Approximating Aggregation Queries in Peer-to-Peer Networks
Arai, B.; Das, G.; Gunopulos, D.; Kalogeraki, V.
Data Engineering, 2006. ICDE apos;06. Proceedings of the 22nd International Conference on
Volume , Issue , 03-07 April 2006 Page(s): 42 - 42
Digital Object Identifier 10.1109/ICDE.2006.23
Summary: 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
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