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Authors: Nabil Benayadi and Marc Le Goc

Affiliation: University Saint Jerome, France

ISBN: 978-989-8425-05-8

Keyword(s): Sequential patterns, Information-theory, Temporal knowledge Discovering, Chronicles models, Markov processes.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Industrial Applications of Artificial Intelligence ; Information Systems Analysis and Specification ; Modeling Formalisms, Languages and Notations ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: We introduce the problem of mining sequential patterns in large database of sequences using a Stochastic Approach. An example of patterns we are interested in is : 50% of cases of engine stops in the car are happened between 0 and 2 minutes after observing a lack of the gas in the engine, produced between 0 and 1 minutes after the fuel tank is empty. We call this patterns “signatures”. Previous research have considered some equivalent patterns, but such work have three mains problems : (1) the sensibility of their algorithms with the value of their parameters, (2) too large number of discovered patterns, and (3) their discovered patterns consider only ”after“ relation (succession in time) and omit temporal constraints between elements in patterns. To address this issue, we present TOM4L process (Timed Observations Mining for Learning process) which uses a stochastic representation of a given set of sequences on which an inductive reasoning coupled with an abductive reasoning is appli ed to reduce the space search. The results obtained with an application on very complex real world system are also presented to show the operational character of the TOM4L process. (More)

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Paper citation in several formats:
Benayadi N.; Le Goc M. and (2010). MINING TIMED SEQUENCES WITH TOM4L FRAMEWORK.In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-05-8, pages 111-120. DOI: 10.5220/0002958401110120

@conference{iceis10,
author={Nabil Benayadi and Marc {Le Goc}},
title={MINING TIMED SEQUENCES WITH TOM4L FRAMEWORK},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2010},
pages={111-120},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002958401110120},
isbn={978-989-8425-05-8},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - MINING TIMED SEQUENCES WITH TOM4L FRAMEWORK
SN - 978-989-8425-05-8
AU - Benayadi, N.
AU - Le Goc, M.
PY - 2010
SP - 111
EP - 120
DO - 10.5220/0002958401110120

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