Ranking of keyword-combined searches in relational databases based on relevance to the user query

W. K. Loh; H. Y. Kwon

doi:10.1049/el.2019.4266

Ranking of keyword-combined searches in relational databases based on relevance to the user query

W. K. Loh, H. Y. Kwon

Dept. of Industrial Engineering

Gachon University

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

In this Letter, the authors deal with ranking of keyword-combined searches in relational databases based on relevance to the user query, which they call KEYSIM searches. They formally define KEYSIM searches and propose a threshold-based method for efficiently processing KEYSIM searches. Their proposed method is the first one to find top-k results considering both numerical similarity and textual similarity. Through the experiments using five real and synthetic data sets, they show the efficiency and scalability of the proposed method.

Original language	English
Pages (from-to)	495-498
Number of pages	4
Journal	Electronics Letters
Volume	56
Issue number	10
DOIs	https://doi.org/10.1049/el.2019.4266
State	Published - 14 May 2020

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10.1049/el.2019.4266

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abstract = "In this Letter, the authors deal with ranking of keyword-combined searches in relational databases based on relevance to the user query, which they call KEYSIM searches. They formally define KEYSIM searches and propose a threshold-based method for efficiently processing KEYSIM searches. Their proposed method is the first one to find top-k results considering both numerical similarity and textual similarity. Through the experiments using five real and synthetic data sets, they show the efficiency and scalability of the proposed method.",

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AB - In this Letter, the authors deal with ranking of keyword-combined searches in relational databases based on relevance to the user query, which they call KEYSIM searches. They formally define KEYSIM searches and propose a threshold-based method for efficiently processing KEYSIM searches. Their proposed method is the first one to find top-k results considering both numerical similarity and textual similarity. Through the experiments using five real and synthetic data sets, they show the efficiency and scalability of the proposed method.

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Ranking of keyword-combined searches in relational databases based on relevance to the user query

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