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February 17, Thursday
12:00 – 14:00

Recent years have seen the emergence of huge distributed systems such as peer-to-peer systems, sensor and ad-hoc networks, and grid systems. Often, these systems produce and store huge amounts of data. Therefore, data access is one of the main services they need to support. More often then not, a user of such a system would access the data in order to analyze it rather than to review it. In such cases, it would make sense to employ data mining algorithms which will analyze the data in-network and only report the result.

This presentation will outline some of the progress we have made as to the development of data mining algorithms suitable for large-scale distributed systems. Specifically, new work on facility location in peer-to-peer and sensor networks. The basic approach we take is to develop local algorithms for these problems. Local algorithms are ones in which each processor typically computes the result based on data it gathers from just a handful of nearby processors. Yet, the algorithms still provide a strong correctness guarantee – that eventually each processor will compute the exact result. The algorithms we develop are entirely asynchronous, pose modest memory requirements, and seamlessly support fail-safe failures and dynamic changes to the data.

This research extends previous work on association rule mining in peer-to-peer networks, which will be briefly reviewed. Other than that it is self contained.

The research is joined work with Denis Krivitski and Prof. Assaf Schuster of the Technion.

Bio:

Ran Wolff has graduated his B.A. and his Ph.D. in computer science from the Technion - Israel. He has numerous publications in the area of data mining and local algorithms for peer-to-peer, Grid, and sensor networks. Currently he is holding a post doctoral position in the University of Maryland, Baltimore County under the supervision of Prof. Hillol Kargupta.

Further details and publication list can be found at http://www.cs.technion.ac.il/~ranw