Browsing by Subject "Query result caching"
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Item Open Access Cost-aware strategies for query result caching in Web search engines(Association for Computing Machinery, 2011) Ozcan, R.; Altingovde, I. S.; Ulusoy, O.Search engines and large-scale IR systems need to cache query results for efficiency and scalability purposes. Static and dynamic caching techniques (as well as their combinations) are employed to effectively cache query results. In this study, we propose cost-aware strategies for static and dynamic caching setups. Our research is motivated by two key observations: (i) query processing costs may significantly vary among different queries, and (ii) the processing cost of a query is not proportional to its popularity (i.e., frequency in the previous logs). The first observation implies that cache misses have different, that is, nonuniform, costs in this context. The latter observation implies that typical caching policies, solely based on query popularity, can not always minimize the total cost. Therefore, we propose to explicitly incorporate the query costs into the caching policies. Simulation results using two large Web crawl datasets and a real query log reveal that the proposed approach improves overall system performance in terms of the average query execution time. © 2011 ACM.Item Open Access A machine learning approach for result caching in web search engines(Elsevier, 2017) Kucukyilmaz T.; Cambazoglu, B. B.; Aykanat, Cevdet; Baeza-Yates R.A commonly used technique for improving search engine performance is result caching. In result caching, precomputed results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access storage. The future occurrences of a query whose results are already stored in the cache can be directly served by the result cache, eliminating the need to process the query using costly computing resources. Although other performance metrics are possible, the main performance metric for evaluating the success of a result cache is hit rate. In this work, we present a machine learning approach to improve the hit rate of a result cache by facilitating a large number of features extracted from search engine query logs. We then apply the proposed machine learning approach to static, dynamic, and static-dynamic caching. Compared to the previous methods in the literature, the proposed approach improves the hit rate of the result cache up to 0.66%, which corresponds to 9.60% of the potential room for improvement. © 2017 Elsevier LtdItem Open Access Static query result caching revisited(ACM, 2008-04) Özcan, Rıfat; Altıngövde, İsmail Şengör; Ulusoy, ÖzgürQuery result caching is an important mechanism for search engine efficiency. In this study, we first review several query features that are used to determine the contents of a static result cache. Next, we introduce a new feature that more accurately represents the popularity of a query by measuring the stability of query frequency over a set of time intervals. Experimental results show that this new feature achieves hit ratios better than those of the previously proposed features.