Handbook of Big Data Analytics: Applications in ICT, security and business analytics (Volume 2) (Computing and Networks)

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

• Publisher: Institution of Engineering and Technology
• Year: 2021
• Tongue: English
• Leaves: 419
• 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. Big data analytics for security intelligence | Sumaiya Thaseen Ikram, Aswani Kumar Cherukuri, Gang Li and Xiao Liu
1.1 Introduction to big data analytics
1.2 Big data: huge potentials for information security
1.3 Big data challenges for cybersecurity
1.4 Related work on decision engine techniques
1.5 Big network anomaly detection
1.6 Big data for large-scale security monitoring
1.7 Mechanisms to prevent attacks
1.8 Big data analytics for intrusion detection system
1.9 Conclusion
Acknowledgment
Abbreviations
References
2. Zero attraction data selective adaptive filtering algorithm for big data applications | Sivashanmugam Radhika and Arumugam Chandrasekar
2.1 Introduction
2.2 System model
2.3 Proposed data preprocessing framework
2.4 Simulations
2.5 Conclusions
References
3. Secure routing in software defined networking and Internet of Things for big data | Jayashree Pougajendy, Arun Raj Kumar Parthiban and Sarath Babu
3.1 Introduction
3.2 Architecture of IoT
3.3 Intersection of big data and IoT
3.4 Big data analytics
3.5 Security and privacy challenges of big data
3.6 Routing protocols in IoT
3.7 Security challenges and existing solutions in IoT routing
3.8 The arrival of SDN into big data and IoT
3.9 Architecture of SDN
3.10 Routing in SDN
3.11 Attacks on SDN and existing solutions
3.12 Can SDN be applied to IoT?
3.13 Summary
References
4. Efficient ciphertext-policy attribute-based signcryption for secure big data storage in cloud | Praveen Kumar Premkamal, Syam Kumar Pasupuleti and Alphonse PJA
4.1 Introduction
4.2 Preliminaries
4.3 System model
4.4 Construction of ECP-ABSC scheme
4.5 Security analysis
4.6 Performance evaluation
4.7 Conclusion
References
5. Privacy-preserving techniques in big data | Remya Krishnan Pacheeri and Arun Raj Kumar Parthiban
5.1 Introduction
5.2 Big data privacy in data generation phase
5.3 Big data privacy in data storage phase
5.4 Big data privacy in data processing phase
5.5 Traditional privacy-preserving techniques and its scalability in big data
5.6 Recent privacy preserving techniques in big data
5.7 Privacy-preserving solutions in resource constrained devices
5.8 Conclusion
References
6. Big data and behaviour analytics | Amit Kumar Tyagi, Keesara Sravanthi and Gillala Rekha
6.1 Introduction about big data and behaviour analytics
6.2 Related work
6.3 Motivation
6.4 Importance and benefits of big data and behaviour analytics
6.5 Existing algorithms, tools available for data analytics and behaviour analytics
6.6 Open issues and challenges with big data analytics and behaviour analytics
6.7 Opportunities for future researchers
6.8 A taxonomy for analytics and its related terms
6.9 Summary
Appendix A
References
7. Analyzing events for traffic prediction on IoT data streams in a smart city scenario | Chittaranjan Hota and Sanket Mishra
7.1 Introduction
7.2 Related works
7.3 Research preliminaries
7.4 Proposed methodology
7.5 Experimental results and discussion
7.6 Conclusion
Acknowledgment
References
8. Gender-based classification on e-commerce big data | Chaitanya Kanchibhotla, Venkata Lakshmi Narayana Somayajulu Durvasula and Radha Krishna Pisipati
8.1 Introduction
8.2 Gender prediction methodology
8.3 Summary
References
9. On recommender systems with big data | Lakshmikanth Paleti, P. Radha Krishna and J.V.R. Murthy
9.1 Introduction
9.2 Recommender systems challenges
9.3 Techniques and approaches for recommender systems
9.4 Leveraging big data analytics on recommender systems
9.5 Evaluation metrics
9.6 Popular datasets for recommender systems
9.7 Conclusion
References
10. Analytics in e-commerce at scale | Vaidyanathan Subramanian and Arya Ketan
10.1 Background
10.2 Analytics use cases
10.3 Data landscape
10.4 Architecture
10.5 Conclusion
11. Big data regression via parallelized radial basis function neural network in Apache Spark | Sheikh Kamaruddin and Vadlamani Ravi
11.1 Introduction
11.2 Motivation
11.3 Contribution
11.4 Literature review
11.5 Proposed methodology
11.6 Experimental setup
11.7 Dataset description
11.8 Results and discussion
11.9 Conclusion and future directions
References
12. Visual sentiment analysis of bank customer complaints using parallel self-organizing maps | Rohit Gavval, Vadlamani Ravi, Kalavala Revanth Harsha, Akhilesh Gangwar and Kumar Ravi
12.1 Introduction
12.2 Motivation
12.3 Contribution
12.4 Literature survey
12.5 Description of the techniques used
12.6 Proposed approach
12.7 Experimental setup
12.8 Results and discussion
12.9 Conclusions and future directions
Acknowledgments
References
13. Wavelet neural network for big data analytics in banking via GPU | Satish Doppalapudi and Vadlamani Ravi
13.1 Introduction
13.2 Literature review
13.3 Techniques employed
13.4 Proposed methodology
13.5 Experimental setup
13.6 Results and discussion
13.7 Conclusion and future work
References
14. Stock market movement prediction using evolving spiking neural networks | Rasmi Ranjan Khansama, Vadlamani Ravi, Akshay Raj Gollahalli, Neelava Sengupta, Nikola K. Kasabov and Imanol Bilbao-Quintana
14.1 Introduction
14.2 Literature review
14.3 Motivation
14.4 The proposed SI-eSNN model for stock trend prediction based on stock indicators
14.5 The proposed CUDA-eSNN model: a parallel eSNN model for GPU machines
14.6 Dataset description and experiments with the SI-eSNN and the CUDA-eSNN models
14.7 Sliding window (SW)-eSNN for incremental learning and stock movement prediction
14.8 Gaussian receptive fields influence
14.9 Conclusion and future directions
References
15. Parallel hierarchical clustering of big text corpora | Karthick Seshadri
15.1 Introduction
15.2 Parallel hierarchical clustering algorithms
15.3 Parallel document clustering algorithms
15.4 Parallel hierarchical algorithms for big text clustering
15.5 Open research challenges
15.6 Concluding remarks
References
16. Contract-driven financial reporting: building automated analytics pipelines with algorithmic contracts, Big Data and Distributed Ledger technology | Wolfgang Breymann, Nils Bundi and Kurt Stockinger
16.1 Introduction
16.2 The ACTUS methodology
16.3 The mathematics of ACTUS
16.4 ACTUS in action: proof of concept with a bond portfolio
16.5 Scalable financial analytics
16.6 Towards future automated reporting
16.7 Conclusion
Acknowledgements
References
Overall conclusions | Vadlamani Ravi and Aswani Kumar Cherukuri
Index