Abstract
Counting queries are fundamental operations widely used in machine learning applications. This paper focuses on optimizing their execution by introducing algorithmic enhancements to the bitmap-based counting query strategy that relies on a Depth-First Search (DFS) traversal. The proposed approach is evaluated through a benchmark involving the execution of random query streams across multiple test datasets. The experimental results demonstrate a significant speedup, with execution times reduced by factors ranging from 1.26× to 2.25×. Furthermore, potential directions for further improving the performance of counting queries on modern high-performance computing (HPC) systems are discussed.
Paper Type
Poster
DOI
10.62036/ISD.2025.71
Optimizing the DFS-based Strategy for Efficient Execution of Counting Queries in Machine Learning Applications
Counting queries are fundamental operations widely used in machine learning applications. This paper focuses on optimizing their execution by introducing algorithmic enhancements to the bitmap-based counting query strategy that relies on a Depth-First Search (DFS) traversal. The proposed approach is evaluated through a benchmark involving the execution of random query streams across multiple test datasets. The experimental results demonstrate a significant speedup, with execution times reduced by factors ranging from 1.26× to 2.25×. Furthermore, potential directions for further improving the performance of counting queries on modern high-performance computing (HPC) systems are discussed.
Recommended Citation
Bratek, P. & Szustak, L. (2025). Optimizing the DFS-based Strategy for Efficient Execution of Counting Queries in Machine Learning ApplicationsIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.71