Shard Ranking and Cutoff Estimation for Topically Partitioned Collections

by Anagha Kulkarni, Almer Tigelaar, Djoerd Hiemstra, and Jamie Callan

Large document collections can be partitioned into topical shards to facilitate distributed search. In a low-resource search environment only a few of the shards can be searched in parallel. Such a search environment faces two intertwined challenges. First, determining which shards to consult for a given query: shard ranking. Second, how many shards to consult from the ranking: cutoff estimation. In this paper we present a family of three algorithms that address both of these problems. As a basis we employ a commonly used data structure, the central sample index (CSI), to represent the shard contents. Running a query against the CSI yields a flat document ranking that each of our algorithms transforms into a tree structure. A bottom up traversal of the tree is used to infer a ranking of shards and also to estimate a stopping point in this ranking that yields cost-effective selective distributed search. As compared to a state-of-the-art shard ranking approach the proposed algorithms provide substantially higher search efficiency while providing comparable search effectiveness.

to be presented at the The 21st ACM International Conference on Information and Knowledge Management, CIKM 2012.

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