Anytime approximation in probabilistic databases

Fink, Robert; Huang, Jiewen; Olteanu, Dan

doi:10.1007/s00778-013-0310-5

Cited by 29 publications

(45 citation statements)

References 58 publications

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“…AAMC and PAAMC relaxations have been used in different contexts in the literature, viz. (Sarkhel et al 2016) and (Fink, Huang, and Olteanu 2013) among others.…”

Section: Probablymentioning

confidence: 94%

“…MAMC relaxations are used in (Fink, Huang, and Olteanu 2013;Wexler and Meek 2008) among others, while PACMC relaxations are used in several recent work, viz. (Zhu and Ermon 2015;Ermon et al 2013;Chakraborty, Meel, and Vardi 2013b;Chakraborty et al 2016).…”

Section: Multiplicatively-approximatementioning

confidence: 99%

“…Hence it is unlikely that efficient algorithms exist for exact probabilistic inference, and various approximations and relaxations are used in practice (viz. (Neal 1993;Sarkhel et al 2016;Fink, Huang, and Olteanu 2013;Wexler and Meek 2008;Gordon et al 2014)). The widespread use of relaxations is premised on the belief that such relaxations offer computational simplicity vis-a-vis exact inference, while being reasonably accurate in practice.…”

Section: Introductionmentioning

confidence: 99%

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On the Hardness of Probabilistic Inference Relaxations

Chakraborty

Meel

Vardi

2019

AAAI

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A promising approach to probabilistic inference that has attracted recent attention exploits its reduction to a set of model counting queries. Since probabilistic inference and model counting are #P-hard, various relaxations are used in practice, with the hope that these relaxations allow efficient computation while also providing rigorous approximation guarantees. In this paper, we show that contrary to common belief, several relaxations used for model counting and its applications (including probablistic inference) do not really lead to computational efficiency in a complexity theoretic sense. Our arguments proceed by showing the corresponding relaxed notions of counting to be computationally hard. We argue that approximate counting with multiplicative tolerance and probabilistic guarantees of correctness is the only class of relaxations that provably simplifies the problem, given access to an NP-oracle. Finally, we show that for applications that compare probability estimates with a threshold, a new notion of relaxation with gaps between low and high thresholds can be used. This new relaxation allows efficient decision making in practice, given access to an NP-oracle, while also bounding the approximation error.

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“…AAMC and PAAMC relaxations have been used in different contexts in the literature, viz. (Sarkhel et al 2016) and (Fink, Huang, and Olteanu 2013) among others.…”

Section: Probablymentioning

confidence: 94%

Section: Multiplicatively-approximatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Hardness of Probabilistic Inference Relaxations

Chakraborty

Meel

Vardi

2019

AAAI

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“…While some top-k approaches support this functionality or can easily be extended to do so, others rely on knowing k for pruning lower-ranked results. In order to more clearly distinguish between them, we will refer to ranked-enumeration algorithms also as "any-k" join algorithms as a shorthand for "anytime top-k. " Despite being reminiscent of the general concept of an anytime algorithm [15,22,32,96], any-k algorithms are not approximating the query result [69]. Instead, they reside squarely at the intersection of top-k and optimal joins, and we will discuss how they are impacted by ideas from both.…”

Section: Part 3: Ranked Enumeration Over Joins ("Any-k")mentioning

confidence: 99%

Optimal Join Algorithms Meet Top-k

Tziavelis

Gatterbauer

Riedewald

2020

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

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Top-k queries have been studied intensively in the database community and they are an important means to reduce query cost when only the "best" or "most interesting" results are needed instead of the full output. While some optimality results exist, e.g., the famous Threshold Algorithm, they hold only in a fairly limited model of computation that does not account for the cost incurred by large intermediate results and hence is not aligned with typical database-optimizer cost models. On the other hand, the idea of avoiding large intermediate results is arguably the main goal of recent work on optimal join algorithms, which uses the standard RAM model of computation to determine algorithm complexity. This research has created a lot of excitement due to its promise of reducing the time complexity of join queries with cycles, but it has mostly focused on full-output computation. We argue that the two areas can and should be studied from a unified point of view in order to achieve optimality in the common model of computation for a very general class of top-k-style join queries. This tutorial has two main objectives. First, we will explore and contrast the main assumptions, concepts, and algorithmic achievements of the two research areas. Second, we will cover recent, as well as some older, approaches that emerged at the intersection to support efficient ranked enumeration of join-query results. These are related to classic work on k-shortest path algorithms and more general optimization problems, some of which dates back to the 1950s. We demonstrate that this line of research warrants renewed attention in the challenging context of ranked enumeration for general join queries.

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“…In probabilistic databases, the MystiQ system supports a limited class of NOT EXISTS queries [25]. A framework for the exact and approximate evaluation of full relational algebra queries (thus including negation) in probabilistic databases is part of SPROUT [13,11]. Further work looks at approximating queries with negation [18].…”

Section: θI|mentioning

confidence: 99%

A dichotomy for non-repeating queries with negation in probabilistic databases

Fink

Olteanu²

2014

Proceedings of the 33rd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems

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This paper shows that any non-repeating conjunctive relational query with negation has either polynomial time or #P-hard data complexity on tuple-independent probabilistic databases. This result extends a dichotomy by Dalvi and Suciu for non-repeating conjunctive queries to queries with negation. The tractable queries with negation are precisely the hierarchical ones and can be recognised efficiently.

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Anytime approximation in probabilistic databases

Cited by 29 publications

References 58 publications

On the Hardness of Probabilistic Inference Relaxations

On the Hardness of Probabilistic Inference Relaxations

Optimal Join Algorithms Meet Top-k

A dichotomy for non-repeating queries with negation in probabilistic databases

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