Development Methodologies for Big Data Analytics Systems (eBook, PDF)

Plan-driven, Agile, Hybrid, Lightweight Approaches
Redaktion: Mora, Manuel; Duran-Limon, Hector; Marx Gomez, Jorge; Wang, Fen

• Format: PDF

Produktbeschreibung

This book presents research in big data analytics (BDA) for business of all sizes. The authors analyze problems presented in the application of BDA in some businesses through the study of development methodologies based on the three approaches - 1) plan-driven, 2) agile and 3) hybrid lightweight. The authors first describe BDA systems and how they emerged with the convergence of Statistics, Computer Science, and Business Intelligent Analytics with the practical aim to provide concepts, models, methods and tools required for exploiting the wide variety, volume, and velocity of available business internal and external data - i.e. Big Data - and provide decision-making value to decision-makers. The book presents high-quality conceptual and empirical research-oriented chapters on plan-driven, agile, and hybrid lightweight development methodologies and relevant supporting topics for BDA systems suitable to be used for large-, medium-, and small-sized business organizations.

• Addresses the mathematical, statistical and computational foundations and techniques of Big Data Analytics;
• Includes specific research problems in the development methodologies from a Systems and Software perspective;
• Presents successful BDA systems applied in diverse domains such as Healthcare, Logistics, Finance, Marketing, Retail, and Education.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 280
• Erscheinungstermin: 3. November 2023
• Englisch
• ISBN-13: 9783031409561
• Artikelnr.: 69301840

Autorenporträt

Manuel Mora is a full-time Professor in the Information Systems Department at the Autonomous University of Aguascalientes (UAA), Mexico. Dr. Mora holds an M.Sc. in Computer Sciences (Artificial Intelligence area, 1989) from Monterrey Tech (ITESM), and an Eng.D. in Engineering (Systems Engineering area, 2003) from the National Autonomous University of Mexico (UNAM). He has published over 90 research papers in international top conferences, research books, and journals such as IEEE-TSMC, European Journal of Operational Research, Int. Journal of Information Management, Engineering Management, Int. J. of Information Technology and Decision Making, Information Technology for Development, Int. J. in Software Engineering and Knowledge Engineering, and Computer Standards & Interface. Dr. Mora is a senior member of ACM (since 2008), an SNI at Level II, and serves in the ERB of several international journals indexed by Emergent Source Citation Index focused on decision-making support systems(DMSS) and IT services systems. Fen Wang is a Full Professor in the Information Technology & Administrative Management Department at Central Washington University (CWU). Before joining CWU, Prof. Wang was an Assistant Professor and Director of the Management Information Systems (MIS) program at the Eastern Nazarene College in Massachusetts. Prof. Wang holds a B.S. in MIS, an M.S., and a Ph.D. in Information Systems from the University of Maryland Baltimore County. Prof. Wang has brought over ten years of professional and research experience in information technology management to her students. Her research focuses on intelligent decision support technologies and E-business strategies. These efforts have resulted in contributions to the applied literature on information technologies that have been well-received in the research community. Prof. Wang has published over thirty papers in internationally-circulated journals and book series, including the International Journal ofE-Business Research (IJEBR), International Journal of Decision Support System Technology (IJDSST), Intelligent Decision Technologies (IDT), Information Technology for Development (ITFD), and the Encyclopedia of E-Commerce, E-Government and Mobile Commerce.  Prof. Wang has also consulted for a variety of public and private organizations on IT management and applications.  Prof. Dr. Jorge Marx Gómez studied Computer Engineering and Industrial Engineering at the University of Applied Sciences Berlin (Technische Fachhochschule Berlin). He was a lecturer and researcher at the Otto-von-Guericke-Universität Magdeburg (Germany) where he also obtained a Ph.D. degree in Business Information Systems with the work Computer-based Approaches to Forecast Returns of Scrapped Products to Recycling. From 2002 till 2003 he was a visiting professor for Business Informatics at the Technical University of Clausthal (TU Clausthal, Germany). In 2004 he received his habilitation for the workAutomated Environmental Reporting through Material Flow Networks at the Otto-von-Guericke-Universität Magdeburg. In 2005 he became a full professor and chair of Business Information Systems at the Carl von Ossietzky University Oldenburg (Germany). His research interests include Very Large Business Applications, Business Information Systems, Federated ERP-Systems, Business Intelligence, Data Warehousing, Interoperability, and Environmental Management Information Systems.   Hector A. Duran-Limon Ph.D., is currently a full Professor at the Information Systems Department, University of Guadalajara, Mexico. He completed a Ph.D. at Lancaster University, England in 2002. Following this, he was a post-doctoral researcher until December 2003. He obtained an IBM Faculty award in 2008. His research interests include Cloud Computing and High-Performance Computing (HPC). He is also interested in Software Architecture, Software Product Lines, and Component-based Development. In 2006, He was invited to create a Ph.D. program in Information Technologies for the University of Guadalajara, becoming a member of the Academic Council.

Inhaltsangabe

Introduction.- Section I - Foundations on Big Data Analytics Systems.- Big Data Analytics foundations.- Big Data Science foundations.- Big Data Analytics Systems Frameworks.- Big Data Analytics Systems Architectures.- Big Data Analytics Tools and Platforms.- Big Data Analytics Computational Techniques.- Section II - Plan-Driven Development Methodologies for Big Data Analytics Systems.- CRISP-DM.- SEMMA.- KDD.- Section III - Emergent Agile and Hybrid Lightweight Development.- Methodologies for Big Data Analytics Systems.- Scrum.- ISO/IEC 29110.- Microsoft TDSP.- Section IV - Cases Studies of Big Data Analytics Systems Projects.- Applications in Healthcare.- Applications in Marketing.- Applications in Financial.- Applications in Education.- Applications in Sports.- Section V - Challenges and Future Directions on Big Data Analytics Systems Projects.- Review of challenges.- Current problems and limitations.- Future directions.- Conclusion.