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LCJoin: Set containment join via list crosscutting

  • Inception Institute of Artificial Intelligence

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

A set containment join operates on two set-valued attributes with a subset () relationship as the join condition. It has many real-world applications, such as in publish/subscribe services and inclusion dependency discovery. Existing solutions can be broadly classified into union-oriented and intersection-oriented methods. Based on several recent studies, union-oriented methods are not competitive as they involve an expensive subset enumeration step. Intersection-oriented methods build an inverted index on one attribute and perform inverted list intersection on another attribute. Existing intersection-oriented methods intersect inverted lists one-by-one. In contrast, in this paper, we propose to intersect all the inverted lists simultaneously while skipping many irrelevant entries in the lists. To share computation, we utilize the prefix tree structure and extend our novel list intersection method to operate on the prefix tree. To further improve the efficiency, we propose to partition the data and use different methods to process each partition. We evaluated our methods using both real-world and synthetic datasets. Experimental results show that our approach outperforms existing methods by up to 10×.

源语言英语
主期刊名Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
出版商IEEE Computer Society
362-373
页数12
ISBN(电子版)9781538674741
DOI
出版状态已出版 - 4月 2019
已对外发布
活动35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, 中国
期限: 8 4月 201911 4月 2019

出版系列

姓名Proceedings - International Conference on Data Engineering
2019-April
ISSN(印刷版)1084-4627

会议

会议35th IEEE International Conference on Data Engineering, ICDE 2019
国家/地区中国
Macau
时期8/04/1911/04/19

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