian)\n'},{id:"B25",body:'Sheremet IA. Recursive Multisets and Their Applications. Berlin: NG Verlag; 2011. p. 249\n'},{id:"B26",body:'Sheremet IA. Multiset approach to the estimation of consequences of natural disaster impacts on industrial systems. Geoinformatics Research Papers. Sochi 2016;4:BS4002, DOI: 10.2205/2016 BS01 .\n'},{id:"B27",body:'Sheremet IA. Multiset analysis of consequences of natural disasters impacts on large-scale industrial systems. Data Science Journal. 2018;17(4):1-17. DOI: 10.5334/dsj-2018-004\n'},{id:"B28",body:'Gvishiani AD, Roberts FS, Sheremet IA. On the assessment of sustainability of distributed sociotechnical systems to natural disasters. Russian Journal of Earth Sciences. 2018;18:ES4004. DOI: 10.2205/2018ES000627\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Igor Sheremet",address:"sheremet@rfbr.ru",affiliation:'
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RV evaluation involves quantifying afterload and preload, assessing the mechanism and severity of tricuspid regurgitation (TR), and quantitative evaluation of RV performance. For quantification of RV size and function, we can use intravenous contrast for endocardial tracing of RV border to measure RV dimensions, tricuspid annular plane systolic excursion (TAPSE), fractional area change (FAC), Doppler index of myocardial performance (Tei index or myocardial performance index), pulsed wave or color Doppler tissue imaging systolic velocity [s'], or strain imaging. For qualitative evaluation of RV, the RV size is compared to the LV size in parasternal, short axis, and subcostal projections.",book:{id:"5166",slug:"echocardiography-in-heart-failure-and-cardiac-electrophysiology",title:"Echocardiography in Heart Failure and Cardiac Electrophysiology",fullTitle:"Echocardiography in Heart Failure and Cardiac Electrophysiology"},signatures:"Gunjan Choudhary, Arushi A. 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Sorrell and Sachin Kumar",authors:[{id:"62264",title:"Dr.",name:"Vincent L.",middleName:null,surname:"Sorrell",slug:"vincent-l.-sorrell",fullName:"Vincent L. Sorrell"},{id:"62565",title:"Dr.",name:"Sachin",middleName:null,surname:"Kumar",slug:"sachin-kumar",fullName:"Sachin Kumar"}]},{id:"52583",title:"Role of Echocardiography in the Critically Ill Patients",slug:"role-of-echocardiography-in-the-critically-ill-patients",totalDownloads:2401,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Since its inception in 1950s, echocardiography has evolved significantly. Its role has expanded beyond cardiology into operating theaters, intensive care units, and emergency departments. It is an easy, inexpensive, noninvasive, and portable technique, which can be rapidly performed at bedside. It is devoid of complications and, for the most part, universally available. This review focuses on growing importance of echocardiography for critically ill patients in the intensive care and high dependency unit settings including indications, modalities, measurements, and therapeutic impact. Literature review of echocardiography use for the cardiovascular assessment of the critically ill patients was done and various indications are discussed including appropriate use scores. Methods being used include transthoracic and transesophageal echo with various modes. This does include assessment of volume status of the hemodynamically unstable patients, myocardial function, global left ventricular systolic function, regional wall motion abnormalities, cardiac output, cardiac tamponade, valvular function, left ventricular outflow obstruction, and right ventricular function. Other diagnostic assessments include aortic dissection, thromboembolisms, pleural effusions, and septal defects. Echocardiography is now considered as an indispensable tool for diagnosis and management including hemodynamic monitoring in critically ill patients. It provides advantages including noninvasiveness and real-time anatomical and functional assessment of the cardiovascular system.",book:{id:"5166",slug:"echocardiography-in-heart-failure-and-cardiac-electrophysiology",title:"Echocardiography in Heart Failure and Cardiac Electrophysiology",fullTitle:"Echocardiography in Heart Failure and Cardiac Electrophysiology"},signatures:"Manivannan Veerasamy",authors:[{id:"185477",title:"Dr.",name:"Manivannan",middleName:null,surname:"Veerasamy",slug:"manivannan-veerasamy",fullName:"Manivannan Veerasamy"}]},{id:"51873",title:"Noninvasive Measurement of Pulmonary Capillary Wedge Pressure by Speckle Tracking Echocardiography",slug:"noninvasive-measurement-of-pulmonary-capillary-wedge-pressure-by-speckle-tracking-echocardiography",totalDownloads:1616,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The severity of left-sided heart failure can be evaluated by pulmonary capillary wedge pressure (PCWP) because PCWP reflects left ventricular (LV) filling pressure. Owing to developments in echocardiographic technology, speckle tracking echocardiography (STE) has allowed automatic construction of time-left atrial (LA) volume (LAV) curves. Thus, we developed a novel index based on a combination of LAV and LA function that would estimate PCWP using STE. The following regression equation described the relationship between PCWP that was obtained by right-heart catheterization and active LAEF/minimum LAV index (volume was indexed to body surface area: LAVI) in the patients with sinus rhythm: PCWP = 10.8–12.4 [log10 (active LAEF/minimum LAVI)] (r = −0.86, p <0.001) (measurements from the apical 4-chamber view). We defined this index [log10 (active LAEF/minimum LAVI)] as the kinetics-tracking index (KT index). The PCWP estimated by the KT index (ePCWP) had a strong correlation with PCWP obtained by right-heart catheterization (r = 0.92, p <0.001). The ePCWP measured by STE could be a useful parameter to improve clinical outcomes in patients with heart failure.",book:{id:"5166",slug:"echocardiography-in-heart-failure-and-cardiac-electrophysiology",title:"Echocardiography in Heart Failure and Cardiac Electrophysiology",fullTitle:"Echocardiography in Heart Failure and Cardiac Electrophysiology"},signatures:"Masanori Kawasaki",authors:[{id:"50603",title:"Dr.",name:"Masanori",middleName:null,surname:"Kawasaki",slug:"masanori-kawasaki",fullName:"Masanori Kawasaki"}]},{id:"51894",title:"Multimodality Echocardiographic Assessment of Patients Undergoing Atrial Fibrillation Ablation",slug:"multimodality-echocardiographic-assessment-of-patients-undergoing-atrial-fibrillation-ablation",totalDownloads:1989,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"Atrial