UNIVERSITY OF HELSINKI -
DEPARTMENT OF COMPUTER SCIENCE
Date Thursday, August 22nd, 1996 Place Room A414 (in the 4th floor, next to the elevators) Time 12:15 Dr. Gautam Das
"Graph spanners: a survey of recent developments"14:15 Dr. Ronen Feldman
"Keyword-Based Browsing and Analysis of Large Document Sets"Both lectures will last approximately an hour.
Dr. Gautam Das from University of Memphis (USA), Department of Mathematical Sciences will give a talk about "Graph spanners: a survey of recent developments".At the moment, Dr. Das is an Associate Professor at Dept. of Mathematical Sciences, The University of Memphis. His research interests include computational geometry, analysis of algorithms, robotics, and motion planning.
Abstract
Given a weighted graph G, a subgraph G' is a t-spanner if between any pairs of vertices, the shortest distance in G' is at most t times the shortest distance in G. Thus G' "approximates" distances in G. In network design, it is frequently necessary to construct t-spanners of dense graphs, such that the spanners have additional properties such as small size, weight, degree and diameter. Spanners find applications in network design, robotics and motion planning, distributed systems and algorithms, geometric clustering, and in approximation algorithms for shortest path problems.
This talk will survey the field of graph spanners and describe some of the recent results.
Dr. Ronen Feldman from Bar-Ilan University (Israel), Department of Mathematics and Computer Science will give a talk about "Keyword-Based Browsing and Analysis of Large Document Sets".At the moment, Dr. Feldman is a Lecturer at Dept. of Computer Science, Bar-Ilan University. His research interest include machine learning, knowledge discovery, expert systems, and telecommunication systems.
Abstract
Knowledge Discovery in Databases (KDD) focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form.
In this talk I will describes the KDT system for Knowledge Discovery in Texts. It is built on top of a text-categorization paradigm where text articles are annotated with keywords organized in a hierarchical structure. Knowledge discovery is performed by analyzing the co-occurrence frequencies of keywords from this hierarchy in the various documents. I will show how this term-frequency approach supports a range of KDD operations, providing a suitable foundation for knowledge discovery and exploration in collections of unstructured text. In addition I will present the FACT system for finding associations in collections of text.
Joint work with Ido Dagan and Haym Hirsh.