Fast Counting in Machine Learning Applications

Subhadeep Karan, Matthew Eichhorn, Blake Hurlburt, Grant Iraci, Jaroslaw Zola
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:539-548, 2018.

Abstract

We propose scalable methods to execute counting queries in machine learning applications. To achieve memory and computational efficiency, we abstract counting queries and their context such that the counts can be aggregated as a stream. We demonstrate performance and scalability of the resulting approach on random queries, and through extensive experimentation using Bayesian networks learning and association rule mining. Our methods significantly outperform commonly used ADtrees and hash tables, and are practical alternatives for processing large-scale data.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-karan18a, title = {Fast Counting in Machine Learning Applications}, author = {Karan, Subhadeep and Eichhorn, Matthew and Hurlburt, Blake and Iraci, Grant and Zola, Jaroslaw}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {539--548}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/karan18a/karan18a.pdf}, url = {https://proceedings.mlr.press/r16/karan18a.html}, abstract = {We propose scalable methods to execute counting queries in machine learning applications. To achieve memory and computational efficiency, we abstract counting queries and their context such that the counts can be aggregated as a stream. We demonstrate performance and scalability of the resulting approach on random queries, and through extensive experimentation using Bayesian networks learning and association rule mining. Our methods significantly outperform commonly used ADtrees and hash tables, and are practical alternatives for processing large-scale data.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Fast Counting in Machine Learning Applications %A Subhadeep Karan %A Matthew Eichhorn %A Blake Hurlburt %A Grant Iraci %A Jaroslaw Zola %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-karan18a %I PMLR %P 539--548 %U https://proceedings.mlr.press/r16/karan18a.html %V R16 %X We propose scalable methods to execute counting queries in machine learning applications. To achieve memory and computational efficiency, we abstract counting queries and their context such that the counts can be aggregated as a stream. We demonstrate performance and scalability of the resulting approach on random queries, and through extensive experimentation using Bayesian networks learning and association rule mining. Our methods significantly outperform commonly used ADtrees and hash tables, and are practical alternatives for processing large-scale data. %Z Reissued by PMLR on 04 October 2026.
APA
Karan, S., Eichhorn, M., Hurlburt, B., Iraci, G. & Zola, J.. (2018). Fast Counting in Machine Learning Applications. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:539-548 Available from https://proceedings.mlr.press/r16/karan18a.html. Reissued by PMLR on 04 October 2026.

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