Handbook of Big Data Analytics: Applications in ICT, Security and Business Analytics, Volume 2

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