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
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
Maintenance
Martin Strauss, AT&T Labs - Research
9:30-10:00 Space-Efficient Algorithms for Maintaining Multi-Dimensional
Histograms
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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