@inproceedings{3cfba763ee1a4759b85fd98a9467ba9c,
title = "LCJoin: Set containment join via list crosscutting",
abstract = "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×.",
keywords = "Approximate query processing, Data integration, Set containment, Similarity join",
author = "Dong Deng and Chengcheng Yang and Shuo Shang and Fan Zhu and Li Liu and Ling Shao",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 35th IEEE International Conference on Data Engineering, ICDE 2019 ; Conference date: 08-04-2019 Through 11-04-2019",
year = "2019",
month = apr,
doi = "10.1109/ICDE.2019.00040",
language = "英语",
series = "Proceedings - International Conference on Data Engineering",
publisher = "IEEE Computer Society",
pages = "362--373",
booktitle = "Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019",
address = "美国",
}