Efficient computation for probabilistic skyline over uncertain preferences

Pujari, Arun K.; Kagita, Venkateswara Rao; Garg, Anubhuti; Padmanabhan, Vineet

doi:10.1016/j.ins.2015.06.041

Cited by 17 publications

(35 citation statements)

References 15 publications

(37 reference statements)

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“…Top-k skyline [12][13][14][15][16] will find the points ranking in the top k position, this algorithm was only suitable for the query within a set limit in volume. In recent years, the skyline query on uncertain data has been studied, in this query, a threshold q was given at first, then the points whose probabilities are larger than q would be returned as probabilistic skyline [17][18][19]. Paper [20,21] introduced the MapReduce technology to compute the skyline efficiently.…”

Section: Related Workmentioning

confidence: 99%

Finding Group-Based Skyline over a Data Stream in the Sensor Network

Dong

Cui

et al. 2018

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Abstract:Along with the application of the sensor network, there will be large amount of dynamic data coming from sensors. How to dig the useful information from such data is significant. Skyline query is aiming to identify the interesting points from a large dataset. The group-based skyline query is to find the outstanding Pareto Optimal groups which cannot be g-dominated by any other groups with the group same size. However, the existing algorithms of group-based skyline (G-Skyline) focus on the static data set, how to conduct advanced research on data stream remains an open problem at large. In this paper, we propose the group-based skyline query over the data stream. In order to compute G-Skyline efficiently, we present a sharing strategy, and based on which we propose two algorithms to efficiently compute the G-Skyline over the data stream: the point-arriving algorithm and the point-expiring algorithm. In our experiments, three synthetic data sets are used to test our algorithms; the experiments results show that our algorithms perform efficiently over a data stream.

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Section: Related Workmentioning

confidence: 99%

Finding Group-Based Skyline over a Data Stream in the Sensor Network

Dong

Cui

et al. 2018

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“…To the best of our knowledge, only a few studies investigate the problem of skyline probability computation over uncertain preferences . Sacharidis et al is the first paper to address the problem.…”

Section: Related Workmentioning

confidence: 99%

“…In order to accelerate computation, they propose an absorption preprocessing step to prune redundant terms in the power set. AK Pujari et al find the absorption preprocessing step proposed in Zhang et al is not sufficient to remove all redundant terms, thus the Det+ algorithm proposed in Zhang et al is not efficient to compute exact skyline probability for data sets of large size. Therefore, they extend base‐level absorption proposed in Zhang et al to prefix‐based k‐level absorption .…”

Section: Related Workmentioning

confidence: 99%

“…The skyline probability of a restaurant is then the probability that no other restaurant is preferred to this one with respect to all attributes. It is a meaningful job to identify restaurants with high skyline probability …”

Section: Introductionmentioning

confidence: 99%

“…Note that this study needs to be perfected from 2 aspects of theoretic analysis and efficiency. Motivated by these concerns, we make the key contributions as follows: We analyse the incompleteness of the lemmas that the prefix‐based k‐level absorption based on.…”

Section: Introductionmentioning

confidence: 99%

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Parallelization of skyline probability computation over uncertain preferences

Zhu

et al. 2017

Concurrency and Computation

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Summary Query processing over uncertain preferences is very common in real‐life situations, because many times, we cannot model users' preferences as strict partial orders. In this paper, we investigate skyline queries over uncertain preferences. The latest state‐of‐the‐art algorithm, called Usky‐base algorithm, makes significant advances. However, it still needs to be perfected in 2 aspects. (1) Theoretic analysis: The correctness of the algorithm is not fully verified. (2) Efficiency: Due to the heavy calculation introduced by adopting inclusion‐exclusion principle to express the skyline probability, it needs massive time when computing skyline probabilities for large data sets. To address the above 2 concerns, we first review the Usky‐base algorithm and lemmas it based on. Then we propose a novel parallel algorithm, called Parallel‐sky, to compute skyline probability of a given object. Moreover, we propose an adding algorithm and a deleting algorithm to deal with dynamic scenarios where new objects are added in and outdated objects are deleted out. Furthermore, we extend our algorithm from computing skyline probability of a given object to all objects in a data set. We conduct extensive experiments on real and synthetic data sets to validate the effectiveness and efficiency of our proposals.

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Query Processing over Incomplete Databases

Gao

Miao

2018

Synthesis Lectures on Data Management

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Efficient computation for probabilistic skyline over uncertain preferences

Cited by 17 publications

References 15 publications

Finding Group-Based Skyline over a Data Stream in the Sensor Network

Finding Group-Based Skyline over a Data Stream in the Sensor Network

Parallelization of skyline probability computation over uncertain preferences

Query Processing over Incomplete Databases

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