Pocket Data Mining Big Data on Small Devices /

• Κύριοι συγγραφείς: Gaber, Mohamed Medhat (Συγγραφέας), Stahl, Frederic (Συγγραφέας), Gomes, João Bártolo (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2014.
• Σειρά: Studies in Big Data, 2
• Θέματα: Engineering.; Data mining.; Artificial intelligence.; Computational intelligence.; Computational Intelligence.; Artificial Intelligence (incl. Robotics).; Data Mining and Knowledge Discovery.

 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. 

 Pocket Data Mining Framework -- Implementation of Pocket Data Mining -- Context-aware PDM(Coll-Stream) -- Experimental Validation of Context-aware PDM -- Potential Applications of Pocket Data Mining -- Conclusions, Discussion and Future Directions.