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International Conference on Practical Applications of Agents and Multi-Agent Systems

PAAMS 2014: Highlights of Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection pp 202–213Cite as

High-Level Information Fusion for Risk and Accidents Prevention in Pervasive Oil Industry Environments

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 430))

Abstract

Information fusion studies theories and methods to effectively combine data from multiple sensors and related information to achieve more specific inferences that could be achieved by using a single, independent sensor. Information fused from sensors and data mining analysis has recently attracted the attention of the research community for real-world applications. In this sense, the deployment of an Intelligent Offshore Oil Industry Environment will help to figure out a risky scenario based on the events occurred in the past related to anomalies and the profile of the current employee (role, location, etc.). In this paper we propose an information fusion model for an intelligent oil environment in which employees are alerted about possible risk situations while their are moving around their working place. The layered architecture, implements a reasoning engine capable of intelligently filtering the context profile of the employee (role, location) for the feature selection of an inter-transaction mining process. Depending on the employee contextual information he will receive intelligent alerts based on the prediction model that use his role and his current location. This model provides the big picture about risk analysis for that employee at that place in that moment.

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Author information

Authors and Affiliations

  1. Instituto de Lógica, Filosofia e Teoria da Ciéncia (ILTC), Niterói, RJ, Brazil

    Nayat Sanchez-Pi

  2. Dept. of Electrical Engineering, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ, Brazil

    Luis Martí

  3. Dept. of Informatics, Universidad Carlos III de Madrid, Colmenarejo, Madrid, Spain

    José Manuel Molina

  4. ADDLabs, Fluminense Federal University, Niterói, RJ, Brazil

    Ana Cristina Bicharra Garcia

Authors
  1. Nayat Sanchez-Pi
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  2. Luis Martí
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  3. José Manuel Molina
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  4. Ana Cristina Bicharra Garcia
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Editor information

Editors and Affiliations

  1. Departamento Informática y Automática, Universidad de Salamanca, Plaza de la Merced s/n, 37008, Salamanca, Spain

    Juan M. Corchado

  2. Department of Artificial Intelligence, Technical Univesity of Madrid, Bloque 2, Despacho 2101, Campus Montegancedo, Boadilla del Monte,, Madrid, Spain

    Javier Bajo

  3. AGH University of Science and Technology (AGH), 30-059, Krakow, Poland

    Jaroslaw Kozlak

  4. Poznan University of Technology, ul.Strzelecka 11, 60-965, Poznań, Poland

    Pawel Pawlewski

  5. Departamento de Informática, Universidad Carlos III de Madrid, Avenida de la Universidad Carlos III, 22, Colmenarejo, Madrid, Spain

    Jose M. Molina

  6. IRIT, University of Toulouse, 118 Route de Narbonne, 31062, Toulouse cedex 9, France

    Benoit Gaudou

  7. Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022, Valencia, Spain

    Vicente Julian

  8. Universität Duisburg-Essen, Schützenbahn 70 (Hochhaus), 45117, Essen, Germany

    Rainer Unland

  9. Laboratório Nacional de Energia e Geologia (LNEG), Est. Paço do Lumiar 22, 1649-038, Lisbon, Portugal

    Fernando Lopes

  10. Maersk Mc-Kinney Moller Institute, University of Southern Denmark, DK-5230, Odense M, Denmark

    Kasper Hallenborg

  11. Dpt. Signal Theory, Telematics and Com-munications, University of Granada, C/ Periodista Daniel Saucedo Aranda, s/n, 18071, Granada, Spain

    Pedro García Teodoro

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© 2014 Springer International Publishing Switzerland

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Sanchez-Pi, N., Martí, L., Molina, J.M., Bicharra Garcia, A.C. (2014). High-Level Information Fusion for Risk and Accidents Prevention in Pervasive Oil Industry Environments. In: Corchado, J.M., et al. Highlights of Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection. PAAMS 2014. Communications in Computer and Information Science, vol 430. Springer, Cham. https://doi.org/10.1007/978-3-319-07767-3_19

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  • DOI: https://doi.org/10.1007/978-3-319-07767-3_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07766-6

  • Online ISBN: 978-3-319-07767-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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