Handbook of Big Data Analytics: Methodologies (Computing and Networks)

✍ Scribed by Vadlamani Ravi (editor), Aswani Kumar Cherukuri (editor)

• Publisher: The Institution of Engineering and Technology
• Year: 2021
• Tongue: English
• Leaves: 390
• Category: Library

✦ Synopsis

Big Data analytics is the complex process of examining big data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable big data processing and analytics in real time.

In the first volume of this comprehensive two-volume handbook, the authors present several methodologies to support Big Data analytics including database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data stream analytics, Fog and Edge computing, and Artificial Intelligence and Big Data.

The second volume is dedicated to a wide range of applications in secure data storage, privacy-preserving, Software Defined Networks (SDN), Internet of Things (IoTs), behaviour analytics, traffic predictions, gender based classification on e-commerce data, recommender systems, Big Data regression with Apache Spark, visual sentiment analysis, wavelet Neural Network via GPU, stock market movement predictions, and financial reporting.

The two-volume work is aimed at providing a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics.

✦ Table of Contents

Contents
About the editors
About the contributors
Foreword
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
References
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
References
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
References
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
References
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
References
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
References
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
References
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
References
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
References
Overall conclusions Vadlamani | Ravi and Aswani Kumar Cherukuri
Index