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E-raamat: Handbook of Big Data Analytics: Applications in ICT, security and business analytics, Volume 2

Edited by (Vellore Institute of Technology, School of Information Technology and Engineering, Vellore, India), Edited by (Institute for Development and Research in Banking Technology, Hyderabad, India)
  • Formaat: EPUB+DRM
  • Sari: Computing and Networks
  • Ilmumisaeg: 20-Sep-2021
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781839530609
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Handbook of Big Data Analytics: Applications in ICT, security and business analytics, Volume 2Suurem pilt
  • Formaat: EPUB+DRM
  • Sari: Computing and Networks
  • Ilmumisaeg: 20-Sep-2021
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781839530609
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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.



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.

About the editors xv
About the contributors xvii
Foreword xxv
Foreword xxvii
Preface xxix
Acknowledgements xxxi
Introduction xxxiii
1 Big data analytics for security intelligence
1(20)
Sumaiya Thaseen Ikram
Aswani Kumar Cherukuri
Gang Li
Xiao Liu
1.1 Introduction to big data analytics
1(1)
1.2 Big data: huge potentials for information security
2(3)
1.3 Big data challenges for cybersecurity
5(1)
1.4 Related work on decision engine techniques
5(2)
1.5 Big network anomaly detection
7(1)
1.6 Big data for large-scale security monitoring
7(3)
1.7 Mechanisms to prevent attacks
10(2)
1.8 Big data analytics for intrusion detection system
12(3)
1.8.1 Challenges of ADS
12(1)
1.8.2 Components of ADS
12(3)
1.9 Conclusion
15(6)
Acknowledgment
15(1)
Abbreviations
15(1)
References
16(5)
2 Zero attraction data selective adaptive filtering algorithm for big data applications
21(16)
Sivashanmugam Radhika
Arumugam Chandrasekar
2.1 Introduction
21(2)
2.2 System model
23(1)
2.3 Proposed data preprocessing framework
24(5)
2.3.1 Proposed update rule
26(1)
2.3.2 Selection of thresholds
27(1)
2.3.3 Sparsity model
28(1)
2.4 Simulations
29(4)
2.5 Conclusions
33(4)
References
33(4)
3 Secure routing in software defined networking and Internet of Things for big data
37(36)
Jayashree Pougajendy
Arun Raj Kumar Parthiban
Sarath Babu
3.1 Introduction
37(3)
3.2 Architecture of IoT
40(1)
3.3 Intersection of big data and IoT
41(1)
3.4 Big data analytics
42(3)
3.4.1 Taxonomy of big data analytics
42(1)
3.4.2 Architecture of IoT big data
43(2)
3.5 Security and privacy challenges of big data
45(1)
3.6 Routing protocols in IoT
46(1)
3.7 Security challenges and existing solutions in IoT routing
47(2)
3.7.1 Selective forwarding attacks
47(1)
3.7.2 Sinkhole attacks
47(1)
3.7.3 HELLO flood and acknowledgment spoofing attacks
48(1)
3.7.4 Replay attacks
48(1)
3.7.5 Wormhole attacks
48(1)
3.7.6 Sybil attack
48(1)
3.7.7 Denial-of-service (DoS) attacks
49(1)
3.8 The arrival of SDN into big data and IoT
49(1)
3.9 Architecture of SDN
50(4)
3.10 Routing in SDN
54(4)
3.11 Attacks on SDN and existing solutions
58(6)
3.11.1 Conflicting flow rules
58(3)
3.11.2 TCAM exhaustion
61(1)
3.11.3 ARP poisoning
62(1)
3.11.4 Information disclosure
62(1)
3.11.5 Denial-of-service (DoS) attacks
63(1)
3.11.6 Exploiting vulnerabilities in OpenFlow switches
63(1)
3.11.7 Exploiting vulnerabilities in SDN controllers
63(1)
