Abstract
We propose an auto-scheduling mechanism to execute counting queries in machine learning applications. Our approach improves the runtime efficiency of query streams by selecting, in the on-line manner, the optimal execution strategy for each query. We also discuss how to scale up counting queries in multi-threaded applications.
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References
Karan, S., Eichhorn, M., Hurlburt, B., Iraci, G., Zola, J.: Fast counting in machine learning applications. In: Uncertainty in Artificial Intelligence (2018)
Kohavi, R.: Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid. In: International Conference on Knowledge Discovery and Data Mining, pp. 202–207 (1996)
Moore, A., Lee, M.: Cached sufficient statistics for efficient machine learning with large datasets. J. Artif. Intell. Res. 8, 67–91 (1998)
Quinlan, J.: Bagging, boosting, and c4.5. In: AAAI Innovative Applications of Artificial Intelligence Conferences, pp. 725–730 (1996)
Ramos, J.: Using TF-IDF to determine word relevance in document queries. In: Instructional Conference on Machine Learning, pp. 133–142 (2003)
Salakhutdinov, R., Hinton, G.: Deep Boltzmann machines. In: International Conference on Artificial Intelligence and Statistics, pp. 448–455 (2009)
Acknowledgments
This research was supported by the National Science Centre (Poland) under grant no. UMO-2017/26/D/ST6/00687.
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Bratek, P., Szustak, L., Zola, J. (2022). Parallelization and Auto-scheduling of Data Access Queries in ML Workloads. In: Chaves, R., et al. Euro-Par 2021: Parallel Processing Workshops. Euro-Par 2021. Lecture Notes in Computer Science, vol 13098. Springer, Cham. https://doi.org/10.1007/978-3-031-06156-1_43
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DOI: https://doi.org/10.1007/978-3-031-06156-1_43
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