Space efficient mining of multigraph streams

The challenge of monitoring massive amounts of data generated by communication networks has led to the interest in data stream processing. We study streams of edges in massive communication multigraphs, defined by (source, destination) pairs. The goal is to compute properties of the underlying graph while using small space (much smaller than the number of communicants), and to avoid bias introduced because some edges may appear many times, while others are seen only once. We give results for three fundamental problems on multigraph degree sequences: estimating frequency moments of degrees, finding the heavy hitter degrees, and computing range sums of degree values. In all cases we are able to show space bounds for our summarizing algorithms that are significantly smaller than storing complete information. We use a variety of data stream methods: sketches, sampling, hashing and distinct counting, but a common feature is that we use cascaded summaries: nesting multiple estimation techni...

• Type: Conference
• Year: 2005
• Where: PODS
• Authors: Graham Cormode, S. Muthukrishnan
• Keywords: Database, Heavy Hitter Degrees, Massive Communication Multigraphs, Massive Multigraph Streams, PODS 2005