3.12 Can SDN be applied to IoT?
64(1)
3.13 Summary
65(8)
References
66(7)
4 Efficient ciphertext-policy attribute-based signcryption for secure big data storage in cloud
73(30)
Praveen Kumar Premkamal
Syam Kumar Pasupuleti
Alphonse Pja
4.1 Introduction
74(2)
4.1.1 Related work
75(1)
4.1.2 Contributions
76(1)
4.2 Preliminaries
76(4)
4.2.1 Security model
78(2)
4.3 System model
80(2)
4.3.1 System architecture of ECP-ABSC
80(1)
4.3.2 Formal definition of ECP-ABSC
81(1)
4.3.3 Security goals
82(1)
4.4 Construction of ECP-ABSC scheme
82(5)
4.4.1 Setup
82(1)
4.4.2 Key generation
83(1)
4.4.3 Signcrypt
84(1)
4.4.4 Designcrypt
85(2)
4.5 Security analysis
87(7)
4.6 Performance evaluation
94(5)
4.7 Conclusion
99(4)
References
99(4)
5 Privacy-preserving techniques in big data
103(24)
Remya Krishnan Pacheeri
Arun Raj Kumar Parthiban
5.1 Introduction
103(2)
5.2 Big data privacy in data generation phase
105(2)
5.2.1 Access restriction
105(1)
5.2.2 Data falsification
106(1)
5.3 Big data privacy in data storage phase
107(2)
5.3.1 Attribute-based encryption
107(1)
5.3.2 Identity-based encryption
107(1)
5.3.3 Homomorphic encryption
108(1)
5.3.4 Storage path encryption
108(1)
5.3.5 Usage of hybrid clouds
109(1)
5.4 Big data privacy in data processing phase
109(2)
5.4.1 Protect data from unauthorized disclosure
109(2)
5.4.2 Extract significant information without trampling privacy
111(1)
5.5 Traditional privacy-preserving techniques and its scalability in big data
111(4)
5.5.1 Data anonymization
112(3)
5.5.2 Notice and consent
115(1)
5.6 Recent privacy preserving techniques in big data
115(6)
5.6.1 HybrEx
115(2)
5.6.2 Differential privacy
117(2)
5.6.3 Hiding a needle in a haystack: privacy-preserving a priori algorithm in MapReduce framework
119(2)
5.7 Privacy-preserving solutions in resource constrained devices
121(1)
5.8 Conclusion
122(5)
References
123(4)
6 Big data and behaviour analytics
127(18)
Amit Kumar Tyagi
Keesara Sravanthi
Gillala Rekha
6.1 Introduction about big data and behaviour analytics
128(2)
6.2 Related work
130(3)
6.3 Motivation
133(1)
6.4 Importance and benefits of big data and behaviour analytics
133(1)
6.4.1 Importance of big data analytics
133(1)
6.5 Existing algorithms, tools available for data analytics and behaviour analytics
134(2)
6.5.1 Apache Hadoop
135(1)
6.5.2 Cloudera
135(1)
6.5.3 Cassandra
135(1)
6.5.4 Konstanz Information Miner
135(1)
6.5.5 Data wrapper
135(1)
6.5.6 MongoDB
136(1)
6.5.7 HPCC
136(1)
6.6 Open issues and challenges with big data analytics and behaviour analytics
136(2)
6.6.1 Challenges with big data analytics
136(1)
6.6.2 Issues with big data analytics (BDA)
137(1)
6.7 Opportunities for future researchers
138(1)
6.8 A taxonomy for analytics and its related terms
139(1)
6.9 Summary
139(6)
Appendix A
140(2)
References
142(3)
7 Analyzing events for traffic prediction on loT data streams in a smart city scenario
145(24)
Chittaranjan Hota
Sanket Mishra
7.1 Introduction
146(2)
7.2 Related works
148(1)
7.3 Research preliminaries
148(7)
7.3.1 Dataset description
148(2)
7.3.2 Data ingestion
150(1)
7.3.3 Complex event processing
151(1)
7.3.4 Clustering approaches
151(1)
7.3.5 OpenWhisk
152(1)
7.3.6 Evaluation metrics
153(2)
7.4 Proposed methodology
155(5)
7.4.1 Statistical approach to optimize the number of retrainings
159(1)
7.5 Experimental results and discussion
160(4)
7.6 Conclusion
164(5)
Acknowledgment
165(1)
References
165(4)
8 Gender-based classification on e-commerce big data
169(28)
Chaitanya Kanchibhotla
Venkata Lakshmi Narayana Somayajulu Durvasula
Radha Krishna Pisipati
8.1 Introduction
170(4)
8.1.1 E-Commerce and big data
171(3)
8.2 Gender prediction methodology
174(20)
8.2.1 Gender prediction based on gender value
