Efficient Distributed Selective Search

Yubin Kim
Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA

James P. Callan
Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA

Shane Culpepper
School of Computer Science and Information Technology, RMIT University, Victoria 3001, Australia.

Alistair Moffat
Department of Computing and Information Systems, The University of Melbourne, Victoria 3010, Australia.


Information Retrieval Journal, 20(3):221-252, 2017. Parts of this paper appeared in preliminary form in the Proceedings of 2016 ACM SIGIR International Conference on Research and Development in Information Retrieval.


Simulation and analysis have shown that selective search can reduce the cost of large-scale distributed information retrieval. By partitioning the collection into small topical shards, and then using a resource ranking algorithm to choose a subset of shards to search for each query, fewer postings are evaluated. In this paper we extend the study of selective search into new areas using a fine-grained simulation, examining the difference in efficiency when term-based and sample-based resource selection algorithms are used; measuring the effect of two policies for assigning index shards to machines; and exploring the benefits of index-spreading and mirroring as the number of deployed machines is varied. Results obtained for two large datasets and four large query logs confirm that selective search is significantly more efficient than conventional distributed search architectures and can handle higher query rates. Furthermore, we demonstrate that selective search can be tuned to avoid bottlenecks, and thus maximize usage of the underlying computer hardware.

Full text

http://dx.doi.org/10.1007/s10791-016-9290-6 or via author link.