Richard S. Segall

Richard S. SegallRichard S. Segall is associate professor of computer & information technology at Arkansas State University. He holds BS and MS in mathematics, MS in operations research and statistics from Rensselaer Polytechnic Institute, and PhD in operations research form University of Massachusetts at Amherst. His publications have appeared in journals including International Journal of Information Technology and Decision Making, Applied Mathematical Modeling, Kybernetes: International Journal of Systems and Cybernetics, and Journal of the Operational Research Society. He has book chapters in Encyclopedia of Data Warehousing and Mining, Handbook of Computational Intelligence in Manufacturing and Production Management, and Handbook of Research on Text and Web Mining Technologies. His research interests include data mining, text mining, web mining, database management, and mathematical modeling, and have been funded by U.S. Air Force, NASA, Arkansas Biosciences Institute, and Arkansas Science & Technology Authority. He is a member of the Editorial Board of the International Journal of Data Mining, Modeling and Management, the Program Committees of the 13th and 14th World Multi-Conference on Systemics, Cybernetics and Informatics, and Local Arrangements Chair of the 2010 MidSouth Computational Biology & Bioinformatics Society Conference.

Publications

Data Streaming Processing Window Joined With Graphics Processing Units (GPUs)
Shen Lu, Richard S. Segall. © 2021. 22 pages.
Big data is large-scale data and can be either discrete or continuous. This article entails research that discusses the continuous case of big data often called “data streaming.”...
Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities
Richard S. Segall, Gao Niu. © 2020. 237 pages.
With the development of computing technologies in today’s modernized world, software packages have become easily accessible. Open source software, specifically, is a popular...
What Is Open Source Software (OSS) and What Is Big Data?
Richard S. Segall. © 2020. 49 pages.
This chapter discusses what Open Source Software is and its relationship to Big Data and how it differs from other types of software and its software development cycle. Open...
Open Source Software (OSS) for Big Data
Richard S. Segall. © 2020. 23 pages.
This chapter discusses Open Source Software and associated technologies for the processing of Big Data. This includes discussions of Hadoop-related projects, the current top open...
Big Data and Its Visualization With Fog Computing
Richard S. Segall, Gao Niu. © 2020. 37 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...
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...