174(11)
8.2.2 Classification using random forest
185(3)
8.2.3 Classification using gradient-boosted trees (GBTs)
188(2)
8.2.4 Experimental results with state-of-the-art classifiers
190(4)
8.3 Summary
194(3)
References
195(2)
9 On recommender systems with big data
197(32)
Lakshmikanth Paleti
P. Radha Krishna
J. V.R. Murthy
9.1 Introduction
198(2)
9.1.1 Big data and recommender systems
199(1)
9.2 Recommender systems challenges
200(4)
9.2.1 Big-data-specific challenges in RS
202(2)
9.3 Techniques and approaches for recommender systems
204(14)
9.3.1 Early recommender systems
205(7)
9.3.2 Big-data recommender systems
212(5)
9.3.3 X-aware recommender systems
217(1)
9.4 Leveraging big data analytics on recommender systems
218(2)
9.4.1 Healthcare
218(1)
9.4.2 Education
219(1)
9.4.3 Banking and finance
219(1)
9.4.4 Manufacturing
220(1)
9.5 Evaluation metrics
220(1)
9.6 Popular datasets for recommender systems
221(2)
9.7 Conclusion
223(6)
References
223(6)
10 Analytics in e-commerce at scale
229(12)
Vaidyanathan Subramanian
Arya Ketan
10.1 Background
229(1)
10.2 Analytics use cases
230(2)
10.2.1 Business and system metrics
230(2)
10.2.2 Data sciences
232(1)
10.3 Data landscape
232(2)
10.3.1 Data producers
233(1)
10.3.2 Data consumers
233(1)
10.3.3 Data freshness
234(1)
10.3.4 Data governance
234(1)
10.4 Architecture
234(5)
10.4.1 Data ingestion
235(1)
10.4.2 Data preprocessing
236(1)
10.4.3 Batch data processing
237(1)
10.4.4 Streaming processing
238(1)
10.4.5 Report visualization
238(1)
10.4.6 Query platform
239(1)
10.4.7 Data governance
239(1)
10.5 Conclusion
239(2)
11 Big data regression via parallelized radial basis function neural network in Apache Spark
241(10)
Sheikh Kamaruddin
Vadlamani Ravi
11.1 Introduction
241(1)
11.2 Motivation
242(1)
11.3 Contribution
242(1)
11.4 Literature review
242(1)
11.5 Proposed methodology
243(3)
11.5.1 Introduction to K-means++
243(1)
11.5.2 Introduction to K-means
243(1)
11.5.3 Introduction to parallel bisecting A"-means
244(1)
11.5.4 PRBFNN: the proposed approach
244(2)
11.6 Experimental setup
246(1)
11.7 Dataset description
246(2)
11.8 Results and discussion
248(1)
11.9 Conclusion and future directions
248(3)
References
249(2)
12 Visual sentiment analysis of bank customer complaints using parallel self-organizing maps
251(22)
Rohit Gawal
Vadlamani Ravi
Kalavala Revanth Harsha
Akhilesh Gangwar
Kumar Ravi
12.1 Introduction
251(2)
12.2 Motivation
253(1)
12.3 Contribution
254(1)
12.4 Literature survey
254(1)
12.5 Description of the techniques used
255(1)
12.5.1 Self-organizing feature maps
255(1)
12.5.2 Compute Unified Device Architecture
255(1)
12.6 Proposed approach
256(2)
12.6.1 Text preprocessing
257(1)
12.6.2 Implementation of CUDASOM
257(1)
12.6.3 Segmentation of customer complaints using SOM
258(1)
12.7 Experimental setup
258(2)
12.7.1 Dataset details
259(1)
12.7.2 Preprocessing steps
259(1)
12.7.3 CUDA setup
260(1)
12.8 Results and discussion
260(8)
12.8.1 Segmentation of customer complaints using CUDASOM
260(5)
12.8.2 Performance of CUDASOM
265(3)
12.9 Conclusions and future directions
268(5)
Acknowledgments
268(1)
References
269(4)
13 Wavelet neural network for big data analytics in banking via GPU
273(12)
Satish Doppalapudi
Vadlamani Ravi
13.1 Introduction
273(1)
13.2 Literature review
274(3)
13.3 Techniques employed
277(1)
13.4 Proposed methodology
277(1)
13.5 Experimental setup
278(1)
13.5.1 Datasets description
278(1)
13.5.2 Experimental procedure
279(1)
13.6 Results and discussion
279(3)
13.7 Conclusion and future work
282(3)
References
282(3)
14 Stock market movement prediction using evolving spiking neural networks
285(28)
Rasmi Ranjan Khansama
Vadlamani Ravi
Akshay Raj Gollahalli
