04804nam a22006015i 4500 978-3-031-40956-1 DE-He213 20251030110829.0 cr nn 008mamaa 231103s2024 sz | s |||| 0|eng d 9783031409561 978-3-031-40956-1 10.1007/978-3-031-40956-1 doi TK5101-5105.9 TJK bicssc TEC041000 bisacsh TJK thema Development Methodologies for Big Data Analytics Systems [electronic resource] : Plan-driven, Agile, Hybrid, Lightweight Approaches / edited by Manuel Mora, Fen Wang, Jorge Marx Gomez, Hector Duran-Limon. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XVI, 280 p. 68 illus., 22 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Transactions on Computational Science and Computational Intelligence, 2569-7080 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. 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. Telecommunication. Data mining. 6941 Big data. 57216 Quantitative research. Communications Engineering, Networks. Data Mining and Knowledge Discovery. Big Data. 57216 Data Analysis and Big Data. Mora, Manuel. editor. edt http://id.loc.gov/vocabulary/relators/edt 66988 Wang, Fen. editor. edt http://id.loc.gov/vocabulary/relators/edt 66989 Marx Gomez, Jorge. editor. edt http://id.loc.gov/vocabulary/relators/edt 66990 Duran-Limon, Hector. editor. edt http://id.loc.gov/vocabulary/relators/edt 66991 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031409554 Printed edition: 9783031409578 Printed edition: 9783031409585 Transactions on Computational Science and Computational Intelligence, 2569-7080 66992 https://doi.org/10.1007/978-3-031-40956-1 ZDB-2-ENG ZDB-2-SXE 56497 56497