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Development methodologies for big data analytics systems = plan-driven, agile, hybrid, lightweight approaches /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Development methodologies for big data analytics systems/ edited by Manuel Mora ... [et al.].
Reminder of title:	plan-driven, agile, hybrid, lightweight approaches /
other author:	Mora, Manuel.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xvi, 280 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Business enterprises - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-40956-1
ISBN:	9783031409561

Development methodologies for big data analytics systems = plan-driven, agile, hybrid, lightweight approaches /
Development methodologies for big data analytics systemsplan-driven, agile, hybrid, lightweight approaches /[electronic resource] :edited by Manuel Mora ... [et al.]. - Cham :Springer International Publishing :2024. - xvi, 280 p. :ill., digital ;24 cm. - Transactions on computational science and computational intelligence,2569-7080. - Transactions on computational science and computational intelligence..

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.

ISBN: 9783031409561

Standard No.: 10.1007/978-3-031-40956-1doiSubjects--Topical Terms:

750112
Business enterprises
--Data processing.

LC Class. No.: HF5548.2

Dewey Class. No.: 658.05

Development methodologies for big data analytics systems = plan-driven, agile, hybrid, lightweight approaches /
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505 0 $a 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.
520 $a 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.
650 0 $a Business enterprises $x Data processing. $3 750112
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700 1 $a Mora, Manuel. $3 2055912
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