VLDB2019 paper acceptance

Our paper “Towards a Unified Framework for String Similarity Joins” is accepted by VLDB2019 conference.

One of our paper, “Towards a Unified Framework for String Similarity Joins”, is accepted by the VLDB2019 conference. The topic of this paper is string similarity joins.

Pengfei Xu, Jiaheng Lu "Towards a Unified Framework for String Similarity Joins" PVLDB 2019. 

Access online version: https://www.cs.helsinki.fi/u/jilu/documents/P1131_Lu.pdf

Github implementation download: https://github.com/HY-UDBMS/AU-Join

Abstract  A similarity join aims to find all similar pairs between two collections of records. Established algorithms utilize different similarity measures, either syntactic or semantic, to quantify the similarity between two records. However, when records are similar in forms of a mixture of syntactic and semantic relations, utilizing a single measure becomes inadequate to disclose the real similarity between records, and hence unable to obtain high-quality join results. In this paper, we study a unified framework to find similar records by combining multiple similarity measures. To achieve this goal, we first develop a new similarity framework that unifies the existing three kinds of similarity measures simultaneously, including syntactic (typographic) similarity, synonym-based similarity, and taxonomy-based similarity. We then theoretically prove that finding the maximum unified similarity between two strings is generally NP-hard, and furthermore develop an approximate algorithm which runs in polynomial time with a non-trivial approximation guarantee. To support efficient string joins based on our unified similarity measure, we adopt the filter-and-verification framework and propose a new signature structure, called pebble, which can be simultaneously adapted to handle multiple similarity measures. The salient feature of our approach is that, it can judiciously select the best pebble signatures and the overlap thresholds to maximise the filtering power. Extensive experiments show that our methods are capable of finding similar records having mixed types of similarity relations, while exhibiting high efficiency and scalability for similarity joins.