@inproceedings{9f71cd9ef2a748c090f32c57ba5e10c5,
title = "Probabilistic Processing of Interval-valued Sensor Data",
abstract = "When dealing with sensors with different time resolutions, it is desirable to model a sensor reading as pertaining to a time interval rather than a unit of time. We introduce two variants on the Hidden Markov Model in which this is possible: a reading extends over an arbitrary number of hidden states. We derive inference algorithms for the models, and analyse their efficiency. For this, we introduce a new method: we start with an inefficient algorithm directly derived from the model, and visually optimize it using a sum-factor diagram.",
keywords = "IR-64883, EWI-13072, METIS-251087, DB-DMSN: Data Management for Sensor Networks, CR-H.2.8",
author = "S. Evers and M.M. Fokkinga and Apers, {Peter M.G.}",
note = "10.1145/1402050.1402060 ; 5th International Workshop on Data Management for Sensor Networks, DMSN ; Conference date: 24-08-2008 Through 24-08-2008",
year = "2008",
month = aug,
day = "24",
doi = "10.1145/1402050.1402060",
language = "Undefined",
isbn = "978-1-60558-284-9",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
number = "DTR08-9",
pages = "42--48",
booktitle = "Proceedings of the 5th International Workshop on Data Management for Sensor Networks (DMSN2008)",
address = "United States",
}