DocumentCode :
1680866
Title :
Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments
Author :
Stahl, Frederic ; Gaber, Mohamed Medhat ; Bramer, Max ; Yu, Philip S.
Author_Institution :
Sch. of Comput., Univ. of Portsmouth, Portsmouth, UK
Volume :
2
fYear :
2010
Firstpage :
323
Lastpage :
330
Abstract :
Pocket Data Mining (PDM) is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. This paper proposes a new architecture that we have prototyped for realizing the significant applications in this area. We have proposed using mobile software agents in this application for several reasons. Most importantly the autonomic intelligent behaviour of the agent technology has been the driving force for using it in this application. Other efficiency reasons are discussed in details in this paper. Experimental results showing the feasibility of the proposed architecture are presented and discussed.
Keywords :
Bluetooth; data mining; groupware; media streaming; mobile agents; mobile computing; mobile handsets; notebook computers; wireless LAN; Bluetooth; WiFi; autonomic intelligent behaviour; collaborative data mining; data streaming; decision making; distributed computing environments; mobile computing environments; mobile software agents; pocket data mining; smart mobile phones; wireless communication; Collaboration; Computer architecture; Computers; Data mining; Mobile agents; Mobile communication; Mobile handsets; Distributed Data Mining; Mining Data Streams; Mobile Computing; Pocket Data Mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on
Conference_Location :
Arras
ISSN :
1082-3409
Print_ISBN :
978-1-4244-8817-9
Type :
conf
DOI :
10.1109/ICTAI.2010.118
Filename :
5670091
Link To Document :
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