## 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.

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