Efficient Algorithms of Parallel Skyline Join over Data Streams

Zhang, Jinchao; Gu, Jiahui; Cheng, Shuai; Li, Bo; Wang, Weiping; Meng, Dan

doi:10.1007/978-3-030-05051-1_13

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…Based on the proposed group division approach, Jinchao Zhang et al [24] proposed an efficient algorithm Skyjog, which was applicable for skyline-join on two or even more relations. For the skyline query problem of multiple data streams, Jinchao Zhang et al [21] proposed the Naive Parallel Sliding Window Join (NP-SWJ) and Incremental Parallel Sliding Window Join (IP-SWJ) algorithms. Mei Bai et al [9] proposed the distributed skyline-join query algorithm (DSJQ) to process skyline-join queries in distributed databases.…”

Section: A Skyline-join Query Processingmentioning

confidence: 99%

“…If so, only one parent node is kept to its edge, and all other edges are broken (lines 14-16). When the link attributes between the parent nodes are different, we check whether the tuple is dominated by two or more parent nodes, if so, if the parent nodes have been traversed, the dominant relationship between the parent nodes may be crowdsourced first (lines [17][18][19][20][21][22]. Before each crowdsourcing, it is necessary to check whether the preference relationship of attribute requiring crowdsourcing already exists, and obtain it through crowdsourcing or transitivity.…”

Section: Global Datasets Filteringmentioning

confidence: 99%

“…If it exists, crowdsourcing is only once. If the join properties are inconsistent, the preference relationship between the parents nodes on the unknown properties can be crowdsourced first (lines [14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Before crowdsourcing, it is necessary to check whether the tuple's preference relationship on unknown attributes exists.…”

Section: B Global Dataset Filteringmentioning

confidence: 99%

“…Skyline-join query can return the results from multiple datasets. The issue of skyline-join query has been extensively studied, and the representative works include [9,[20][21][22][23]. Figure 1 illustrates an example of computing the skyline-join over two datasets.…”

Section: Introductionmentioning

confidence: 99%

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CrowdSJ: Skyline-Join Query Processing of Incomplete Datasets With Crowdsourcing

Ding

Zhang

et al. 2021

IEEE Access

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Skyline query is very useful in decision-making systems, WSN and so on. As a variation of skyline query, skyline-join query can return the results from multiple datasets. However, incomplete datasets are a frequent phenomenon due to the widespread use of automated information extraction and aggregation. Existing methods for dealing with incomplete data, such as probability, data padding can solve the problem, but cannot effectively reflect the real situation and are lack of integrality. Therefore, in this paper, in order to reflect the situation more accuracy and more user-centric, we research the problem of skyline-join query over incomplete datasets with crowdsourcing, named CrowdSJ. The crowdsourcingbased skyline-join query processing problem over incomplete datasets is divided into two situations. One is the skyline-join query only involves the unknown crowdsourcing attribute and the join attribute, named Partial Skyline-Join with Crowdsourcing (PSJCrowd). The other one is the skyline-join query involves all the attributes, named All Skyline-Join with Crowdsourcing (ASJCrowd). For PSJCrowd, first, we filter the known dataset. Then, we present the level-preference-tree-index, and propose the partial skyline-join with crowdsourcing algorithm. For ASJCrowd, first, we filter the known dataset too. Second, we build a levelpreference-tree-index based on the known attributes of the incomplete dataset. Third, we propose the skyline-join with crowdsourcing on single dataset algorithm, CrowdSJ-single, to filter the dataset containing unknown attributes. Then, we build a global level-preference-tree-index based on the known attributes of the incomplete dataset and the complete dataset. We propose the skyline-join with crowdsourcing on multiple datasets algorithm, CrowdSJ-multiple. We filter the linked tuples based on the global level-preference-tree-index and the results of each round of crowdsourcing. Numerous experiments on synthetic and real datasets demonstrate that our algorithms are efficient and effective.

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Section: A Skyline-join Query Processingmentioning

confidence: 99%