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

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


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.


Information fusion context data mining ontologies oil industry 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Instituto de Lógica, Filosofia e Teoria da Ciéncia (ILTC)NiteróiBrazil
  2. 2.Dept. of Electrical EngineeringPontifícia Universidade Católica do Rio de JaneiroRio de JaneiroBrazil
  3. 3.Dept. of InformaticsUniversidad Carlos III de MadridMadridSpain
  4. 4.ADDLabsFluminense Federal UniversityNiteróiBrazil

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