Markov Chain Monitoring

Harshal A. Chaudhari, Michael Mathioudakis, Evimaria Terzi

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

In networking applications, one often wishes to obtain estimates about the number of objects at different parts of the network (e.g., the number of cars at an intersection of a road network or the number of packets expected to reach a node in a computer network) by monitoring the traffic in a small number of network nodes or edges. We formalize this task by defining the Markov Chain Monitoring problem.

Given an initial distribution of items over the nodes of a Markov chain, we wish to estimate the distribution of items at subsequent times. We do this by asking a limited number of queries that retrieve, for example, how many items transitioned to a specific node or over a specific edge at a particular time. We consider different types of queries, each defining a different variant of the Markov Chain Monitoring. For each variant, we design efficient algorithms for choosing the queries that make our estimates as accurate as possible. In our experiments with synthetic and real datasets, we demonstrate the efficiency and the efficacy of our algorithms in a variety of settings.
Original languageEnglish
Title of host publicationProceedings of the 2018 SIAM International Conference on Data Mining
EditorsMartin Ester, Dino Pedreschi
Number of pages9
PublisherSociety for Industrial and Applied Mathematics
Publication date2018
Pages441-449
ISBN (Electronic)978-1-61197-532-1
DOIs
Publication statusPublished - 2018
MoE publication typeA4 Article in conference proceedings
EventSIAM International Conference on Data Mining - San Diego, United States
Duration: 3 May 20185 May 2018
Conference number: SDM18

Fields of Science

  • 113 Computer and information sciences

Cite this

Chaudhari, H. A., Mathioudakis, M., & Terzi, E. (2018). Markov Chain Monitoring. In M. Ester, & D. Pedreschi (Eds.), Proceedings of the 2018 SIAM International Conference on Data Mining (pp. 441-449). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611975321
Chaudhari, Harshal A. ; Mathioudakis, Michael ; Terzi, Evimaria. / Markov Chain Monitoring. Proceedings of the 2018 SIAM International Conference on Data Mining. editor / Martin Ester ; Dino Pedreschi. Society for Industrial and Applied Mathematics, 2018. pp. 441-449
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Chaudhari, HA, Mathioudakis, M & Terzi, E 2018, Markov Chain Monitoring. in M Ester & D Pedreschi (eds), Proceedings of the 2018 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, pp. 441-449, SIAM International Conference on Data Mining, San Diego, United States, 03/05/2018. https://doi.org/10.1137/1.9781611975321

Markov Chain Monitoring. / Chaudhari, Harshal A.; Mathioudakis, Michael; Terzi, Evimaria.

Proceedings of the 2018 SIAM International Conference on Data Mining. ed. / Martin Ester; Dino Pedreschi. Society for Industrial and Applied Mathematics, 2018. p. 441-449.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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AB - In networking applications, one often wishes to obtain estimates about the number of objects at different parts of the network (e.g., the number of cars at an intersection of a road network or the number of packets expected to reach a node in a computer network) by monitoring the traffic in a small number of network nodes or edges. We formalize this task by defining the Markov Chain Monitoring problem.Given an initial distribution of items over the nodes of a Markov chain, we wish to estimate the distribution of items at subsequent times. We do this by asking a limited number of queries that retrieve, for example, how many items transitioned to a specific node or over a specific edge at a particular time. We consider different types of queries, each defining a different variant of the Markov Chain Monitoring. For each variant, we design efficient algorithms for choosing the queries that make our estimates as accurate as possible. In our experiments with synthetic and real datasets, we demonstrate the efficiency and the efficacy of our algorithms in a variety of settings.

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Chaudhari HA, Mathioudakis M, Terzi E. Markov Chain Monitoring. In Ester M, Pedreschi D, editors, Proceedings of the 2018 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics. 2018. p. 441-449 https://doi.org/10.1137/1.9781611975321