This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent soluti
Pocket Data Mining: Big Data on Small Devices
β Scribed by Mohamed Medhat Gaber, Frederic Stahl, JoΓ£o BΓ‘rtolo Gomes (auth.)
- Publisher
- Springer International Publishing
- Year
- 2014
- Tongue
- English
- Leaves
- 112
- Series
- Studies in Big Data 2
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
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 PDMdealing 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.
β¦ Table of Contents
Front Matter....Pages 1-7
Introduction....Pages 1-5
Background....Pages 7-21
Pocket Data Mining Framework....Pages 23-40
Implementation of Pocket Data Mining....Pages 41-59
Context-Aware PDM (Coll-Stream) ....Pages 61-68
Experimental Validation of Context-Aware PDM....Pages 69-80
Potential Applications of Pocket Data Mining....Pages 81-94
Conclusions, Discussion and Future Work....Pages 95-98
Back Matter....Pages 99-107
β¦ Subjects
Computational Intelligence; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery
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