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Atlanta, GA, USA
April 3, 2006 to April 7, 2006
ISBN: 0-7695-2570-9
pp: 42
Benjamin Arai , UC Riverside
Gautam Das , UT Arlington
Dimitrios Gunopulos , UC Riverside
Vana Kalogeraki , UC Riverside
Peer-to-peer databases are becoming prevalent on the Internet for distribution and sharing of documents, applications, and other digital media. The problem of answering large scale, ad-hoc analysis queries ― e.g., aggregation queries ― on these databases poses unique challenges. Exact solutions can be time consuming and difficult to implement given the distributed and dynamic nature of peer-to-peer databases. In this paper we present novel sampling-based techniques for approximate answering of ad-hoc aggregation queries in such databases. Computing a high-quality random sample of the database efficiently in the P2P environment is complicated due to several factors ― the data is distributed (usually in uneven quantities) across many peers, within each peer the data is often highly correlated, and moreover, even collecting a random sample of the peers is difficult to accomplish. To counter these problems, we have developed an adaptive two-phase sampling approach, based on random walks of the P2P graph as well as block-level sampling techniques. We present extensive experimental evaluations to demonstrate the feasibility of our proposed solutio
Benjamin Arai, Gautam Das, Dimitrios Gunopulos, Vana Kalogeraki, "Approximating Aggregation Queries in Peer-to-Peer Networks", ICDE, 2006, 22nd International Conference on Data Engineering, 22nd International Conference on Data Engineering 2006, pp. 42, doi:10.1109/ICDE.2006.23
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