### Abstract

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 language | English |
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Title of host publication | Proceedings of the 2018 SIAM International Conference on Data Mining |

Editors | Martin Ester, Dino Pedreschi |

Number of pages | 9 |

Publisher | Society for Industrial and Applied Mathematics |

Publication date | 2018 |

Pages | 441-449 |

ISBN (Electronic) | 978-1-61197-532-1 |

DOIs | |

Publication status | Published - 2018 |

MoE publication type | A4 Article in conference proceedings |

Event | SIAM International Conference on Data Mining - San Diego, United States Duration: 3 May 2018 → 5 May 2018 Conference number: SDM18 |

### Fields of Science

- 113 Computer and information sciences

### Cite this

*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

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*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Markov Chain Monitoring

AU - Chaudhari, Harshal A.

AU - Mathioudakis, Michael

AU - Terzi, Evimaria

PY - 2018

Y1 - 2018

N2 - 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.

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.

KW - 113 Computer and information sciences

U2 - 10.1137/1.9781611975321

DO - 10.1137/1.9781611975321

M3 - Conference contribution

SP - 441

EP - 449

BT - Proceedings of the 2018 SIAM International Conference on Data Mining

A2 - Ester, Martin

A2 - Pedreschi, Dino

PB - Society for Industrial and Applied Mathematics

ER -