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 count- ing queries in machine learning applications. To achieve memory and computational effi- ciency, 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 associ- ation rule mining. Our methods significantly outperform commonly used ADtrees and hash tables, and are practical alternatives for process- ing 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 count- ing queries in machine learning applications. To achieve memory and computational effi- ciency, 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 associ- ation rule mining. Our methods significantly outperform commonly used ADtrees and hash tables, and are practical alternatives for process- ing 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 count- ing queries in machine learning applications. To achieve memory and computational effi- ciency, 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 associ- ation rule mining. Our methods significantly outperform commonly used ADtrees and hash tables, and are practical alternatives for process- ing 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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