Handbook of Big Data Analytics: 2 Volume Set
Book
811 pages
978-1-83953-061-6 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 234 mm
Width: 156 mm
ISBN-13
978-1-83953-061-6 (9781839530616)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
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.
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.
Content
Volume 1
Chapter 1: The impact of Big Data on databases
Chapter 2: Big data processing frameworks and architectures: a survey
Chapter 3: The role of data lake in big data analytics: recent developments and challenges
Chapter 4: Query optimization strategies for big data
Chapter 5: Toward real-time data processing: an advanced approach in big data analytics
Chapter 6: A survey on data stream analytics
Chapter 7: Architectures of big data analytics: scaling out data mining algorithms using Hadoop-MapReduce and Spark
Chapter 8: A review of fog and edge computing with big data analytics
Chapter 9: Fog computing framework for Big Data processing using cluster management in a resource-constraint environment
Chapter 10: Role of artificial intelligence and big data in accelerating accessibility for persons with disabilities
Overall conclusions
Volume 2
Chapter 1: Big data analytics for security intelligence
Chapter 2: Zero attraction data selective adaptive filtering algorithm for big data applications
Chapter 3: Secure routing in software defined networking and Internet of Things for big data
Chapter 4: Efficient ciphertext-policy attribute-based signcryption for secure big data storage in cloud
Chapter 5: Privacy-preserving techniques in big data
Chapter 6: Big data and behaviour analytics
Chapter 7: Analyzing events for traffic prediction on IoT data streams in a smart city scenario
Chapter 8: Gender-based classification on e-commerce big data
Chapter 9: On recommender systems with big data
Chapter 10: Analytics in e-commerce at scale
Chapter 11: Big data regression via parallelized radial basis function neural network in Apache Spark
Chapter 12: Visual sentiment analysis of bank customer complaints using parallel self-organizing maps
Chapter 13: Wavelet neural network for big data analytics in banking via GPU
Chapter 14: Stock market movement prediction using evolving spiking neural networks
Chapter 15: Parallel hierarchical clustering of big text corpora
Chapter 16: Contract-driven financial reporting: building automated analytics pipelines with algorithmic contracts, Big Data and Distributed Ledger technology
Overall conclusions
Chapter 1: The impact of Big Data on databases
Chapter 2: Big data processing frameworks and architectures: a survey
Chapter 3: The role of data lake in big data analytics: recent developments and challenges
Chapter 4: Query optimization strategies for big data
Chapter 5: Toward real-time data processing: an advanced approach in big data analytics
Chapter 6: A survey on data stream analytics
Chapter 7: Architectures of big data analytics: scaling out data mining algorithms using Hadoop-MapReduce and Spark
Chapter 8: A review of fog and edge computing with big data analytics
Chapter 9: Fog computing framework for Big Data processing using cluster management in a resource-constraint environment
Chapter 10: Role of artificial intelligence and big data in accelerating accessibility for persons with disabilities
Overall conclusions
Volume 2
Chapter 1: Big data analytics for security intelligence
Chapter 2: Zero attraction data selective adaptive filtering algorithm for big data applications
Chapter 3: Secure routing in software defined networking and Internet of Things for big data
Chapter 4: Efficient ciphertext-policy attribute-based signcryption for secure big data storage in cloud
Chapter 5: Privacy-preserving techniques in big data
Chapter 6: Big data and behaviour analytics
Chapter 7: Analyzing events for traffic prediction on IoT data streams in a smart city scenario
Chapter 8: Gender-based classification on e-commerce big data
Chapter 9: On recommender systems with big data
Chapter 10: Analytics in e-commerce at scale
Chapter 11: Big data regression via parallelized radial basis function neural network in Apache Spark
Chapter 12: Visual sentiment analysis of bank customer complaints using parallel self-organizing maps
Chapter 13: Wavelet neural network for big data analytics in banking via GPU
Chapter 14: Stock market movement prediction using evolving spiking neural networks
Chapter 15: Parallel hierarchical clustering of big text corpora
Chapter 16: Contract-driven financial reporting: building automated analytics pipelines with algorithmic contracts, Big Data and Distributed Ledger technology
Overall conclusions