Reservoir-sampling algorithms of time complexity
 O
 (
 n
 (1 + log(
 N
 /
 n
 )))

Li, Kim-Hung

doi:10.1145/198429.198435

Cited by 55 publications

(22 citation statements)

References 7 publications

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“…To reduce the cost, we have integrated the new model to a sampling-based tool -Suggestion of Locality Optimization (SLO), developed by Beyls and D'Hollander at Ghent University [4]. SLO uses reservoir sampling [14], which has two distinct properties. First, it keeps a bounded number of samples in reservoir, so the collection rate drops as a program execution lengthens.…”

Section: Sampling Analysismentioning

confidence: 99%

A component model of spatial locality

Gu¹,

Christopher

Bai

et al. 2009

Proceedings of the 2009 International Symposium on Memory Management

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Good spatial locality alleviates both the latency and bandwidth problem of memory by boosting the effect of prefetching and improving the utilization of cache. However, conventional definitions of spatial locality are inadequate for a programmer to precisely quantify the quality of a program, to identify causes of poor locality, and to estimate the potential by which spatial locality can be improved.This paper describes a new, component-based model for spatial locality. It is based on measuring the change of reuse distances as a function of the data-block size. It divides spatial locality into components at program and behavior levels. While the base model is costly because it requires the tracking of the locality of every memory access, the overhead can be reduced by using small inputs and by extending a sampling-based tool. The paper presents the result of the analysis for a large set of benchmarks, the cost of the analysis, and the experience of a user study, in which the analysis helped to locate a data-layout problem and improve performance by 7% with a 6-line change in an application with over 2,000 lines.

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Section: Sampling Analysismentioning

confidence: 99%

A component model of spatial locality

Gu¹,

Christopher

Bai

et al. 2009

Proceedings of the 2009 International Symposium on Memory Management

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“…The first reservoir algorithms [26], [22] in the database context maintain an array RS with maximum size s (the target sample size), which is initially empty. The first s tuples are directly added into RS, after which the array becomes full.…”

Section: Reservoir Samplingmentioning

confidence: 99%

Random Sampling for Continuous Streams with Arbitrary Updates

Tao

Lian

Papadias

et al. 2007

IEEE Trans. Knowl. Data Eng.

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Abstract-The existing random sampling methods have at least one of the following disadvantages: they 1) are applicable only to certain update patterns, 2) entail large space overhead, or 3) incur prohibitive maintenance cost. These drawbacks prevent their effective application in stream environments (where a relation is updated by a large volume of insertions and deletions that may arrive in any order), despite the considerable success of random sampling in conventional databases. Motivated by this, we develop several fully dynamic algorithms for obtaining random samples from individual relations, and from the join result of two tables. Our solutions can handle any update pattern with small space and computational overhead. We also present an in-depth analysis that provides valuable insight into the characteristics of alternative sampling strategies and leads to precision guarantees. Extensive experiments validate our theoretical findings and demonstrate the efficiency of our techniques in practice.

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“…This technique ensures that at every moment, the reservoir contains a random sample of the elements seen so far. Vitter and subsequent researchers proposed efficient algorithms to simulate this procedure that instead of checking every element skip over a number of them (see, for example, [21,16]). …”

Section: Estimating the Sampling Probabilitymentioning

confidence: 99%

Sampling search-engine results

Anagnostopoulos

Bröder

Carmel

2005

Proceedings of the 14th International Conference on World Wide Web - WWW '05

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We consider the problem of efficiently sampling Web search engine query results. In turn, using a small random sample instead of the full set of results leads to efficient approximate algorithms for several applications, such as:• Determining the set of categories in a given taxonomy spanned by the search results; • Finding the range of metadata values associated to the result set in order to enable "multi-faceted search;" • Estimating the size of the result set;• Data mining associations to the query terms.We present and analyze an efficient algorithm for obtaining uniform random samples applicable to any search engine based on posting lists and document-at-a-time evaluation. (To our knowledge, all popular Web search engines, e.g. Google, Inktomi, AltaVista, AllTheWeb, belong to this class.) Furthermore, our algorithm can be modified to follow the modern object-oriented approach whereby posting lists are viewed as streams equipped with a next method, and the next method for Boolean and other complex queries is built from the next method for primitive terms. In our case we show how to construct a basic next(p) method that samples term posting lists with probability p, and show how to construct next(p) methods for Boolean operators (AND, OR, WAND) from primitive methods.Finally, we test the efficiency and quality of our approach on both synthetic and real-world data.

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Reservoir-sampling algorithms of time complexity O ( n (1 + log( N / n )))

Cited by 55 publications

References 7 publications

A component model of spatial locality

A component model of spatial locality

Random Sampling for Continuous Streams with Arbitrary Updates

Sampling search-engine results

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