Ranking uncertain sky: The probabilistic top-k skyline operator

Zhang, Ying; Zhang, Wenjie; Lin, Xuemin; Jiang, Bin; Pei, Jian

doi:10.1016/j.is.2011.03.008

Cited by 29 publications

(11 citation statements)

References 19 publications

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“…They used multivariate probability density function in terms of parametric equation. Zhang et al [11] proposed a top-k skyline computational model that addresses the problem of uncertain data, and implemented two efficient methods for data filtering and randomization process.…”

Section: Related Workmentioning

confidence: 99%

A Novel Filtered Based Grid partitioning multiple reducers skyline computation using Hadoop framework

Rao¹,

Hussain²

2018

IJET

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Due to the exponential growth of multi-dimensional skyline objects, the computational memory and efficiency of the traditional skyline measures also increases on large spatial datasets. Skyline query computation has attracted a major research problem recently, due to its exponential time and space complexities over imbalanced datasets. A large number of sequential skyline query processing models have been implemented to evaluate the spatial pattern discovery on limited datasets. It is practically expensive and time consuming process to predict various spatial patterns from a large spatial candidate sets. Another major limitation with the sequential spatial pattern mining models is that a large number of spatial candidate sets are generated with duplicate event sets. In the proposed model, a novel parallel skyline processing using MapReduce framework is implemented on large spatial uncertain datasets. In this model, a filtered based k-nearest neighbor approach is used to eliminate the sparsity or empty patterns using the hadoop framework. Experimental results proved that the proposed model has high computational efficiency in terms of time and candidate sets are concerned.

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Section: Related Workmentioning

confidence: 99%

A Novel Filtered Based Grid partitioning multiple reducers skyline computation using Hadoop framework

Rao¹,

Hussain²

2018

IJET

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“…Moreover, Zhang et al [45] study the probabilistic top-k skyline operator, which retrieves the k uncertain objects with the highest skyline probabilities. It is worth noting that, unlike the aforementioned work, our MDSO operator still adopts the conventional dominance relationship (Definition 1), while it takes the potential weight of non-skyline objects into consideration, which is neglected by the above work.…”

Section: Related Workmentioning

confidence: 99%

Efficient algorithms for finding the most desirable skyline objects

Gao

Liu

Chen

et al. 2015

Knowledge-Based Systems

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“…While [16] solves the case of probabilistic skyline computation with a pre-given threshold, [1] studies the problem of computing skyline probabilities for every object in the uncertain database. In [21], instead of a pre given probability threshold, k uncertain objects from the data set with the highest skyline probabilities are retrieved. Stochastic skyline operators are proposed in [13,19] to retain a minimum set of candidates for all ranking functions in the light of expected utility principles.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic n-of-N skyline computation over uncertain data streams

Zhang

Cheema

et al. 2014

World Wide Web

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Skyline operator is a useful tool in multi-criteria decision making in various applications. Uncertainty is inherent in real applications due to various reasons. In this paper, we consider the problem of efficiently computing probabilistic skylines against the most recent N uncertain elements in a data stream seen so far. Specifically, we study the problem in the nof-N model; that is, computing the probabilistic skyline for the most recent n (∀n≤N) elements, where an element is a probabilistic skyline element if its skyline probability is not below a given probability threshold q. Firstly, an effective pruning technique to minimize the number of uncertain elements to be kept is developed. It can be shown that on average storing only O(log d N) uncertain elements from the most recent N elements is sufficient to support the precise computation of all probabilistic n-of-N skyline queries in a d-dimension space if the data distribution on each dimension is independent. A novel encoding scheme is then proposed together with efficient update techniques so that computing a probabilistic n-of-N skyline query in a d-dimension space is reduced to O(dloglogN+s) if the data distribution is independent, where s is the number of skyline points. A trigger based technique is provided to process continuous n-of-N skyline queries. Extensive experiments demonstrate that the new techniques World Wide Web on uncertain data streams can support on-line probabilistic skyline query computation over rapid data streams.

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Ranking uncertain sky: The probabilistic top-k skyline operator

Cited by 29 publications

References 19 publications

A Novel Filtered Based Grid partitioning multiple reducers skyline computation using Hadoop framework

A Novel Filtered Based Grid partitioning multiple reducers skyline computation using Hadoop framework

Efficient algorithms for finding the most desirable skyline objects

Probabilistic n-of-N skyline computation over uncertain data streams

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