Pocket data mining: towards collaborative data mining in mobile computing environments

F. Stahl, M. Gaber, Max Bramer, P. Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publication2010 22nd IEEE International Conference on tools with artificial intelligence (ICTAI). Vol. 2
Place of PublicationPiscataway
PublisherIEEE
Pages323-330
ISBN (Print)9781424488179
DOIs
Publication statusPublished - 2010
EventIEEE 22nd International Conference on Tools with Artificial Intelligence - Arras, France
Duration: 27 Oct 201029 Oct 2010

Conference

ConferenceIEEE 22nd International Conference on Tools with Artificial Intelligence
Abbreviated titleICTAI 2010
CountryFrance
CityArras
Period27/10/1029/10/10

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