SPROUT
            <sup>2</sup>

Fink, Robert; Hogue, Andrew; Olteanu, Dan; Rath, Swaroop

doi:10.1145/1989323.1989481

Cited by 8 publications

(1 citation statement)

References 3 publications

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“…Prime examples of TI databases are the Google Knowledge Vault [8] and NELL (Never Ending Language Learner, http://rtw.ml.cmu.edu/rtw/) knowledge bases, of BID databases are the Google Squared tables [10], and of PC databases are the answers and their lineage for relational queries on probabilistic databases in any of the above formalisms.…”

Section: Scientific Fundamentals Representation Of Uncertain Datamentioning

confidence: 99%

Query Processing over Uncertain Data

Dalvi¹,

Olteanu²

2018

Encyclopedia of Database Systems

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An uncertain or probabilistic database is defined as a probability distribution over a set of deterministic database instances called possible worlds.In the classical deterministic setting, the query processing problem is to compute the set of tuples representing the answer of a given query on a given database. In the probabilistic setting, this problem becomes the computation of all pairs (t, p), where the tuple t is in the query answer in some random world of the input probabilistic database with probability p.

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Section: Scientific Fundamentals Representation Of Uncertain Datamentioning

confidence: 99%

Query Processing over Uncertain Data

Dalvi¹,

Olteanu²

2018

Encyclopedia of Database Systems

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Anytime approximation in probabilistic databases

2013

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This article describes an approximation algorithm for computing the probability of propositional formulas over discrete random variables. It incrementally refines lower and upper bounds on the probability of the formulas until the desired absolute or relative error guarantee is reached. This algorithm is used by the SPROUT query engine to approximate the probabilities of results to relational algebra queries on expressive probabilistic databases.

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Efficient and scalable monitoring and summarization of large probabilistic data

Tang

2013

Proceedings of the 2013 SIGMOD/PODS Ph.D. Symposium

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In numerous real applications, uncertainty is inherently introduced when massive data are generated. Modern database management systems aim to incorporate and handle data with uncertainties as a first-class citizen, where uncertain data are represented as probabilistic relations. In my thesis, my work has focused on monitoring and summarization of large probabilistic data. Specifically, we extended the distributed threshold monitoring problem to distributed probabilistic data. Instead, we actually need to monitor the aggregated value (e.g. sum) of distributed probabilistic data against both the score threshold and the probability threshold, which make the techniques designed for deterministic data are not directly applicable. Our algorithms have significantly reduced both the communication and computation costs as shown by an extensive experimental evaluation on large real datasets. On the other hand, building histograms to summarize the distribution of certain feature in a large data set is a fundamental problem in data management. Recent work have extended this studies to probabilistic data [6, 7] but their methods suffer from the limited scalability. We present novel methods to build scalable histograms over large probabilistic data using distributed and parallel algorithms. Extensive experiments on large real data sets have demonstrated the superb scalability and efficiency achieved by our implementations in MapReduce, when compared to the existing, state-of-the-art centralized methods.

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SPROUT ²

Cited by 8 publications

References 3 publications

Query Processing over Uncertain Data

Query Processing over Uncertain Data

Anytime approximation in probabilistic databases

Efficient and scalable monitoring and summarization of large probabilistic data

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SPROUT 2

Cited by 8 publications

References 3 publications

Query Processing over Uncertain Data

Query Processing over Uncertain Data

Anytime approximation in probabilistic databases

Efficient and scalable monitoring and summarization of large probabilistic data

Contact Info

Product

Resources

About

SPROUT ²