Abstract
This paper studies the impact of the tail of the query distribution on caches of Web search engines, and proposes a technique for achieving higher hit ratios compared to traditional heuristics such as LRU. The main problem we solve is the one of identifying infrequent queries, which cause a reduction on hit ratio because caching them often does not lead to hits. To mitigate this problem, we introduce a cache management policy that employs an admission policy to prevent infrequent queries from taking space of more frequent queries in the cache. The admission policy uses either stateless features, which depend only on the query, or stateful features based on usage information. The proposed management policy is more general than existing policies for caching of search engine results, and it is fully dynamic. The evaluation results on two different query logs show that our policy achieves higher hit ratios when compared to previously proposed cache management policies.
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References
Denning, P.J.: Virtual memory. ACM Computing Surveys 2, 153–189 (1970)
Baeza-Yates, R., Gionis, A., Junqueira, F., Murdock, V., Plachouras, V., Silvestri, F.: The Impact of Caching on Search Engines. In: Proceedings of the 30th ACM SIGIR Conference, ACM Press, New York (2007)
Markatos, E.P.: On caching search engine query results. Computer Communications 24, 137–143 (2001)
Xie, Y., O’Hallaron, D.R.: Locality in search engine queries and its implications for caching. In: INFOCOM (2002)
Lempel, R., Moran, S.: Predictive caching and prefetching of query results in search engines. In: Proceedings of the 12th WWW Conference, pp. 19–28 (2003)
Fagni, T., Perego, R., Silvestri, F., Orlando, S.: Boosting the performance of Web search engines: Caching and prefetching query results by exploiting historical usage data. ACM Transactions on Information Systems 24, 51–78 (2006)
Megiddo, N., Modha, D.S.: Outperforming LRU with an adaptive replacement cache algorithm. IEEE Computer 37, 58–65 (2004)
Saraiva, P.C., de Moura, E.S., Ziviani, N., Meira, W., Fonseca, R., Riberio-Neto, B.: Rank-preserving two-level caching for scalable search engines. In: Proceedings of the 24th ACM SIGIR Conference, pp. 51–58. ACM Press, New York (2001)
Long, X., Suel, T.: Three-level caching for efficient query processing in large web search engines. In: Proceedings of the 14th WWW Conference, pp. 257–266 (2005)
Sivasubramanian, S., Pierre, G., van Steen, M., Alonso, G.: Analysis of caching and replication strategies for Web applications. IEEE Internet Computing 11, 60–66 (2007)
Olston, C., Manjhi, A., Garrod, C., Ailamaki, A., Maggs, B., Mowry, T.: A scalability service for dynamic Web applications. In: CIDR, Asilomar, California, USA, pp. 56–69 (2005)
Malik, T., Burns, R., Chaudhary, A.: Bypass Caching: Making Scientific Databases Good Network Citizens. In: ICDE, pp. 94–105 (2005)
Brehob, M., Enbody, R.: An analytical model of locality and caching. Technical Report MSU-CSE-99-31, Michigan State University (1999)
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Baeza-Yate, R., Junqueira, F., Plachouras, V., Witschel, H.F. (2007). Admission Policies for Caches of Search Engine Results. In: Ziviani, N., Baeza-Yates, R. (eds) String Processing and Information Retrieval. SPIRE 2007. Lecture Notes in Computer Science, vol 4726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75530-2_7
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DOI: https://doi.org/10.1007/978-3-540-75530-2_7
Publisher Name: Springer, Berlin, Heidelberg
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