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Information Fusion for Improving Decision-Making in Big Data Applications

  • Nayat Sanchez-Pi
  • Luis Martí
  • José Manuel Molina
  • Ana C. Bicharra García
Chapter
Part of the Computer Communications and Networks book series (CCN)

Abstract

The danger involved in oil and gas industry allied to, the not rare, world-spread accidents have promoted the concerns toward achieving and demonstrating good performance with regard to occupational, health and safety (OHS) issues. There are international OHS compliance policies that must be followed by any petroleum company to be able to operate. One of these policies is the register, at the spur of the moment, any anomaly that occurs during operation including environmental accidents, human accidents or, even, simply noncompliance behavior of the work force. In addition to register the anomaly, the entire process of analyzing, finding the root cause and solving the problem must get registered. As a consequence, an increasingly huge database has been created in many companies with these reports. The data may or may not be structured, but for sure is composed of different sources and types. For instance, whenever needed, data from the workforce will be registered side by side with data from the involved equipment. Human manipulation of this huge and diversified data is a difficult, or even impossible, task. We present a data fusion architecture coupled with a machine-learning layer for providing abstractions and inferences over the data. The idea is to prove that our approach allows analysts to infer the relevant root-cause-and-effect relations that underlie the domain. We developed a system according to our model and used with data from a petroleum company. In addition to prove the feasibility of our approach we have compared with state-of-the art data mining techniques. Results have shown the efficiency in terms of accuracy and recall of our approach.

Keywords

Association Rule Rule Mining Frequent Itemsets Information Fusion Situation Assessment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was partially funded by CNPq PVE 314017/2013-5, FAPERJ APQ1 Project 211.500/2015, FAPERJ APQ1 Project 211.451/2015 and by projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Nayat Sanchez-Pi
    • 1
  • Luis Martí
    • 2
  • José Manuel Molina
    • 3
  • Ana C. Bicharra García
    • 2
  1. 1.Institute of Mathematics and StatisticsUniversidade do Estado do Rio de JaneiroRio de JaneiroBrazil
  2. 2.Institute of ComputingUniversidade Federal FluminenseRio de JaneiroBrazil
  3. 3.Computer Science DepartmentUniversidad Carlos III de MadridLeganesSpain

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