Representing and processing lineages over uncertain data based on the Bayesian network

Yue, Kun; Wu, Hao; Liu, Weiyi; Zhu, Yunlei

doi:10.1016/j.asoc.2015.07.047

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…In query optimization, lineage trees can be used to track the lineage of intermediate query results and optimize subsequent queries based on that lineage. The steps to use lineage trees [24] for query optimization are as follows:…”

Section: A Query Optimization Using Lineagementioning

confidence: 99%

Query Optimization in Uncertain and Probabilistic Databases

Kheradkar,

vivek,

Shirgave

2023

Preprint

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Query optimization is a critical aspect of database systems as it helps to reduce query execution time and improve system performance. In this study, Probabilistic object models to get the specific facts from available statistics and efficient query optimization. Query optimization is a technique that considers potential query plans based on lineage in order to determine the most effective way to perform a particular query. Many exceptional components are together used to perform query optimization along with scanner and parser, intermediate shape of query and query optimizer. Based on order of all of the clause and lineage expression tree of query, many query execution plan can be generated, then query optimizer will select efficient query plan for query optimization. The query's ultimate result will be produced based on that strategy. This paper is concentrated on, to analyzed the performance of different query optimization techniques in uncertain and probabilistic databases using the RelationalCross Model, Simple PODM, Optimize PODM, and Optimize Cache PODM. The outcomes demonstrated that in terms of query execution time and system performance, the Optimize Cache PODM strategy performed better than the other techniques. It also identified some challenges and limitations in query optimization for uncertain and probabilistic databases. Overall, this study highlights the importance of query optimization techniques in uncertain and probabilistic databases and provides insights into the effectiveness of different optimization techniques. These findings can help database administrators and developers to make informed decisions when choosing the most suitable query optimization technique for their database system.

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Section: A Query Optimization Using Lineagementioning

confidence: 99%

Query Optimization in Uncertain and Probabilistic Databases

Kheradkar,

vivek,

Shirgave

2023

Preprint

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show abstract

“…In the new sampling space, the Gibbs sampling method will be used. Gibbs sampling [31][32][33] or a Gibbs sampler is a MCMC (Markov chain Monte Carlo) algorithm for obtaining a sequence of observations that are approximated from a specified multivariate probability distribution. Like other MCMC algorithms, Gibbs sampling from Markov chain can be regarded as a special case of the Metropolis-Hastings algorithm; its sampling distribution can be deduced from the properties of the Markov chain and probability transition matrix, and it finally converges to joint distribution.…”

mentioning

confidence: 99%

“…Like other MCMC algorithms, Gibbs sampling from Markov chain can be regarded as a special case of the Metropolis-Hastings algorithm; its sampling distribution can be deduced from the properties of the Markov chain and probability transition matrix, and it finally converges to joint distribution. The name of the algorithm originated from Josiah Willard Gibbs and was proposed by brothers Stewart and Donald Gemman in 1984 [31][32][33]. Gibbs sampling is suitable for multivariate distribution, where conditional distribution is easier to sample than edge distribution.…”

mentioning

confidence: 99%

A Novel Robust Method for Solving CMB Receptor Model Based on Enhanced Sampling Monte Carlo Simulation

et al. 2019

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The traditional effective variance weighted least squares algorithms for solving CMB (Chemical Mass Balance) models have the following drawbacks: When there is collinearity among the sources or the number of species is less than the number of sources, then some negative value of contribution will appear in the results of the source apportionment or the algorithm does not converge to calculation. In this paper, a novel robust algorithm based on enhanced sampling Monte Carlo simulation and effective variance weighted least squares (ESMC-CMB) is proposed, which overcomes the above weaknesses. In the following practical instances for source apportionment, when nine species and nine sources, with no collinearity among them, are selected, EPA-CMB8.2 (U.S. Environmental Protection Agency-CMB8.2), NKCMB1.0 (NanKai University, China-CMB1.0) and ESMC-CMB can obtain similar results. When the source raise dust is added to the source profiles, or nine sources and eight species are selected, EPA-CMB8.2 and NKCMB1.0 cannot solve the model, but the proposed ESMC-CMB algorithm can achieve satisfactory results that fully verify the robustness and effectiveness of ESMC-CMB.

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Assumption Queries Processing of Probabilistic Relational Databases

Zhang

Cui

2018

Communications in Computer and Information Science

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Representing and processing lineages over uncertain data based on the Bayesian network

Cited by 7 publications

References 32 publications

Query Optimization in Uncertain and Probabilistic Databases

Query Optimization in Uncertain and Probabilistic Databases

A Novel Robust Method for Solving CMB Receptor Model Based on Enhanced Sampling Monte Carlo Simulation

Assumption Queries Processing of Probabilistic Relational Databases

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