Databases for interval probabilities

Zhao, Wei; Dekhtyar, Alex; Goldsmith, Judy

doi:10.1002/int.20025

Cited by 19 publications

(15 citation statements)

References 17 publications

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“…They develop an algebra to query databases of such SPOs and a prototype implementation. In Zhao et al 2003Zhao et al , 2004, they extended their work to handle interval probabilities. Here, we do not develop an algebra; the query language described in this paper is a very simple logical one.…”

Section: Algorithm To Solve Dmimo Queriesmentioning

confidence: 97%

Probabilistic interval XML

Hung

Getoor

Subrahmanian

2007

ACM Trans. Comput. Logic

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Interest in XML databases has been expanding rapidly over the last few years. In this paper, we study the problem of incorporating probabilistic information into XML databases. We propose the Probabilistic Interval XML (PIXML for short) data model in this paper. Using this data model, users can express probabilistic information within XML markups. In addition, we provide two alternative formal model-theoretic semantics for PIXML data. The first semantics is a "global" semantics which is relatively intuitive, but is not directly amenable to computation. The second semantics is a "local" semantics which supports efficient computation. We prove several correspondence results between the two semantics. To our knowledge, this is the first formal model theoretic semantics for probabilistic interval XML. We then provide an operational semantics that may be used to compute answers to queries and that is correct for a large class of probabilistic instances.

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Section: Algorithm To Solve Dmimo Queriesmentioning

confidence: 97%

Probabilistic interval XML

Hung

Getoor

Subrahmanian

2007

ACM Trans. Comput. Logic

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“…in [1,17,20], and a large number of PRDB models have been proposed, e.g. in [2,5,6,9,10,14,16,22,24,27], respectively for representing and handling uncertain and imprecise information. However, no model would be so universal that could include all measures and tackle all facets of uncertain and imprecise information.…”

Section: Introductionmentioning

confidence: 99%

“…For instances, in [2,6,9,13,15], the value of an attribute was assigned to a probability to represent the uncertain level for that attribute to take the value. The models in [22,27] allowed the value of each attribute associated with a probability interval to represent the uncertain degree of both the probability and the value that the attribute could take. More flexibly, the model in [7] represented the value of each attribute as a probability distribution on a set.…”

Section: Introductionmentioning

confidence: 99%

“…Particularly, regarding the models in [2,6,9,10,13,15,26], the probability associated with each tuple or each attribute value is not always determined exactly in practice. The models in [7,18,19,22,27] overcame the shortcoming of the models in [2,6,9,13,15] by estimating a probability interval or a pair of lower and upper bound probabilities for each attribute value of relations. However, in [7,18,19,22,27], the uncertain degree of each tuple in a relation was not represented.…”

Section: Introductionmentioning

confidence: 99%

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Extending Relational Database Model for Uncertain Information

Nguyen¹

2019

JCC

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In this paper, we propose a new probabilistic relational database model, denote by PRDB, as an extension of the classical relational database model where the uncertainty of relational attribute values and tuples are respectively represented by finite sets and probability intervals. A probabilistic interpretation of binary relations on finite sets is proposed for the computation of their probability measures. The combination strategies on probability intervals are employed to combine attribute values and compute uncertain membership degrees of tuples in a relation. The fundamental concepts of the classical relational database model are extended and generalized for PRDB. Then, the probabilistic relational algebraic operations are formally defined accordingly in PRDB. In addition, a set of the properties of the algebraic operations in this new model also are formulated and proven.

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“…The semantics is that any probability distribution consistent with the intervals is possible. Modeling uncertainty of the probabilities has been done for probabilistic databases [16,21] and XML [9,22]. In all prior work the dependencies considered are so basic that confidence computation becomes easy.…”

Section: Introductionmentioning

confidence: 99%

A compositional framework for complex queries over uncertain data

Götz

2009

Proceedings of the 12th International Conference on Database Theory

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The ability to flexibly compose confidence computation with the operations of relational algebra is an important feature of probabilistic database query languages. Computing confidences is computationally hard, however, and has to be approximated in practice. In a compositional query language, even very small errors caused by approximation can lead to an entirely incorrect result: A selection operation on an approximated probability can incorrectly keep or drop a tuple even if the probability value has been approximated to a very narrow confidence interval.In this paper, we study the query evaluation problem for compositional query languages for probabilistic databases with particular focus on providing overall result quality guarantees in the face of approximate intermediate results. We present a framework for evaluating compositional queries based on a new representation system that can capture uncertainty about probabilities. More specifically, we consider probability intervals instead of exact probabilities, interpreting tuples obtained by selection on approximate values as unreliable.We study the complexity of query evaluation over our new model. We present efficient confidence computation algorithms which compute bounds that are close to tight for important classes. For deciding a selection predicate, we show that no efficient randomized algorithm exists unless BPP⊃NP. Still we are able to efficiently guess robust predicates with a good error bound. Putting all these pieces together in our framework, we evaluate queries using a decomposition into a relational algebra plan and an approximation plan. The latter allows to successively improve accuracy and error bounds, while the relational algebra plan only has to be executed once.

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Databases for interval probabilities

Cited by 19 publications

References 17 publications

Probabilistic interval XML

Probabilistic interval XML

Extending Relational Database Model for Uncertain Information

A compositional framework for complex queries over uncertain data

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