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