Approximate Aggregations in Structured P2P Networks

In corporate networks, daily business data are generated in gigabytes or even terabytes. It is costly to process aggregate queries in those systems. In this paper, we propose PACA, a probably approximately correct aggregate query processing scheme, for answering aggregate queries in structured Peer-to-Peer (P2P) network. PACA retrieves random samples from peers´ databases and applies the samples to process queries. Instead of scanning the entire database of each peer, PACA only accesses a small random number of data. Moreover, based on the query distribution,PACA publishes a precomputed synopsis and uses the synopsis to answer future queries. Most queries are expected to be answered by the precomputed synopsis partially or fully. And the synopsis is adaptively tuned to follow the query distribution. Experiments on the PlanetLab show the effectiveness of the approach.