Engineering Agile Big-Data Systems

Kevin Feeney, Jim Davies, James Welch

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English

River Publishers
21 October 2024

Software Engineering; Data mining; Computer science

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.

Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems.

By: Kevin Feeney, Jim Davies, James Welch
Imprint: River Publishers
Country of Publication: Denmark
Dimensions: Height: 234mm, Width: 156mm,
Weight: 802g
ISBN: 9788770043816
ISBN 10: 8770043817
Pages: 434
Publication Date: 21 October 2024
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Preface Acknowledgements List of Contributors List of Figures List of Tables List of Abbreviations 1 Introduction 2 ALIGNED Use Cases – Data and Software Engineering Challenges 3 Methodology 4 ALIGNED MetaModel Overview 5 Tools 6 Use Cases 7 Evaluation Appendix A – Requirements Index About the Editors

Kevin Feeney, Jim Davies, James Welch