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المؤلفTang, Mingjie
المؤلفYu, Yongyang
المؤلفAref, Walid G.
المؤلفMalluhi, Qutaibah M.
المؤلفOuzzani, Mourad
تاريخ الإتاحة2024-07-17T07:14:43Z
تاريخ النشر2019
اسم المنشورProceedings - International Conference on Data Engineering
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/ICDE.2019.00251
الرقم المعياري الدولي للكتاب10844627
معرّف المصادر الموحدhttp://hdl.handle.net/10576/56745
الملخصGiven a set of multidimensional data points, skyline queries retrieve those points that are not dominated by any other points in the set. Due to the ubiquitous use of skyline queries, such as in preference-based query answering and decision making, and the large amount of data that these queries have to deal with, enabling their scalable processing is of critical importance. However, there are several outstanding challenges that have not been well addressed. More specifically, in this paper, we are tackling the data straggler and data skew challenges introduced by distributed skyline query processing, as well as the ensuing high computation cost of merging skyline candidates. We thus introduce a new efficient three-phase approach for large scale processing of skyline queries. In the first preprocessing phase, the data is partitioned along the Z-order curve. We utilize a novel data partitioning approach that formulates data partitioning as an optimization problem to minimize the size of intermediate data. In the second phase, each compute node partitions the input data points into disjoint subsets, and then performs the skyline computation on each subset to produce skyline candidates in parallel. In the final phase, we build an index and employ an efficient algorithm to merge the generated skyline candidates. Extensive experiments demonstrate that the proposed skyline algorithm achieves more than one order of magnitude enhancement in performance compared to existing state-of-the-art approaches.
راعي المشروعIII. EXPERIMENTAL RESULTS We evaluate the performance of the proposed techniques using synthetic and real-word data. We use a Hadoop cluster consisting of 6 computing nodes, and setup one Hadoop virtual machine cluster (version 2.6) based on the the Amazon EC2 with 48 nodes, each node has an Intel Xeon E5-2666 v3 (Haswell) and 8GB of memory. Figure 2 shows the proposed approaches can achieve up to one order of magnitude speedup over existing state-of-the-art approach IV. CONCLUSIONS This paper demonstrates an efficient solution for the problem of parallel skyline query processing. Part of this work is supported by the National Natural Science Foundation of China (Grant No. 61802364).
اللغةen
الناشرIEEE Computer Society
الموضوعBig data; Parallel computation; Skyline query
العنوانEfficient parallel skyline query processing for high-dimensional data
النوعConference Paper
الصفحات2113-2114
رقم المجلد2019-April
dc.accessType Abstract Only


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