Name
Abstract | ||
|---|---|---|
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 |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1871437.1871682 | CIKM |
Keywords | Field | DocType |
decentralized peer-selection algorithm,novel method,information retrieval,collection summary,multi-term quer,large real-world dataset,different number,per-term synopsis,k minimal values,p2p | Publication,Data mining,Information retrieval,Computer science,Selection algorithm | Conference |
Citations | PageRank | References |
1 | 0.35 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yosi Mass | 1 | 574 | 60.91 |
| Yehoshua Sagiv | 2 | 5362 | 1575.95 |
| Michal Shmueli-Scheuer | 3 | 89 | 16.11 |