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The SimilarQL framework: similarity queries in plain SQL

Published: 08 April 2019 Publication History

Abstract

As the variety and complexity of collected data increases, also does the need to analyze them by similarity. However, current Database Management Systems (DBMS) do not provide effective support for similarity queries, and the research on the subject has, until now, provided only a limited support through tools that are not simple to be deployed nor maintained. Thus, what to do if you need to perform a quick exploratory analysis over your data, and must do it 'now'? In this paper we show how to use the readily available support from standard DBMS to target this issue, and present the simple, yet powerful SimilarQL framework, which provides a complete and flexible set of similarity query operators. It can be readily deployed over conventional DBMS, without depending on the bulk data structures and software systems required to handle long-term standing similarity queries. We present results from a real-world dataset regarding the query execution times using SimilarQL, and show that acceptable runtimes may be achieved, while intuitively and easily exploring complex data by similarity.

References

[1]
Nikolaus Augsten. 2018. A Roadmap towards Declarative Similarity Queries. In 21th EDBT 2018. OpenProceedings.org, Vienna, Austria, 509--512.
[2]
Maria Camila Nardini Barioni, Humberto Luís Razente, Agma Juci Machado Traina, and Caetano Traina Jr. 2009. Seamlessly Integrating Similarity Queries in SQL. Software: Practice and Experience 39, 4 (2009), 355--384.
[3]
Wei Lu, Jiajia Hou, Ying Yan, Meihui Zhang, Xiaoyong Du, and Thomas Moscibroda. 2017. MSQL: efficient similarity search in metric spaces using SQL. VLDB J. 26, 6 (2017), 829--854.
[4]
Yasin N. Silva, Ahmed M. Aly, Walid G. Aref, and Per-Ake Larson. 2010. SimDB: a similarity-aware database system. In Proceedings of the 2010 international conference on Management of data. ACM, Indianapolis, Indiana, USA, 1243--1246.

Cited By

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  • (2021)Similarity vs. Relevance: From Simple Searches to Complex DiscoverySimilarity Search and Applications10.1007/978-3-030-89657-7_9(104-117)Online publication date: 29-Sep-2021
  • (2020)A Comparison of Two Database Partitioning Approaches that Support Taxonomy-Based Query AnsweringProceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services10.1145/3428757.3429108(426-435)Online publication date: 30-Nov-2020

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cover image ACM Conferences
SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing
April 2019
2682 pages
ISBN:9781450359337
DOI:10.1145/3297280
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2019

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Author Tags

  1. SQL
  2. complex data
  3. exploratory data analysis
  4. multidimensional proximity search
  5. relational database systems
  6. similarity search

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Funding Sources

  • Coordination for Improvement of Higher Education Personnel (CAPES)
  • Sao Paulo Research Foundation (FAPESP)
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico

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SAC '19
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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2021)Similarity vs. Relevance: From Simple Searches to Complex DiscoverySimilarity Search and Applications10.1007/978-3-030-89657-7_9(104-117)Online publication date: 29-Sep-2021
  • (2020)A Comparison of Two Database Partitioning Approaches that Support Taxonomy-Based Query AnsweringProceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services10.1145/3428757.3429108(426-435)Online publication date: 30-Nov-2020

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