Frederic Stahl: Abstract

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining (PDM). Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system.

This emerging area of study has been shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users.

We propose the use of distributed data stream classification techniques in the PDM framework over vertically partitioned data streams.

Organised by BCS SGAI The Specialist Group on Artificial Intelligence http://www.bcs-sgai.org