Open access peer-reviewed Edited Volume

Web Development for User Interface, Data Visualization, and Visual Analytics

Tommy Dang

Texas Tech University

His research on data visualization and visual analytics has appeared in Computer Graphics Forum and IEEE Transactions on Visualization and Computer Graphics and has been presented at IEEE Information Visualization, IEEE Visual Analytics Science and Technology, EG/VGTC Conference.

Co-editor:

Vung Pham

Sam Houston State University

Dr. Pham is an Assistant Professor at the Computer Science Department, Sam Houston State University, USA. His areas of expertise centered around data visualizations, data analytics, machine learning, and deep learning. He has published one patent, various peer-reviewed journals, and conference papers.

Covering

Multivariate Time Series Dimensional Reduction Small Multiples Visual Reasoning User Interaction Data Aggregation Data Characterization D3 ThreeJS NodeJS AFrame JavaScript Packages Web Browsers

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About the book

User Interface, Data Visualization, and Visual Analytics study the organization, presentation, and interactions between human and graphical components on display. While user interface and data visualization are concerned with efficiently representing graphical widgets and charts, Visual Analytics focuses on understanding the visual reasoning process and then extracting and characterizing the input data to fit users' interests. User Interface, Data Visualization, and Visual Analytics are challenging domains within data science as users are diverse in cultural background, technical skill, cognitive load, age, gender, etc. At the same, each research/application domain may pose different needs and requirements. Even for the same users in a specific domain, their interests, needs, and interactions may change over time. While the web UI has been popular, recent developments of JavaScript packages/libraries (such as D3 and ThreeJS) brought advanced visualizations onto smart devices via web browsers.

The limited cognitive load of humans and finite rendering resources and display real estate are the major obstacles to developing interactive visualization systems for analyzing big data. Recent technological innovation has significantly improved computing power (faster CPUs, GPUs,...) and the display screen (ultra-high-resolution displays and video walls). However, large and complex data is still ahead in the run as we are generating mind-boggling amounts of data on a daily basis. The aim of visualization is to map the massive and complex data (using the existing resource) onto the limited human cognitive load. This book aims to make the step in effectively connecting the three components: Big Data - Visual Interface – Users and making them more accessible through the web environment.

Publishing process

Book initiated and editor appointed

Date completed: April 20th 2022

Applications to edit the book are assessed and a suitable editor is selected, at which point the process begins.

Chapter proposals submitted and reviewed

Deadline for chapter proposals: May 18th 2022

Potential authors submit chapter proposals ready for review by the academic editor and our publishing review team.

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Approved chapters written in full and submitted

Deadline for full chapters: July 17th 2022

Once approved by the academic editor and publishing review team, chapters are written and submitted according to pre-agreed parameters

Full chapters peer reviewed

Review results due: October 5th 2022

Full chapter manuscripts are screened for plagiarism and undergo a Main Editor Peer Review. Results are sent to authors within 30 days of submission, with suggestions for rounds of revisions.

Book compiled, published and promoted

Expected publication date: December 4th 2022

All chapters are copy-checked and typesetted before being published. IntechOpen regularly submits its books to major databases for evaluation and coverage, including the Clarivate Analytics Book Citation Index in the Web of ScienceTM Core Collection. Other discipline-specific databases are also targeted, such as Web of Science's BIOSIS Previews.

About the editor

Tommy Dang

Texas Tech University

Tommy Dang is an Assistant Professor of Computer Science at Texas Tech University where he directs the interactive Data Visualization Lab (iDVL). The mainstream of his research is on visual features (or Scagnostics) for analyzing the pairwise correlation of multivariate data. Working directly with these measures, his research was able to locate anomalous or interesting subsets of variables/sub-series for massive, dynamic, and high dimensional data in scientific and social applications. He also has special interests and skills in 3D modeling, computer animation, and virtual reality. Dr. Dang has previously been a post-doc on a DARPA-funded project on biological network visualization at the Electronic Visualization Lab at the University of Illinois at Chicago which focuses on advanced virtual reality, notably the CAVE2™ hybrid reality environment and the SAGE2™ scalable amplified group environment.

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