Integrated document caching and prefetching in storage hierarchies based on Markov-chain predictions

Kraiss, Achim; Weikum, Gerhard

doi:10.1007/s007780050060

Cited by 31 publications

(10 citation statements)

References 42 publications

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“…When the number of di erent result pages that may be cached per topic is bounded by a constant, and when PQ is implemented by a heap, the 8 The behavior of the number of views per result page number in the entire log was discussed in Section 2 and Figure 1. amortized complexity of the above procedure is (log C PQ ) per query 9 . See 5] for discussions of the heap data structure and of amortized analysis.…”

Section: If F = 1 and Page (T; 1) Is Not Cached (T; 1) Is Inserted Amentioning

confidence: 99%

Predictive caching and prefetching of query results in search engines

Lempel

Moran

2003

Proceedings of the Twelfth International Conference on World Wide Web - WWW '03

134

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We study the caching of query result pages in Web search engines. Popular search engines receive millions of queries per day, and e cient policies for caching query results may enable them to lower their response time and reduce their hardware requirements. We present PDC (probability driven cache), a novel scheme tailored for caching search results, that is based on a probabilistic model of search engine users. We then use a trace of over seven million queries submitted to the search engine AltaVista to evaluate PDC, as well as traditional LRU and SLRU based caching schemes. The trace driven simulations show that PDC outperforms the other policies. We also examine the prefetching of search results, and demonstrate that prefetching can increase cache hit ratios by 50% for large caches, and can double the hit ratios of small caches. When integrating prefetching into PDC, we attain hit ratios of over 0:53.

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Section: If F = 1 and Page (T; 1) Is Not Cached (T; 1) Is Inserted Amentioning

confidence: 99%

Predictive caching and prefetching of query results in search engines

Lempel

Moran

2003

Proceedings of the Twelfth International Conference on World Wide Web - WWW '03

134

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“…-prefetching in file and database systems [4,34,40,56]: Most of the current work on prefetching documents/objects from databases use the semantic, reference, or access relationships between information units (page, document, or object) to identify clusters. Prefetching is performed using these clusters.…”

Section: Retrieving Query Results For Interactive Results Visualizationmentioning

confidence: 99%

View management in multimedia databases

2000

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Though there has been extensive work on multimedia databases in the last few years, there is no prevailing notion of a multimedia view, nor there are techniques to create, manage, and maintain such views. Visualizing the results of a dynamic multimedia query or materializing a dynamic multimedia view corresponds to assembling and delivering an interactive multimedia presentation in accordance with the visualization specifications. In this paper, we suggest that a non-interactive multimedia presentation is a set of virtual objects with associated spatial and temporal presentation constraints. A virtual object is either an object, or the result of a query. As queries may have different answers at different points in time, scheduling the presentation of such objects is nontrivial. We then develop a probabilistic model of interactive multimedia presentations, extending the non-interactive model described earlier. We also develop a probabilistic model of interactive visualization where the probabilities reflect the user profiles, or the likelihood of certain user interactions. Based on this probabilistic model, we develop three utility-theoretic based types of prefetching algorithms that anticipate how users will interact with the presentation. These prefetching algorithms allow efficient visualization of the query results in accordance with the underlying specification. We have built a prototype system that incorporates these algorithms. We report on the results of experiments conducted on top of this implementation.

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“…We begin by formalizing the notions of queries and topics, introduced in Section 2: a query will refer to an ordered pair (t; k) where t is the topic of the query (the search phrase that initiated the session), and k¿1 is the number of result page requested. For example, a (t; 2) query will denote the second page of results (which typically contains results [11][12][13][14][15][16][17][18][19][20] for the topic t.…”

Section: From the Practical Problem To A Theoretic Modelmentioning

confidence: 99%

Competitive caching of query results in search engines

Lempel

Moran

2004

Theoretical Computer Science

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We study the problem of caching query result pages in Web search engines. Popular search engines receive millions of queries per day, and for each query, return a result page to the user who submitted the query. The user may request additional result pages for the same query, submit a new query, or quit searching altogether. An e cient scheme for caching query result pages may enable search engines to lower their response time and reduce their hardware requirements.This work studies query result caching within the framework of the competitive analysis of algorithms. We deÿne a discrete time stochastic model for the manner in which queries are submitted to search engines by multiple user sessions. We then present an adaptation of a known online paging scheme to this model. The expected number of cache misses of the resulting algorithm is no greater than 4 times the expected number of misses that any online caching algorithm will experience under our speciÿc model of query generation.

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Integrated document caching and prefetching in storage hierarchies based on Markov-chain predictions

Cited by 31 publications

References 42 publications

Predictive caching and prefetching of query results in search engines

Predictive caching and prefetching of query results in search engines

View management in multimedia databases

Competitive caching of query results in search engines

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