Efficient Probabilistic Skyline Query Processing in MapReduce

Ding, Linlin; Wang, Guoren; Xin, Junchang; Yuan, Ye

doi:10.1109/bigdata.congress.2013.35

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…The following corollary shows that the Lemma 3.3 used by PSMR [14] is a special case of our Lemma 4.6 since…”

Section: Upper-bound Filteringmentioning

confidence: 83%

“…Recently, parallel skyline processing algorithms with Map-Reduce for certain and uncertain data were presented in [2,21,27] and [14], respectively. We can develop the parallel algorithms for uncertain data by simply performing one of the algorithms for certain data in [2,21,27] for every possible world.…”

Section: Related Workmentioning

confidence: 99%

“…However, due to a lot of possible worlds, naive extensions of such algorithms to uncertain data are very inefficient and impractical. The most relevant work to ours is the MapReduce algorithm PSMR [14], but PSMR can compute the probabilistic skylines only for the case where each uncertain object has a single instance in the discrete model.…”

Section: Related Workmentioning

confidence: 99%

“…We first introduce the definition of the probabilistic skyline [22] by the popular possible worlds semantics [3,10] and next present the state-of-the-art in [14].…”

Section: Preliminariesmentioning

confidence: 99%

“…Recently, Pei et al [22] proposed serial algorithms for probabilistic skyline queries. Processing probabilistic skyline queries with MapReduce was also considered in [14] for the special case of discrete model in which each object has a single instance.…”

Section: Introductionmentioning

confidence: 99%

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Processing of probabilistic skyline queries using MapReduce

2015

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There has been an increased growth in a number of applications that naturally generate large volumes of uncertain data. By the advent of such applications, the support of advanced analysis queries such as the skyline and its variant operators for big uncertain data has become important. In this paper, we propose the effective parallel algorithms using MapReduce to process the probabilistic skyline queries for uncertain data modeled by both discrete and continuous models. We present three filtering methods to identify probabilistic non-skyline objects in advance. We next develop a single MapReduce phase algorithm PS-QP-MR by utilizing space partitioning based on a variant of quadtrees to distribute the instances of objects effectively and the enhanced algorithm PS-QPF-MR by applying the three filtering methods additionally. We also propose the workload balancing technique to balance the workload of reduce functions based on the number of machines available. Finally, we present the brute-force algorithms PS-BR-MR and PS-BRF-MR with partitioning randomly and applying the filtering methods. In our experiments, we demonstrate the efficiency and scalability of PS-QPF-MR compared to the other algorithms.

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“…The following corollary shows that the Lemma 3.3 used by PSMR [14] is a special case of our Lemma 4.6 since…”

Section: Upper-bound Filteringmentioning

confidence: 83%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…We first introduce the definition of the probabilistic skyline [22] by the popular possible worlds semantics [3,10] and next present the state-of-the-art in [14].…”

Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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