Sunkyung Kim, Ji Su Park, Jin Gon Shon

KIPS Transactions on Software and Data Engineering, Vol. 11, No. 4, pp. 157-162, Apr. 2022
https://doi.org/10.3745/KTSDE.2022.11.4.157,   PDF Download:
Keywords: similarity, Lightweight Similarity, Similarity Query, database
Abstract

Today, most data is stored in a database (DB). In the DB environment, the users requests the DB to find the data they wants. Similarity Query has predicate that explained by a similarity. However, in the process of processing the similarity query, it is difficult to use an index that can reduce the range of processed records, so the cost of calculating the similarity for all records in the table is high each time. To solve this problem, this paper defines a lightweight similarity function. The lightweight similarity function has lower data filtering accuracy than the similarity function, but consumes less cost than the similarity function. We present a method for reducing similarity query processing cost by using the lightweight similarity function features. Then, Chebyshev distance is presented as a lightweight similarity function to the Euclidean distance function, and the processing cost of a query using the existing similarity function and a query using the lightweight similarity function is compared. And through experiments, it is confirmed that the similarity query processing cost is reduced when Chebyshev distance is applied as a lightweight similarity function for Euclidean similarity.

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