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003			ES-MaUEC
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008			231103s2024 sz | fo |||| 0|eng d
020			_a9783031409561
024	7		_a10.1007/978-3-031-40956-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aHF5548.2 _b2024 EB
245	0	0	_aDevelopment Methodologies for Big Data Analytics Systems : _bPlan-driven, Agile, Hybrid, Lightweight Approaches _cedited by Manuel Mora, Fen Wang, Jorge Marx Gomez, Hector Duran-Limon
250			_a1st ed. 2024
264		1	_aCham _bSpringer International Publishing _c2024
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aTransactions on Computational Science and Computational Intelligence _x2569-7080
505	0		_aIntroduction -- 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.
520			_aThis 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.
988			_aSpringer_Engineering_2024
650		7	_2embne _9141658 _aEmpresas _xProceso de datos
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-40956-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2024 _dz _eb _zSI