Richard S. Segall

Richard S. Segall

Dr. Richard S. Segall is a Professor of Computer & Information Technology in the College of Business at Arkansas State University in Jonesboro, AR and also teaches in the Master of Engineering Management (MEM) Program in the College of College of Agriculture, Engineering & Technology. He is also Affiliated Faculty at the University of Arkansas at Little Rock (UALR) where he serves on thesis committees. He holds a Bachelor of Science and Master of Science in Mathematics as well as a Master of Science in Operations Research and Statistics from Rensselaer Polytechnic Institute in Troy, New York. He also holds a PhD in Operations Research form University of Massachusetts at Amherst, He has served on the faculty of Texas Tech University, University of Louisville, University of New Hampshire, University of Massachusetts-Lowell, and West Virginia University. His research interests include data mining, text mining, web mining, database management, Big Data, and mathematical modeling.

Dr. Segall‘s publications have appeared in numerous journals including International Journal of Information Technology and Decision Making (IJITDM), International Journal of Information and Decision Sciences (IJIDS), Applied Mathematical Modelling (AMM), Kybernetes: The International Journal of Cybernetics, Systems and Management Sciences, Journal of the Operational Research Society (JORS) and Journal of Systemics, Cybernetics and Informatics (JSCI). He has published book chapters in Encyclopedia of Data Warehousing and Mining, Handbook of Computational Intelligence in Manufacturing and Production Management, Handbook of Research on Text and Web Mining Technologies, Encyclopedia of Information Science & Technology, and Encyclopedia of Business Analytics & Optimization.

Dr. Segall is a member of the Arkansas Center for Plant-Powered-Production (P3), and on the Editorial Board of the International Journal of Data Mining, Modelling and Management (IJDMMM) and International Journal of Data Science (IJDS), and served as Local Arrangements Chair of the MidSouth Computational Biology & Bioinformatics Society (MCBIOS) Conference that was hosted at Arkansas State University.

His research has been funded by National Research Council (NRC), U.S. Air Force (USAF), National Aeronautical and Space Administration (NASA), Arkansas Biosciences Institute (ABI), and Arkansas Science & Technology Authority (ASTA). He is recipient of several Session Best Paper awards at World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI) conferences. He is co-editor of two other books published by IGI Global: Visual Analytics and Interactive Technologies: Data, Text and Web Mining Applications in 2011 and Research and Applications in Global Supercomputing in 2015. Dr. Segall is recipient of Arkansas State University, College of Business Faculty Award for Excellence in Research in 2015.

Publications

Data Linkage Discovery Applications
Richard S. Segall, Shen Lu. © 2019. 13 pages.
This chapter discusses the topic of linkage discovery for data and their applications. This chapter enhances a previous study by the authors and includes additional references...
Overview of Big Data-Intensive Storage and its Technologies for Cloud and Fog Computing
Richard S. Segall, Jeffrey S Cook, Gao Niu. © 2019. 40 pages.
Computing systems are becoming increasingly data-intensive because of the explosion of data and the needs for processing the data, and subsequently storage management is critical...
Handbook of Research on Big Data Storage and Visualization Techniques
Richard S. Segall, Jeffrey S. Cook. © 2018. 917 pages.
The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across...
Data Linkage Discovery Applications
Richard S. Segall, Shen Lu. © 2018. 11 pages.
Overview of Big Data and Its Visualization
Richard S. Segall, Gao Niu. © 2018. 32 pages.
Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This chapter discusses what Big...
Overview of Big-Data-Intensive Storage and Its Technologies
Richard S. Segall, Jeffrey S. Cook. © 2018. 42 pages.
This chapter deals with a detailed discussion on the storage systems for data-intensive computing using Big Data. The chapter begins with a brief introduction about...
Big Data and Its Visualization With Fog Computing
Richard S. Segall, Gao Niu. © 2018. 32 pages.
Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This article discusses what is...
Information Retrieval by Linkage Discovery
Richard S. Segall, Shen Lu. © 2015. 8 pages.
Research and Applications in Global Supercomputing
Richard S. Segall, Jeffrey S. Cook, Qingyu Zhang. © 2015. 672 pages.
Rapidly generating and processing large amounts of data, supercomputers are currently at the leading edge of computing technologies. Supercomputers are employed in many different...
Overview of Global Supercomputing
Richard S. Segall, Neha Gupta. © 2015. 32 pages.
In this chapter, a discussion is presented of what a supercomputer really is, as well as of both the top few of the world's fastest supercomputers and the overall top 500 in...
Linkage Discovery with Glossaries
Richard S. Segall, Shen Lu. © 2014. 11 pages.
Visual Analytics and Interactive Technologies: Data, Text and Web Mining Applications
Qingyu Zhang, Richard S. Segall, Mei Cao. © 2011. 362 pages.
Large volumes of data and complex problems inspire research in computing and data, text, and Web mining. However, analyzing data is not sufficient, as it has to be presented...
Comparing Four-Selected Data Mining Software
Richard S. Segall. © 2009. 9 pages.
This chapter discusses four-selected software for data mining that are not available as free open-source software. The four-selected software for data mining are SAS® Enterprise...
A Survey of Selected Software Technologies for Text Mining
Richard S. Segall. © 2009. 19 pages.
This chapter presents background on text mining, and comparisons and summaries of seven selected software for text mining. The text mining software selected for discussion and...
A Survey of Selected Software Technologies for Text Mining
Richard S. Segall, Qingyu Zhang. © 2009. 18 pages.
This chapter presents background on text mining, and comparisons and summaries of seven selected software for text mining. The text mining software selected for discussion and...
Comparing Four-Selected Data Mining Software
Richard S. Segall, Qingyu Zhang. © 2009. 10 pages.
This chapter discusses four-selected software for data mining that are not available as free opensource software. The four-selected software for data mining are SAS® Enterprise...
Using Data Mining for Forecasting Data Management Needs
Qingyu Zhang, Richard S. Segall. © 2008. 17 pages.
This chapter illustrates the use of data mining as a computational intelligence methodology for forecasting data management needs. Specifically, this chapter discusses the use of...
Using Data Mining for Forecasting Data Management Needs
Qingyu Zhang, Richard S. Segall. © 2008. 18 pages.
This chapter illustrates the use of data mining as a computational intelligence methodology for forecasting data management needs. Specifically, this chapter discusses the use of...
Microarray Databases for Biotechnology
Richard S. Segall. © 2005. 6 pages.
Microarray informatics is a rapidly expanding discipline in which large amounts of multi-dimensional data are compressed into small storage units. Data mining of microarrays can...