DIMACS Workshop on Streaming Data Analysis and Mining

November 5, 2001
DIMACS Center, CoRE Building, Rutgers University, Piscataway, NJ

Adam Buchsbaum, AT&T Labs - Research, alb@research.att.com
Rajeev Motwani, Stanford University, rajeev@cs.stanford.edu
Jennifer Rexford, AT&T Labs, jrex@research.att.com
Presented under the auspices of the Special Focus on Data Analysis and Mining.

Working Group on Streaming Data Analysis and Mining Home Page.

This material is based upon work supported by the National Science Foundation under Grant No. 0100921

Workshop Program:

8:00-8:50       Breakfast and Registration

8:50-8:55       Welcome and Greeting: 
                Fred S. Roberts, DIMACS Director

8:55-9:00       Welcome and Greeting:
                Adam Buchsbaum, AT&T Labs - Research
                Rajeev Motwani, Stanford University
                Jennifer Rexford, AT&T Labs             

9:00-9:30	Fast, Small-Space Algorithms for Approximate Histogram 
		Martin Strauss, AT&T Labs - Research

9:30-10:00	Space-Efficient Algorithms for Maintaining Multi-Dimensional 
		Nitin Thaper, MIT

10:00-10:30	Maintaining Stream Statistics over Sliding Windows
		Mayur Datar, Stanford University

10:30-10:45     Break

10:45-11:15	Finding Frequent Items in Data Streams
		Kevin Chen, University of California, Berkeley

11:15-11:45	Clustering Data Streams
		Liadan O'Callaghan, Stanford University

11:45-12:15	Open Problems in Data Stream Algorithmics
		Sampath Kannan, University of Pennsylvania

12:15-1:30      Lunch

1:30-2:00	Computing Traffic Demands From Flow-Level Measurements
		Jennifer Rexford, AT&T Labs - Research

2:00-2:30	Analyzing Transaction Streams with Hancock
		Anne Rogers, AT&T Labs - Research

2:30-3:00	Massive Lossless Data Compression and Multiple Parameter
		Estimation from Galaxy Spectra
		Raul Jimenez, Rutgers University

3:00-3:15       Break

3:15-3:45	A Large-Scale Network Visualization System
		Stephen North, AT&T Labs - Research

3:45-4:15	A Streaming Framework for Scalable Visualization on Clusters
		Greg Humphreys, Stanford University

4:15-4:45	Characterizing Memory Requirements for Queries over
		Continuous Data Streams
		Brian Babcock, Stanford University

4:45-5:15       Distinct Sampling of Streams: Theory and Practice
                Phil Gibbons, Bell Labs

Working Group Presentations:

Communities of Interest Corinna Cortes, AT&T Labs - Research

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Document last modified on April 16, 2002.