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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.
Inhaltsverzeichnis
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.
