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 LabsWorking Group Presentations:
Communities of Interest Corinna Cortes, AT&T Labs - Research