Overview
- Introduction to "Pocket Data Mining"
- Describes the process of performing collaborative distributed data stream mining in the mobile computing environment
- Written by experts in the field
Part of the book series: Studies in Big Data (SBD, volume 2)
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Table of contents (8 chapters)
Keywords
About this book
Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.
Authors and Affiliations
Bibliographic Information
Book Title: Pocket Data Mining
Book Subtitle: Big Data on Small Devices
Authors: Mohamed Medhat Gaber, Frederic Stahl, João Bártolo Gomes
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-319-02711-1
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Hardcover ISBN: 978-3-319-02710-4Published: 28 October 2013
Softcover ISBN: 978-3-319-34686-1Published: 23 August 2016
eBook ISBN: 978-3-319-02711-1Published: 19 October 2013
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: IX, 108
Number of Illustrations: 46 b/w illustrations
Topics: Computational Intelligence, Artificial Intelligence, Data Mining and Knowledge Discovery