Neelava Sengupta
Nikola K. Kasabov
Imanol Bilbao-Quintana
14.1 Introduction
286(1)
14.2 Literature review
287(1)
14.3 Motivation
288(1)
14.4 The proposed SI-eSNN model for stock trend prediction based on stock indicators
289(5)
14.4.1 Overall architecture
289(2)
14.4.2 Neural encoding
291(1)
14.4.3 Neural model
292(1)
14.4.4 Learning in the output neurons
292(1)
14.4.5 Algorithm for eSNN training
293(1)
14.4.6 Testing (recall of the model on new data)
294(1)
14.5 The proposed CUDA-eSNN model: a parallel eSNN model for GPU machines
294(1)
14.6 Dataset description and experiments with the SI-eSNN and the CUDA-eSNN models
295(2)
14.7 Sliding window (SW)-eSNN for incremental learning and stock movement prediction
297(8)
14.8 Gaussian receptive fields influence
305(3)
14.9 Conclusion and future directions
308(5)
References
309(4)
15 Parallel hierarchical clustering of big text corpora
313(30)
Karthick Seshadri
15.1 Introduction
313(4)
15.2 Parallel hierarchical clustering algorithms
317(6)
15.2.1 Agglomerative clustering
318(1)
15.2.2 Graph-based clustering
318(1)
15.2.3 Partitional clustering algorithms
319(1)
15.2.4 Parallel clustering on SIMD/MIMD machines
320(1)
15.2.5 Density-based clustering algorithms
321(1)
15.2.6 Transform-based clustering
321(1)
15.2.7 Grid-based clustering
322(1)
15.2.8 Evolutionary clustering
322(1)
15.2.9 Spectral clustering
322(1)
15.2.10 Latent model-based clustering
323(1)
15.3 Parallel document clustering algorithms
323(2)
15.4 Parallel hierarchical algorithms for big text clustering
325(12)
15.4.1 Parallel hierarchical cut clustering
326(2)
15.4.2 Parallel hierarchical latent semantic analysis
328(3)
15.4.3 Parallel hierarchical modularity-based spectral clustering
331(3)
15.4.4 Parallel hierarchical latent Dirichlet allocation
334(1)
15.4.5 PHCUT vs. PHLSA vs. PHMS vs. PHLDA
335(2)
15.4.6 Research challenges addressed
337(1)
15.5 Open research challenges
337(1)
15.6 Concluding remarks
338(5)
References
339(4)
16 Contract-driven financial reporting: building automated analytics pipelines with algorithmic contracts, Big Data and Distributed Ledger technology
343(24)
Wolfgang Breymann
Nils Bundi
Kurt Stockinger
16.1 Introduction
343(3)
16.2 The ACTUS methodology
346(2)
16.3 The mathematics of ACTUS
348(6)
16.3.1 Contract terms, contract algorithms and cash flow streams
348(2)
16.3.2 Description of cash flow streams
350(1)
16.3.3 Standard analytics as linear operators
351(3)
16.4 ACTUS in action: proof of concept with a bond portfolio
354(5)
16.5 Scalable financial analytics
359(5)
16.6 Towards future automated reporting
364(3)
16.7 Conclusion
367(1)
Acknowledgements 367(1)
References 367(4)
Overall conclusions 371(2)
Vadlamani Ravi
Aswani Kumar Cherukuri
Index 373
Vadlamani Ravi is a professor at the Institute for Development and Research in Banking Technology, Hyderabad, where he spearheads the Center of Excellence in Analytics, the first-of-its-kind in India. He has over 32 years of experience in research and teaching. He is on the Editorial Board several international journals. He has published more than 230 papers in international journals, conferences and book chapters.



Aswani Kumar Cherukuri is a professor of the School of Information Technology and Engineering at Vellore Institute of Technology, India. He has almost 20 years of academic and research experience. His research interests include machine learning and information security. He has published more than 150 research papers in various journals and conferences, and executed major research projects funded by Govt. of India. He is a senior member of ACM and life member of CSI, ISTE.
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