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Call for Chapters: IET Handbook of Big Data Analytics
Book Series: IET Book Series on Big Data
Call for Chapters: IET handbooks on Big Data Analytics
Editors
Dr. Vadlamani Ravi, Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, India; http://www.idrbt.ac.in/vravi.html
Dr. Aswani Kumar Cherukuri, Vellore Institute of Technology (VIT), Vellore, India. https://www.linkedin.com/in/cherukuri9
"Big Data" characterized by 5 Vs (volume, velocity, variety, veracity and value) has graduated from being a fad into an absolute necessity. Consequently, big data analytics has become ubiquitous in all fields of science, engineering, medicine, business and management. This has been possible because of (i) the phenomenal rise in a variety of data-driven models across all forms of analytics -descriptive, predictive and prescriptive as well as (ii) the concomitant developments in data engineering wherein parallel and distributed computational frameworks for ingesting and analyzing big data have been proposed. Hadoop MapReduce and Apache Spark are the two successful frameworks in this context. Further, many of the three forms of analytical models have been made scalable (rendered amenable for distributed and parallel computation) in three ways: (i) horizontal (ii) vertical and (iii) hybrid. Horizontal parallelization refers to cluster computing where a master and host of slave nodes perform computation in parallel. This involves both data and algorithm parallelization with the help of Hadoop or Spark frameworks. Vertical parallelization involves GPGPU programming where a single server has multiple GPU processors. Finally, hybrid parallelization integrates both concepts with a cluster of GPU -based servers. Near real-time data analytics are the reality now with the availability of memory at cheaper prices. Several frameworks are
currently available to achieve real-time big data analytics. However, there are still many potential issues
Volume 1- Methodologies under the frameworks Hadoop MapReduce, Apache Spark and GPGPU programming:
Clustering, Classification, Association rule Mining, Regression, Outlier detection
Recommender systems
Text analytics
Subspace learning
Data lakes, Data Fabric & Data cataloging
Big data tensor models
In-memory databases & In-database analytics
Imbalanced data – learning, classification, dimensionality reduction and analytics
Parallelism techniques
Indexing approaches
Data partitioning strategies
Data curation methods
High dimensional models
Advances in High Dimensional Big data analytics
Spatial and temporal big data analytics
Streaming and real-time data analytics
Scalable search architectures
Cognitive data analytics
Data lineage
Big Data aggregation and interpretation and Big data analytics capabilities
Smoothing models, Statistical models for big data analytics, Sparse learning models for big data and analytics
Big data analytics in uncertain environments
Graph and bulk parallel processing paradigms
Computational science and intelligence
Practical case studies that deal with scalability
Machine learning algorithms for Big data analytics including deep learning
Important Dates
Energy considerations for Big data analytics
Data governance issues for Big data analytics: data policies and processes viz GDPR.
Volume 2- Application areas of interest include, but not limited to:
Security (Cyber Security, Cyber intelligence and defence, Crime & Fraud Analytics, Exploratory security analytics, Big data analytics for security intelligence, Cyber forensics etc.)
Internet & Dark Web Data Analytics
IoT & Cyber-Physical Systems Data Analytics
Private preserving data analytics
Time series data analytics
Big data analytics for customer churn prediction
Knowledge-centered Big data analytics
Big data and behaviour analytics
Big Social data analytics
Big data analytics with Cloud, Fog and Edge computing.
Financial Services (Banking, Stock markets and Insurance sectors)
Big data analytics for Business (customer analytics subsuming churn prediction, credit scoring, customer acquisition, campaign management, sentiment analysis, recommendation system, operational problems)
Management data analytics
Video and visual analytics
Big data analytics in Marketing
Ethical implications of big data analytics.
that need to be addressed for big data processing and analytics in real time. Big Data Analytics using the Open Source frameworks such as Hadoop, Spark, Cassandra, MongoDB, etc. are at a massive scale. This trend will continue to grow. This edited 2-volume handbook will present a large spectrum of contributions from methodologies of Big data analytics to applications. It is aimed at providing a unique platform for researchers, engineers, developers, educators and students.
These two volumes will be indexed in Scopus, ISI/Web of Science, INSPEC and Google Scholar.
1. Submission of the Chapter Proposals: July 31st, 2019.
2. Notification of Acceptance of the Chapter Proposal: August 15th, 2019.
3. Submission of the Full Chapter: October 31st, 2019.
4. Reviews to the authors: December 15th, 2019.
5. Revised Chapter Submissions: February 28th, 2020.
6. Notification of Final Acceptance: March 31st, 2020.
For the proposed chapter, please provide a title and a brief abstract. For further details and submissions, please contact: [email protected]
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