Robust and efficient aggregate query processing in peer-to-peer networks

The ability to approximately answer aggregation queries accurately and efficiently is of great benefit for decision support and data mining tools. A P2P network consists of numerous peer nodes that share data and resources with other peers on an equal basis. Unlike traditional client-server models, no central coordination exists in a P2P system; thus, there is no central point of failure. P2P networks are scalable, fault tolerant, and dynamic, and nodes can join and depart the network with ease. The most compelling applications on P2P systems to date have been file sharing and retrieval. Millions of peers across the world may be cooperating on a grand experiment in astronomy, and astronomers may be interested in asking decision support queries that require the aggregation of vast amounts of data covering thousands of peers. In addition, there is real-world value for aggregation queries in network monitoring scenarios such as temperature and anomaly detection in sensor networks, Intrusion Detection Systems, and application signature analysis in P2P networks. A common characteristic of these applications is that they execute aggregation queries on large databases, which can often be expensive and resource intensive. Therefore, the ability to obtain approximate answers to such queries accurately and efficiently can greatly benefit the scalability of these applications. One approach to address this problem is to use precomputed samples of the data instead of the complete data to answer the queries. While this approach can give approximate answers very efficiently, it is easy to see that identifying an appropriate precomputed sample that avoids large errors on an arbitrary query is virtually impossible. In seeking a random sample of the P2P database, this algorithm will visit a predefined number of peers m by using a random walk such that the sample of visited peers will appear as if they have been drawn from the stationary distribution of the graph.