Abstract:

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 progresss we have made as to
the development of data mining algorithms suitable for large-scale
distributed systems. 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 near by 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.

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.