Semantics and evaluation of top-k queries in probabilistic databases

Zhang, Xi; Chomicki, Jan

doi:10.1007/s10619-009-7050-y

Cited by 66 publications

(71 citation statements)

References 23 publications

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“…Contrasting the properties of different semantics of ω * , and studying their potential applications have been addressed in recent works [7,22,24,26]. Our focus in this paper, however, is building an infrastructure that incrementally computes both J k and ω * under multiple semantics in the context of URANKJOIN queries.…”

Section: Problem Definitionmentioning

confidence: 99%

Building ranked mashups of unstructured sources with uncertain information

2010

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Mashups are situational applications that join multiple sources to better meet the information needs of Web users. Web sources can be huge databases behind query interfaces, which triggers the need of ranking mashup results based on some user preferences.We present MashRank, a mashup authoring and processing system building on concepts from rank-aware processing, probabilistic databases, and information extraction to enable ranked mashups of (unstructured) sources with uncertain ranking attributes. MashRank is based on new semantics, formulations and processing techniques to handle uncertain preference scores, represented as intervals enclosing possible score values.MashRank integrates information extraction with query processing by asynchronously pushing extracted data on-the-fly into pipelined rank-aware query plans, and using ranking early-out requirements to limit extraction cost. To the best of our knowledge, both the technical problems and target applications of MashRank have not been addressed before.

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Section: Problem Definitionmentioning

confidence: 99%

Building ranked mashups of unstructured sources with uncertain information

2010

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“…Zhang and Chomicki developed the global top-k semantics on uncertain data which returns k tuples having the largest probability in the top-k list, and gave a dynamic programming algorithm [56].…”

Section: Top-k Queries On Uncertain Datamentioning

confidence: 99%

“…Recently, there have been a few interesting studies on ranking queries on static uncertain data using the possible world semantics [30,31,40,49,54,56]. Among them, probabilistic threshold top-k queries [30,31] compute uncertain records taking a probability of at least p to be in the top-k list where p is a user specified probability threshold.…”

Section: Introductionmentioning

confidence: 99%

Continuously monitoring top-k uncertain data streams: a probabilistic threshold method

Hua

Pei

2009

Distrib Parallel Databases

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Recently, uncertain data processing has become more and more important. Although a significant amount of previous research explores various continuous queries on data streams, continuous queries on uncertain data streams have seldom been investigated. In this paper, we formulate a novel and challenging problem of continuously monitoring top-k uncertain data streams, and propose a probabilistic threshold method. We develop four algorithms systematically: a deterministic exact algorithm, a randomized method, and their space-efficient versions using quantile summaries. An extensive empirical study using real data sets and synthetic data sets is reported to verify the effectiveness and the efficiency of our methods.

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“…Also the Expected rank query satisfy all these properties [6], the expected value always leads to information loss. The Global-topk [8] query returns k highest-ranked tuples according to their probability of being in the top-k answers in possible worlds. It satisfies all properties except 'Containment'.…”

Section: Introductionmentioning

confidence: 99%

Top (k1,k2) Query in Uncertain Datasets

Liu

Lin

Jia

2015

IEICE Trans. Inf. & Syst.

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SUMMARYIn this letter, we propose a novel kind of uncertain query, top (k 1 , k 2 ) query. The x-tuple model and the possible world semantics are used to describe data objects in uncertain datasets. The top (k 1 , k 2 ) query is going to find k 2 x-tuples with largest probabilities to be the result of top k 1 query in a possible world. Firstly, we design a basic algorithm for top (k 1 , k 2 ) query based on dynamic programming. And then some pruning strategies are designed to improve its efficiency. An improved initialization method is proposed for further acceleration. Experiments in real and synthetic datasets prove the performance of our methods. key words: uncertain query, top k, x-tuple, possible world

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Semantics and evaluation of top-k queries in probabilistic databases

Cited by 66 publications

References 23 publications

Building ranked mashups of unstructured sources with uncertain information

Building ranked mashups of unstructured sources with uncertain information

Continuously monitoring top-k uncertain data streams: a probabilistic threshold method

Top (<i>k</i><sub>1</sub>,<i>k</i><sub>2</sub>) Query in Uncertain Datasets

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