Abstract
Selective search is a distributed retrieval technique that reduces the computational cost of large-scale information retrieval. By partitioning the collection into topical shards, and using a resource selection algorithm to identify a subset of shards to search, selective search allows retrieval effectiveness to be maintained while evaluating fewer postings, often resulting in 90+% reductions in querying cost. However, there has been only limited attention given to the interaction between dynamic pruning algorithms and topical index shards. We demonstrate that the WAND dynamic pruning algorithm is more effective on topical index shards than it is on randomly-organized index shards, and that the savings generated by selective search and WAND are additive. We also compare two methods for applying WAND to topical shards: searching each shard with a separate top-k heap and threshold; and sequentially passing a shared top-k heap and threshold from one shard to the next, in the order established by a resource selection mechanism. Separate top-k heaps provide low query latency, whereas a shared top-k heap provides higher throughput.
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Notes
- 1.
The values for b and \(k_1\) are based on the parameter choices reported for Atire and Lucene in the 2015 IR-Reproducibility Challenge, see http://github.com/lintool/IR-Reproducibility.
- 2.
We recognize that the AOL log has been withdrawn, but also note that it continues to be widely used for research purposes.
References
Aly, R., Hiemstra, D., Demeester, T.: Taily: shard selection using the tail of score distributions. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 673–682 (2013)
Arguello, J., Callan, J., Diaz, F.: Classification-based resource selection. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1277–1286 (2009)
Broder, A.Z., Carmel, D., Herscovici, M., Soffer, A., Zien, J.: Efficient query evaluation using a two-level retrieval process. In: Proceedings of the 12th International Conference on Information and Knowledge Management, pp. 426–434 (2003)
Cacheda, F., Carneiro, V., Plachouras, V., Ounis, I.: Performance comparison of clustered and replicated information retrieval systems. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECIR 2007. LNCS, vol. 4425, pp. 124–135. Springer, Heidelberg (2007)
Cambazoglu, B.B., Varol, E., Kayaaslan, E., Aykanat, C., Baeza-Yates, R.: Query forwarding in geographically distributed search engines. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 90–97 (2010)
Croft, W.B.: A model of cluster searching based on classification. Inf. Syst. 5(3), 189–195 (1980)
Dimopoulos, C., Nepomnyachiy, S., Suel, T.: Optimizing top-\(k\) document retrieval strategies for block-max indexes. In: Proceedings of the of the Sixth ACM International Conference on Web Search and Data Mining, pp. 113–122 (2013)
Gravano, L., García-Molina, H., Tomasic, A.: GlOSS: Text-source discovery over the internet. ACM Trans. Database Syst. 24, 229–264 (1999)
Ipeirotis, P.G., Gravano, L.: Distributed search over the hidden web: Hierarchical database sampling and selection. In: Proceedings of the 28th International Conference on Very Large Data Bases, pp. 394–405 (2002)
Kang, C., Wang, X., Chang, Y., Tseng, B.: Learning to rank with multi-aspect relevance for vertical search. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 453–462 (2012)
Kulkarni, A., Callan, J.: Document allocation policies for selective searching of distributed indexes. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 449–458 (2010)
Kulkarni, A., Callan, J.: Selective search: Efficient and effective search of large textual collections. ACM Trans. Inf. Syst. 33(4), 17:1–17:33 (2015)
Kulkarni, A., Tigelaar, A., Hiemstra, D., Callan, J.: Shard ranking and cutoff estimation for topically partitioned collections. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 555–564 (2012)
Lemire, D., Boytsov, L.: Decoding billions of integers per second through vectorization. Soft. Prac. & Exp. 41(1), 1–29 (2015)
Nottelmann, H., Fuhr, N.: Evaluating different methods of estimating retrieval quality for resource selection. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 290–297. ACM (2003)
Paltoglou, G., Salampasis, M., Satratzemi, M.: Integral based source selection for uncooperative distributed information retrieval environments. In: Proceedings of the 2008 ACM Workshop on Large-Scale Distributed Systems for Information Retrieval, pp. 67–74 (2008)
Petri, M., Culpepper, J.S., Moffat, A.: Exploring the magic of WAND. In: Proceedings of the Australian Document Computing Symposium, pp. 58–65 (2013)
Rojas, O., Gil-Costa, V., Marin, M.: Distributing effciently the block-max WAND algorithm. In: Proceedings of the 2013 International Conference on Computational Science, pp. 120–129 (2013)
Salton, G.: Automatic Information Organization and Retrieval. McGraw-Hill, New York (1968)
Shokouhi, M.: Central-Rank-Based Collection Selection in Uncooperative Distributed Information Retrieval. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECIR 2007. LNCS, vol. 4425, pp. 160–172. Springer, Heidelberg (2007)
Si, L., Callan, J.: Relevant document distribution estimation method for resource selection. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, pp. 298–305 (2003)
Strohman, T., Turtle, H., Croft, W.B.: Optimization strategies for complex queries. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 219–225 (2005)
Thomas, P., Shokouhi, M.: Sushi: Scoring scaled samples for server selection. In: Proceedings of the 32nd ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 419–426 (2009)
Yuwono, B., Lee, D.L.: Server ranking for distributed text retrieval systems on internet. In: Proceedings of the International Conference on Database Systems for Advanced Applications, pp. 41–49 (1997)
Acknowledgments
This research was supported by National Science Foundation (NSF) grant IIS-1302206; a Natural Sciences and Engineering Research Council of Canada (NSERC) Postgraduate Scholarship-Doctoral award; and the Australian Research Council (ARC) under the Discovery Projects scheme (DP140103256). Shane Culpepper is the recipient of an Australian Research Council (ARC) DECRA Research Fellowship (DE140100275).
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Kim, Y., Callan, J., Culpepper, J.S., Moffat, A. (2016). Does Selective Search Benefit from WAND Optimization?. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_11
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DOI: https://doi.org/10.1007/978-3-319-30671-1_11
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