Melola Discovery

← Melola Discovery

Open access Journal SINTA 2

Statistical Regression Model for Query Efficiency in Relational Databases

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 20 No. 4 (2026)

DOI
10.22146/ijccs.121225
ISSN
2460-7258, 1978-1520
Published
2026-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Predicting query response time supports capacity planning and the prioritization of database design changes, yet many published prediction models rely on benchmark workloads and opaque learned models. This study reports an empirical analysis of a production PostgreSQL transaction system in a glass manufacturing company, using multivariate linear regression as a transparent baseline. Response time (s) was modelled from four design-level variables: number of tables, number of indexes, database size (MB) and query complexity (number of joins), using 45 observations collected during the 2016–2025 period via direct measurement from production logs. The fitted model reported R² = 0.92 and RMSE = 0.12 s on chronological hold-out data. The four predictors were strongly intercorrelated (r = 0.92–0.99) and each correlated 0.97–0.98 with response time, so individual coefficients cannot be read as separate causal effects. The results are associations observed in one system and have not been validated on other workloads or configurations. The model is proposed as a transparent baseline for workload planning, and optimization recommendations are presented as hypotheses that require before-and-after testing.

Keywords

Collaboration network of the authors

2 authors · 1 co-authorship link

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Soegijapranata Catholic University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 1 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Statistical Regression Model for Query Efficiency in Relational Databases</dc:title>
	<dc:creator>Leong, Hironimus</dc:creator>
	<dc:creator>Santosa, Yonathan Purbo</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Query Response Time, Linear Regression, Query Complexity, System Optimization, PostgreSQL</dc:subject>
	<dc:description xml:lang="en-US">Predicting query response time supports capacity planning and the prioritization of database design changes, yet many published prediction models rely on benchmark workloads and opaque learned models. This study reports an empirical analysis of a production PostgreSQL transaction system in a glass manufacturing company, using multivariate linear regression as a transparent baseline. Response time (s) was modelled from four design-level variables: number of tables, number of indexes, database size (MB) and query complexity (number of joins), using 45 observations collected during the 2016–2025 period via direct measurement from production logs. The fitted model reported R² = 0.92 and RMSE = 0.12 s on chronological hold-out data. The four predictors were strongly intercorrelated (r = 0.92–0.99) and each correlated 0.97–0.98 with response time, so individual coefficients cannot be read as separate causal effects. The results are associations observed in one system and have not been validated on other workloads or configurations. The model is proposed as a transparent baseline for workload planning, and optimization recommendations are presented as hypotheses that require before-and-after testing.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/121225</dc:identifier>
	<dc:identifier>10.22146/ijccs.121225</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 20, No 4 (2026): October</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/121225/45919</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/downloadSuppFile/121225/46659</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>