Title	Advances in Data and Information Sciences [electronic resource] : Proceedings of ICDIS 2017, Volume 2 / edited by Mohan L. Kolhe, Munesh C. Trivedi, Shailesh Tiwari, Vikash Kumar Singh
Imprint	Singapore : Springer Singapore : Imprint: Springer, 2019
Edition	1st ed. 2019
Connect to	https://doi.org/10.1007/978-981-13-0277-0
Descript	XXI, 396 p. 123 illus., 86 illus. in color. online resource

The book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Data and Information Systems (ICDIS 2017), held at Indira Gandhi National Tribal University, India from November 3 to 4, 2017. The book covers all aspects of computational sciences and information security. In chapters written by leading researchers, developers and practitioner from academia and industry, it highlights the latest developments and technical solutions, helping readers from the computer industry capitalize on key advances in next-generation computer and communication technology

Replica Control Following 1SR in DRTDBS Through Best Case of Transaction Execution -- FPGA Implementation of a Fast Scalar Point Multiplier for an Elliptic Curve Processor -- A Bloom Filter Based Data Deduplication for Big Data -- ITDA: CUBE – Less Architecture for Effective Multidimensional Data Analysis -- On Using Priority Inheritance Based Distributed Static Two Phase Locking Protocol -- A New Way to Find Way Using Depth Direction A* -- ECG Biometric Analysis Using Walsh Hadamard Transform -- A Priority Heuristic Policy in Mobile Distributed Real Time Database System -- A Proposal for Optimization of Horizontal Scaling in Big Data Environment -- A Literature Review on Hadoop Ecosystem and Various Techniques of Bigdata Optimization -- Smart Mobile Bot Detection Through Behavioral Analysis -- Compendium Depiction on the Applications of Cloud Robotics for the Reclamation of Mankind -- A Framework for Data Storage Security in Cloud -- Dynamic Sentiment Analysis Using Multiple Machine Learning Algorithms: A Comparative Knowledge Methodology -- Sentimental Analysis Using Tuned Ensemble Machine Learning Approach