Data streaming applications, usually composed with sequential/parallel tasks in a data pipeline form, bring new challenges to task scheduling and resource allocation in grid envir...
Concept drifting in data streams often occurs unpredictably at any time. Currently many classification mining algorithms deal with this problem by using an incremental learning ap...
A pattern is considered useful if it can be used to help a person to achieve his goal. Mining data streams for useful patterns is important in many applications. However, data stre...
A fundamental building block of many data mining and analysis approaches is density estimation as it provides a comprehensive statistical model of a data distribution. For that re...
We study the problem of maintaining a sketch of recent elements of a data stream. Motivated by applications involving network data, we consider streams that are asynchronous, in wh...