Handbook of big data analytics. volume 1, methodologies / edited by Vadlamani Ravi and Aswani Kumar Cherukuri.

Format:

Book

Contributor:

Ravi, Vadlamani, editor.

Cherukuri, Aswani Kumar, editor.

Series:

IET book series on big data.

IET book series on big data

Language:

English

Subjects (All):

Big data--Statistical methods.

Big data.

Physical Description:

1 online resource : illustrations.

Edition:

1st ed.

Place of Publication:

Stevenage : The Institution of Engineering & Technology, 2021.

Summary:

This comprehensive edited 2-volume handbook provides a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics. The first volume presents methodologies that support Big Data analytics, while the second volume offers a wide range of Big Data analytics applications.

Contents:

Intro

Contents

About the editors

About the contributors

Foreword

Preface

Acknowledgements

Introduction

1. The impact of Big Data on databases | Antonio Sarasa Cabezuelo

1.1 The Big Data phenomenon

1.2 Scalability in relational databases

1.3 NoSQL databases

1.4 Data distribution models

1.5 Design examples using NoSQL databases

1.6 Design examples using NoSQL databases

1.7 Conclusions

References

2. Big data processing frameworks and architectures: a survey | Raghavendra Kumar Chunduri and Aswani Kumar Cherukuri

2.1 Introduction

2.2 Apache Hadoop framework and Hadoop Ecosystem

2.3 HaLoop framework

2.4 Twister framework

2.5 Apache Pig

2.6 Apache Mahout

2.7 Apache Sqoop

2.8 Apache Flume

2.9 Apache Oozie

2.10 Hadoop 2

2.11 Apache Spark

2.12 Big data storage systems

2.13 Distributed stream processing engines

2.14 Apache Zookeeper

2.15 Open issues and challenges

2.16 Conclusion

3. The role of data lake in big data analytics: recent developments and challenges | T. Ramalingeswara Rao, Pabitra Mitra and Adrijit Goswami

3.1 Introduction

3.2 Taxonomy of data lakes

3.3 Architecture of a data lake

3.4 Commercial-based data lakes

3.5 Open source-based data lakes

3.6 Case studies

3.7 Conclusion

4. Query optimization strategies for big data | Nagesh Bhattu Sristy, Prashanth Kadari and Harini Yadamreddy

4.1 Introduction

4.2 Multi-way joins using MapReduce

4.3 Graph queries using MapReduce

4.4 Multi-way spatial join

4.5 Conclusion and future work

5. Toward real-time data processing: an advanced approach in big data analytics | Shafqat Ul Ahsaan, Harleen Kaur and Sameena Naaz

5.1 Introduction

5.2 Real-time data processing topology

5.3 Streaming processing.

5.4 Stream mining

5.5 Lambda architecture

5.6 Stream processing approach for big data

5.7 Evaluation of data streaming processing approaches

5.8 Conclusion

Acknowledgment

6. A survey on data stream analytics | Sumit Misra, Sanjoy Kumar Saha and Chandan Mazumdar

6.1 Introduction

6.2 Scope and approach

6.3 Prediction and forecasting

6.4 Outlier detection

6.5 Concept drift detection

6.6 Mining frequent item sets in data stream

6.7 Computational paradigm

6.8 Conclusion

7. Architectures of big data analytics: scaling out data mining algorithms using Hadoop-MapReduce and Spark | Sheikh Kamaruddin and Vadlamani Ravi

7.1 Introduction

7.2 Previous related reviews

7.3 Review methodology

7.4 Review of articles in the present work

7.5 Discussion

7.6 Conclusion and future directions

8. A review of fog and edge computing with big data analytics | Ch. Rajyalakshmi, K. Ram Mohan Rao and Rajeswara Rao Ramisetty

8.1 Introduction

8.2 Introduction to cloud computing with IoT applications

8.3 Importance of fog computing

8.4 Significance of edge computing

8.5 Architecture review with cloud and fog and edge computing with IoT applications

8.6 Conclusion

9. Fog computing framework for Big Data processing using cluster management in a resource-constraint environment | Srinivasa Raju Rudraraju, Nagender Kumar Suryadevara and Atul Negi

9.1 Introduction

9.2 Literature survey

9.3 System description

9.4 Implementation details

9.5 Results and discussion

9.6 Conclusion and future work

10. Role of artificial intelligence and big data in accelerating accessibility for persons with disabilities | Kundumani Srinivasan Kuppusamy

10.1 Introduction

10.2 Rationale for accessibility.

10.3 Artificial intelligence for accessibility

10.4 Conclusions

Overall conclusions Vadlamani | Ravi and Aswani Kumar Cherukuri

Index.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

ISBN:

9781839530586

1839530588

OCLC:

1263027797