fibrillation (AF) is most common sustained arrhythmia in clinical practice. The new treatment standard in paroxysmal and persistent AF is the catheter ablation. Echocardiography plays a key role in risk stratification and management of patients with AF and is critical in the assessment of candidates for AF ablation, providing both anatomic and hemodynamic information. Echocardiography is crucial for patient selection, preprocedural left atrial appendage thrombus excluding, intraprocedural guidance, and detection and monitoring for early and late ablation related complications. Transthoracic echocardiography allows rapid and comprehensive assessment of cardiac anatomical structure and function. Transoesophageal echocardiography also provides accurate information about the presence of a thrombus in the atria and thromboembolic risk, making safe the ablation procedure by immediately detection of the complications related procedure. Intracardiac echocardiography has emerged as a popular and useful tool in the everyday practice of interventional electrophysiology, being very useful only during the ablation procedure. This paper presents the role of echocardiography in all these steps concerning AF ablation procedure, and also (1) delineates the role of echocardiographic techniques in guiding the procedure, (2) discusses the critical echocardiographic aspects of this procedure, and (3) underlines the strengths and limitations of various echocardiographic modalities.",book:{id:"5166",slug:"echocardiography-in-heart-failure-and-cardiac-electrophysiology",title:"Echocardiography in Heart Failure and Cardiac Electrophysiology",fullTitle:"Echocardiography in Heart Failure and Cardiac Electrophysiology"},signatures:"Mariana Floria and Maria Daniela Tanase",authors:[{id:"185089",title:"Dr.",name:"Tanase",middleName:null,surname:"Daniela Maria",slug:"tanase-daniela-maria",fullName:"Tanase Daniela Maria"},{id:"185115",title:"Dr.",name:"Mariana",middleName:null,surname:"Floria",slug:"mariana-floria",fullName:"Mariana Floria"}]}],onlineFirstChaptersFilter:{topicId:"982",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},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:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:320,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,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:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:17,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,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:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:null,scope:"
\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
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\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
",coverUrl:"https://cdn.intechopen.com/series/covers/24.jpg",latestPublicationDate:"July 6th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:0,editor:{id:"262440",title:"Prof.",name:"Usha",middleName:null,surname:"Iyer-Raniga",slug:"usha-iyer-raniga",fullName:"Usha Iyer-Raniga",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRYSXQA4/Profile_Picture_2022-02-28T13:55:36.jpeg",biography:"Usha Iyer-Raniga is a professor in the School of Property and Construction Management at RMIT University. Usha co-leads the One Planet Network’s Sustainable Buildings and Construction Programme (SBC), a United Nations 10 Year Framework of Programmes on Sustainable Consumption and Production (UN 10FYP SCP) aligned with Sustainable Development Goal 12. The work also directly impacts SDG 11 on Sustainable Cities and Communities. She completed her undergraduate degree as an architect before obtaining her Masters degree from Canada and her Doctorate in Australia. Usha has been a keynote speaker as well as an invited speaker at national and international conferences, seminars and workshops. Her teaching experience includes teaching in Asian countries. She has advised Austrade, APEC, national, state and local governments. She serves as a reviewer and a member of the scientific committee for national and international refereed journals and refereed conferences. She is on the editorial board for refereed journals and has worked on Special Issues. Usha has served and continues to serve on the Boards of several not-for-profit organisations and she has also served as panel judge for a number of awards including the Premiers Sustainability Award in Victoria and the International Green Gown Awards. Usha has published over 100 publications, including research and consulting reports. Her publications cover a wide range of scientific and technical research publications that include edited books, book chapters, refereed journals, refereed conference papers and reports for local, state and federal government clients. She has also produced podcasts for various organisations and participated in media interviews. She has received state, national and international funding worth over USD $25 million. Usha has been awarded the Quarterly Franklin Membership by London Journals Press (UK). Her biography has been included in the Marquis Who's Who in the World® 2018, 2016 (33rd Edition), along with approximately 55,000 of the most accomplished men and women from around the world, including luminaries as U.N. Secretary-General Ban Ki-moon. In 2017, Usha was awarded the Marquis Who’s Who Lifetime Achiever Award.",institutionString:null,institution:{name:"RMIT University",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,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. Vouros",slug:"deep-multiagent-reinforcement-learning-methods-addressing-the-scalability-challenge",totalDownloads:1,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Multi-Agent Technologies and Machine Learning",coverURL:"https://cdn.intechopen.com/books/images_new/11445.jpg",subseries:{id:"27",title:"Multi-Agent Systems"}}},{id:"82196",title:"Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature",doi:"10.5772/intechopen.104944",signatures:"Kamarajugadda Kishore Kumar and Movva Pavani",slug:"multi-features-assisted-age-invariant-face-recognition-and-retrieval-using-cnn-with-scale-invariant-",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"82063",title:"Evaluating Similarities and Differences between Machine Learning and Traditional Statistical Modeling in Healthcare Analytics",doi:"10.5772/intechopen.105116",signatures:"Michele Bennett, Ewa J. 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