Deterministic and probabilistic implementation of context

This paper addresses the problem of implementing an abstract context model. First, the abstract context model is represented by a network of situations. Two different implementations for the situation model are then proposed: a deterministic one based on Petri nets and a probabilistic one based on h...

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Main Authors: Brdiczka, O., Reignier, P., Crowley, J.L., Vaufreydaz, D., Maisonnasse, J.
Format: Conference Proceeding
Language:English
Subjects:
Context modeling
Context-aware services
Hidden Markov models
Humans
Knowledge representation
Natural language processing
Pervasive computing
Petri nets
Testing
Ubiquitous computing
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creator Brdiczka, O.
Reignier, P.
Crowley, J.L.
Vaufreydaz, D.
Maisonnasse, J.
description This paper addresses the problem of implementing an abstract context model. First, the abstract context model is represented by a network of situations. Two different implementations for the situation model are then proposed: a deterministic one based on Petri nets and a probabilistic one based on hidden Markov models. Both implementations are illustrated and applied to real-world problems
doi_str_mv 10.1109/PERCOMW.2006.40
format conference_proceeding
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identifier ISBN: 0769525202
ispartof Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW'06), 2006, p.5 pp.-50
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Context modeling
Context-aware services
Hidden Markov models
Humans
Knowledge representation
Natural language processing
Pervasive computing
Petri nets
Testing
Ubiquitous computing
title Deterministic and probabilistic implementation of context
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