A Framework for Set Similarity Join on Multi-Attribute Data

  • Leonardo Andrade Ribeiro Universidade Federal de Goiás
  • Felipe Ferreira Borges Universidade Federal de Goiás
  • Diego Junior do Carmo Oliveira Universidade Federal de Goiás


Set similarity join, which finds all pairs of similar sets in a collection, plays an important role in data cleaning and integration. Many algorithms have been proposed to efficiently answer set similarity join on single-attribute data. However, real-world data often contain multiple attributes. In this paper, we propose a framework to enhance existing algorithms with additional filters for dealing with multi-attribute data. We then present a simple, yet effective filter based on lightweight indexes, for which exact and probabilistic implementation alternatives are evaluated. Finally, we devise a cost model to identify the best attribute ordering to reduce processing time. Our experimental results show that our approach is effective and significantly outperforms previous work.

Palavras-chave: advanced query processing, data cleaning, data integration, set similarity join


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RIBEIRO, Leonardo Andrade; BORGES, Felipe Ferreira; OLIVEIRA, Diego Junior do Carmo. A Framework for Set Similarity Join on Multi-Attribute Data. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 35. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 61-72. ISSN 2763-8979. DOI: https://doi.org/10.5753/sbbd.2020.13625.