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River Publishers Series in Information Science and Technology Series

Big Data

Concepts, Warehousing, and Analytics

Hardback
June 2020
9788770221849
More details
  • Publisher
    River Publishers
  • ISBN 9788770221849
  • Language English
  • Pages 312 pp.
  • Size 6" x 9"
$115.00
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May 2020
9788770221832
More details
  • Publisher
    River Publishers
  • Published
    1st May
  • ISBN 9788770221832
  • Language English
  • Pages 312 pp.
  • Size 6" x 9"
$115.00

Big Data is a concept of major relevance in today's world, sometimes highlighted as a key asset for productivity, growth, innovation, and customer relationships. Its popularity has increased considerably during recent years. Areas like smart cities, manufacturing, retail, finance, software development, environment, digital media, among others, can benefit from the collection, storage, processing, and analysis of Big Data, leveraging unprecedented data-driven workflows and considerably improved decision-making processes.

The concept of a Big Data Warehouse (BDW) is emerging as either an augmentation or a replacement of the traditional Data Warehouse (DW), a concept that has a long history as one of the most valuable enterprise data assets. Nevertheless, research in Big Data Warehousing is still in its infancy, lacking an integrated and validated approach for designing and implementing both the logical layer (data models, data flows, and interoperability between components) and the physical layer (technological infrastructure) of these complex systems.

This book addresses models and methods for designing and implementing Big Data Systems to support mixed and complex decision processes, giving special attention to BDWs as a way of efficiently storing and processing batch or streaming data for structured or semi-structured analytical problems.

The Authors

Acknowledgments

Foreword

Notation

1. Introduction

2. Big Data Concepts, Techniques and Technologies

3. OLTP-oriented Databases for Big Data Environments

4. LAP-oriented Databases for Big Data Environments

5. Design and Implementation of Big Data Warehouses

6. Big Data Warehouses Modelling: From Theory to Practice

7. Fuelling Analytical Objects in Big Data Warehouses

8. Evaluating the Performance of Big Data Warehouses

9. Big Data Warehousing in Smart Cities

10. Conclusion

References

Index

Maribel Yasmina Santos

Maribel Yasmina Santos, PhD, is Associate Professor at the Department of Information Systems, University of Minho, Portugal; Senior Researcher of the ALGORITMI Research Centre; and leader of SEMAG, the Software-based Information Systems Engineering and Management Group at ALGORITMI. Her research interests include Business Intelligence and Analytics, Big Data Analytics, (Big) Data Warehousing, and Online Analytical Processing.

Carlos Costa

Carlos Costa, PhD, is Invited Lecturer in the field of Information Systems, University of Minho, Portugal. Previous experiences include senior Big Data engineer, researcher, and software developer. He is the co-author of several scientific and technical publications in the area of Big Data, Data Warehousing and Data Science, and he is constantly looking for new ways of contributing to the community of researchers and practitioners related to these topics.

 

Big Data; Big Data Warehouse; Analytics; BDWing; DWing; OLTP; Logical architectures; Technological infrastructures; Data modelling method; Data models; Analytical applications