Incremental cluster-based retrieval using compressed cluster-skipping inverted files
Altingovde, I. S.
ACM Transactions on Information Systems
Association for Computing Machinery
15:1 - 15:36
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We propose a unique cluster-based retrieval (CBR) strategy using a new cluster-skipping inverted file for improving query processing efficiency. The new inverted file incorporates cluster membership and centroid information along with the usual document information into a single structure. In our incremental-CBR strategy, during query evaluation, both best(-matching) clusters and the best(-matching) documents of such clusters are computed together with a single posting-list access per query term. As we switch from term to term, the best clusters are recomputed and can dynamically change. During query-document matching, only relevant portions of the posting lists corresponding to the best clusters are considered and the rest are skipped. The proposed approach is essentially tailored for environments where inverted files are compressed, and provides substantial efficiency improvement while yielding comparable, or sometimes better, effectiveness figures. Our experiments with various collections show that the incremental-CBR strategy using a compressed cluster-skipping inverted file significantly improves CPU time efficiency, regardless of query length. The new compressed inverted file imposes an acceptable storage overhead in comparison to a typical inverted file. We also show that our approach scales well with the collection size. © 2008 ACM.
Cluster-based retrieval (CBR)
Cluster-skipping inverted index structure (CS-IIS)
Full search (FS)
Inverted index structure (IIS)
CPU time efficiency
Data storage equipment
Information retrieval systems