Abstract
An interesting observation was made that only a few (far shorter than the prefix) low-frequency tokens are enough to help finding similarity pairs for processing top-k set joins. This phenomenon is ubiquitous in all real datasets we have experimented with, covering domains as varied as text, social network, protein sequence data. Possible explanations are discussed. Based on this observation, we propose an algorithm called AEtop-k for processing both approximate and exact top-k similarity join in a unified framework. Comprehensive experiments demonstrate that, compared with the state-of-the-art algorithm on a large collection of real-life datasets, the approximate version of our algorithm can achieve up to 10000\(\times \) speedup with little sacrifice on accuracy and the exact version runs up to 5\(\times \) faster than the existing algorithm.
The work reported in this paper is partially supported by NSFC under grant number 61370205.
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Notes
- 1.
http://dblp.uni-trier.de/db/, a snapshot of the bibliography records from the DBLP web site, contains about 0.9M records (author names + title).
- 2.
http://www.cs.cmu.edu/enron, about 0.25M ENRON emails from about 150 users.
- 3.
http://ftp.acc.umu.se/mirror/wikimedia.org/dumps/, picked from enwiki-*-pages-articles*.xml (title + content).
- 4.
BIBLE is a KJV version bible, and take one verse to one record.
- 5.
PARADISE is from Paradise Lost by the poet John Milton (1608–1674).
- 6.
http://snap.stanford.edu/data, Social circles from Twitter. It take every node as a record and every edge connected by this node as its tokens.
- 7.
https://archive.org/download/stackexchange, Stack Overflow collections.
- 8.
ZIPF is generated by python’s numpy.random.zipf package.
- 9.
- 10.
- 11.
http://www.uniprot.org/downloads, protein sequence datas from the UniProt.
- 12.
A customer’s product views from jd.com’s data-mining contest.
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Sun, C., Wang, H., Xiao, Y., Liu, Z. (2020). A Unified Framework for Processing Exact and Approximate Top-k Set Similarity Join. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham. https://doi.org/10.1007/978-3-030-60290-1_33
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