Parallel computation of probabilistic skyline queries using MapReduce

Gavagsaz, Elaheh

doi:10.1007/s11227-020-03279-x

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…A great research effort has been devoted to develop efficient algorithms to skyline computation in both complete and incomplete databases Kalyvas and Maragoudakis (2019), Siddique et al (2019), Hadjali et al (2010), Khalefa et al (2008), Pei et al (2007), Yiu and Mamoulis (2007), Lee and Hwang (2014), Gulzar et al (2017, 2019), Gavagsaz (2021), Ghosh et al (2021). It is worth noticing that when computing the skyline, two scenarios often occur: either (1) a huge number of skyline objects are returned which are less informative for the end users or (2) a small number of skyline objects are retrieved which could be insufficient to serve the user needs.…”

Section: Introductionmentioning

confidence: 99%

Skyline refinement exploiting fuzzy formal concept analysis

Haddache

Hadjali

Asayesh

2021

IJICC

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PurposeThe study of the skyline queries has received considerable attention from several database researchers since the end of 2000's. Skyline queries are an appropriate tool that can help users to make intelligent decisions in the presence of multidimensional data when different, and often contradictory criteria are to be taken into account. Based on the concept of Pareto dominance, the skyline process extracts the most interesting (not dominated in the sense of Pareto) objects from a set of data. Skyline computation methods often lead to a set with a large size which is less informative for the end users and not easy to be exploited. The purpose of this paper is to tackle this problem, known as the large size skyline problem, and propose a solution to deal with it by applying an appropriate refining process.Design/methodology/approachThe problem of the skyline refinement is formalized in the fuzzy formal concept analysis setting. Then, an ideal fuzzy formal concept is computed in the sense of some particular defined criteria. By leveraging the elements of this ideal concept, one can reduce the size of the computed Skyline.FindingsAn appropriate and rational solution is discussed for the problem of interest. Then, a tool, named SkyRef, is developed. Rich experiments are done using this tool on both synthetic and real datasets.Research limitations/implicationsThe authors have conducted experiments on synthetic and some real datasets to show the effectiveness of the proposed approaches. However, thorough experiments on large-scale real datasets are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.Practical implicationsThe tool developed SkyRef can have many domains applications that require decision-making, personalized recommendation and where the size of skyline has to be reduced. In particular, SkyRef can be used in several real-world applications such as economic, security, medicine and services.Social implicationsThis work can be expected in all domains that require decision-making like hotel finder, restaurant recommender, recruitment of candidates, etc.Originality/valueThis study mixes two research fields artificial intelligence (i.e. formal concept analysis) and databases (i.e. skyline queries). The key elements of the solution proposed for the skyline refinement problem are borrowed from the fuzzy formal concept analysis which makes it clearer and rational, semantically speaking. On the other hand, this study opens the door for using the formal concept analysis and its extensions in solving other issues related to skyline queries, such as relaxation.

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Section: Introductionmentioning

confidence: 99%

Skyline refinement exploiting fuzzy formal concept analysis

Haddache

Hadjali

Asayesh

2021

IJICC

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“…Zhang et al [8] proposed a centralized computing framework for deriving the skyline over sliding windows on uncertain data elements against probability thresholds in real-time. Gavagsaz [9] proposed a parallel skyline processing framework based on MapReduce system for processing probabilistic skyline queries, but this work did not support streaming computing for real-time monitoring.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Skyline Query Processing over Uncertain Data Streams in Edge Computing Environments

Lai,

Liu,

Chen

et al. 2020

Preprint

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With the advancement of technology, the data generated in our lives is getting faster and faster, and the amount of data that various applications need to process becomes extremely huge. Therefore, we need to put more effort into analyzing data and extracting valuable information. Cloud computing used to be a good technology to solve a large number of data analysis problems. However, in the era of the popularity of the Internet of Things (IoT), transmitting sensing data back to the cloud for centralized data analysis will consume a lot of wireless communication and network transmission costs. To solve the above problems, edge computing has become a promising solution. In this paper, we propose a new algorithm for processing probabilistic skyline queries over uncertain data streams in an edge computing environment. We use the concept of a second skyline set to filter data that is unlikely to be the result of the skyline. Besides, the edge server only sends the information needed to update the global analysis results on the cloud server, which will greatly reduce the amount of data transmitted over the network. The results show that our proposed method not only reduces the response time by more than 50% compared with the brute force method on two-dimensional data but also maintains the leading processing speed on high-dimensional data.

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Weighted spatial skyline queries with distributed dominance tests

Gavagsaz

2022

Cluster Comput

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Parallel computation of probabilistic skyline queries using MapReduce

Cited by 5 publications

References 38 publications

Skyline refinement exploiting fuzzy formal concept analysis

Skyline refinement exploiting fuzzy formal concept analysis

Probabilistic Skyline Query Processing over Uncertain Data Streams in Edge Computing Environments

Weighted spatial skyline queries with distributed dominance tests

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