CT-NOR: representing and reasoning about events in continuous time

Aleksandr Simma, Moises Goldszmidt, John MacCormick, Paul Barham, Richard Black, Rebecca Isaacs, Richard Mortier
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:484-493, 2008.

Abstract

We present a generative model for representing and reasoning about the relationships among events in continuous time. We apply the model to the domain of networked and distributed computing environments where we fit the parameters of the model from timestamp observations, and then use hypothesis testing to discover dependencies between the events and changes in behavior for monitoring and diagnosis. After introducing the model, we present an EM algorithm for fitting the parameters and then present the hypothesis testing approach for both dependence discovery and change-point detection. We validate the approach for both tasks using real data from a trace of network events at Microsoft Research Cambridge. Finally, we formalize the relationship between the proposed model and the noisy-or gate for cases when time can be discretized.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR6-simma08a, title = {CT-NOR: representing and reasoning about events in continuous time}, author = {Simma, Aleksandr and Goldszmidt, Moises and MacCormick, John and Barham, Paul and Black, Richard and Isaacs, Rebecca and Mortier, Richard}, booktitle = {Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence}, pages = {484--493}, year = {2008}, editor = {McAllester, David A. and Myllymäki, Petri}, volume = {R6}, series = {Proceedings of Machine Learning Research}, month = {09--12 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r6/main/assets/simma08a/simma08a.pdf}, url = {https://proceedings.mlr.press/r6/simma08a.html}, abstract = {We present a generative model for representing and reasoning about the relationships among events in continuous time. We apply the model to the domain of networked and distributed computing environments where we fit the parameters of the model from timestamp observations, and then use hypothesis testing to discover dependencies between the events and changes in behavior for monitoring and diagnosis. After introducing the model, we present an EM algorithm for fitting the parameters and then present the hypothesis testing approach for both dependence discovery and change-point detection. We validate the approach for both tasks using real data from a trace of network events at Microsoft Research Cambridge. Finally, we formalize the relationship between the proposed model and the noisy-or gate for cases when time can be discretized.}, note = {Reissued by PMLR on 09 October 2024.} }
Endnote
%0 Conference Paper %T CT-NOR: representing and reasoning about events in continuous time %A Aleksandr Simma %A Moises Goldszmidt %A John MacCormick %A Paul Barham %A Richard Black %A Rebecca Isaacs %A Richard Mortier %B Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2008 %E David A. McAllester %E Petri Myllymäki %F pmlr-vR6-simma08a %I PMLR %P 484--493 %U https://proceedings.mlr.press/r6/simma08a.html %V R6 %X We present a generative model for representing and reasoning about the relationships among events in continuous time. We apply the model to the domain of networked and distributed computing environments where we fit the parameters of the model from timestamp observations, and then use hypothesis testing to discover dependencies between the events and changes in behavior for monitoring and diagnosis. After introducing the model, we present an EM algorithm for fitting the parameters and then present the hypothesis testing approach for both dependence discovery and change-point detection. We validate the approach for both tasks using real data from a trace of network events at Microsoft Research Cambridge. Finally, we formalize the relationship between the proposed model and the noisy-or gate for cases when time can be discretized. %Z Reissued by PMLR on 09 October 2024.
APA
Simma, A., Goldszmidt, M., MacCormick, J., Barham, P., Black, R., Isaacs, R. & Mortier, R.. (2008). CT-NOR: representing and reasoning about events in continuous time. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R6:484-493 Available from https://proceedings.mlr.press/r6/simma08a.html. Reissued by PMLR on 09 October 2024.

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