Handbook of Big Data Analytics: Methodologies (Volume 1) (Computing and Networks)

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