Composability of Markov Models for Processing Sensor Data

S. Evers

Research output: Book/ReportReportProfessional

14 Downloads (Pure)

Abstract

We show that it is possible to apply the divide-and-conquer principle in constructing a Markov model for sensor data from available sensor logs. The state space can be partitioned into clusters, for which the required transition counts or probabilities can be acquired locally. The combination of these local parameters into a global model takes the form of a system of linear equations with a confined solution space. Expected advantages of this approach lie for example in reduced (wireless) communication costs.
Original languageUndefined
Place of PublicationEnschede
PublisherDatabases (DB)
Number of pages18
Publication statusPublished - 21 Dec 2007

Publication series

NameCTIT Technical Report Series
PublisherCentre for Telematics and Information Technology, University of Twente
No.1/TR-CTIT-07-91
ISSN (Print)1381-3625

Keywords

  • EWI-11576
  • METIS-245862
  • IR-64536

Cite this

Evers, S. (2007). Composability of Markov Models for Processing Sensor Data. (CTIT Technical Report Series; No. 1/TR-CTIT-07-91). Enschede: Databases (DB).
Evers, S. / Composability of Markov Models for Processing Sensor Data. Enschede : Databases (DB), 2007. 18 p. (CTIT Technical Report Series; 1/TR-CTIT-07-91).
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Evers, S 2007, Composability of Markov Models for Processing Sensor Data. CTIT Technical Report Series, no. 1/TR-CTIT-07-91, Databases (DB), Enschede.

Composability of Markov Models for Processing Sensor Data. / Evers, S.

Enschede : Databases (DB), 2007. 18 p. (CTIT Technical Report Series; No. 1/TR-CTIT-07-91).

Research output: Book/ReportReportProfessional

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AB - We show that it is possible to apply the divide-and-conquer principle in constructing a Markov model for sensor data from available sensor logs. The state space can be partitioned into clusters, for which the required transition counts or probabilities can be acquired locally. The combination of these local parameters into a global model takes the form of a system of linear equations with a confined solution space. Expected advantages of this approach lie for example in reduced (wireless) communication costs.

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Evers S. Composability of Markov Models for Processing Sensor Data. Enschede: Databases (DB), 2007. 18 p. (CTIT Technical Report Series; 1/TR-CTIT-07-91).