IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Counting Distinct Elements in a Data Stream
RANDOM '02 Proceedings of the 6th International Workshop on Randomization and Approximation Techniques
Discovering and exploiting keyword and attribute-value co-occurrences to improve P2P routing indices
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
On synopses for distinct-value estimation under multiset operations
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Exploiting correlated keywords to improve approximate information filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A scalable and effective full-text search in P2P networks
Proceedings of the 18th ACM conference on Information and knowledge management
KMV-peer: a robust and adaptive peer-selection algorithm
Proceedings of the fourth ACM international conference on Web search and data mining
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A novel method for creating collection summaries is developed, and a fully decentralized peer-selection algorithm is described. This algorithm finds the most promising peers for answering a given query. Specifically, peers publish per-term synopses of their documents. The synopses of a peer for a given term are divided into score intervals and for each interval, a KMV (K Minimal Values) synopsis of its documents is created. The synopses are used to effectively rank peers by their relevance to a multi-term quer. The proposed approach is verified by experiments on a large real-world dataset. In particular, two collections were created from this dataset, each with a different number of peers. Compared to the state-of-the-art approaches, the proposed method is effective and efficient even when documents are randomly distributed among peers