Dr. Richard S. Segall is Professor of Computer & Information Technology at Arkansas State University in Jonesboro, AR where he also teaches in the College of Engineering Master of Engineering Management (MEM) Program. 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 publications have appeared in journals including International Journal of Information Technology and Decision Making (IJITDM), International Journal of Information and Decision Sciences (IJIDS), Applied Mathematical Modelling (AMM), Kybernetes: International Journal of Cybernetics, Systems and Management Science, Journal of the Operational Research Society (JORS) and Journal of Systemics, Cybernetics and Informatics (JSCI).
He has 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.
He has edited 2 published books: Visual Analytics and Interactive Technologies: Data, Text and Web Mining Applications published by IGI Global in 2011, Research and Applications in Global Supercomputing published by IGI Global in 2015, and is currently editing a 3rd book titled Big Data Storage and Visualization Techniques to be published by IGI Global in 2017.
He 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 2010 MidSouth Computational Biology & Bioinformatics Society (MCBIOS) Conference.
His research interests include data mining, text mining, web mining, database management, Big Data, and mathematical modeling. 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 Session Best Paper awards at the 2008, 2009, 2010, 2011 and 2013 World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI) conferences, and Arkansas State University, College of Business Faculty Award for Excellence
Big Data is an evolving term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information.
Although Big Data doesn't refer to any specific quantity, the term is often used when speaking about Petabytes and Exabytes of data. Measured in terms of volume, velocity, and variety, Big Data represents a major disruption in the business intelligence and data management landscape, upending fundamental notions about governance and Information Technology (IT) delivery.
With traditional solutions becoming too expensive to scale or adapt to rapidly evolving conditions, companies are scrambling to find affordable technologies that will help them store, process, and query all of their data. Innovative solutions will enable companies to extract maximum value from Big Data and create differentiated, more personal customer experiences.
Big Data also is all data that is not fit for the traditional highly structured Relational Database Management Systems (RDBMS) whether used for Online Transactional Processing (OLTP) or analytics purposes. Hence utilizing Big Data calls for the needs for new technology to handle and analyze. Clusters in Big Data contains data organized into key spaces that can contain multiple “column families” that can be considered analogous to tables but can have any number of columns or be completely dynamic columns that change with the time horizon.
According to a report on Big Data by the McKinsey Global Institute (2011): Big Data if used creatively and effectively to drive efficiency and quality, the health care sector can create more than $300 billion in value each year, a retailer using Big Data to the full could increase its operating margin by more than 60 percent, and users of services enabled by personal-location data could capture $600 billion in consumer surplus.
The field of Big Data involves expanded technologies & architectural patterns, using Big Data in clouds, clusters and grids; programming systems for Big Data; storage, visualization and analytics for Big Data; and study of state of practice of using Big Data.
The numerous applications of Big Data range from such areas as manufacturing, agriculture, air traffic control, to computational energy, medicine, earth and atmospheric sciences.
The objective of this talk is to present the concepts of Big Data, explore its analytics and technologies and their applications and develop a broad understanding of issues pertaining to the use of Big Data in multidisciplinary fields.
