Information Fusion for Improving Decision-Making in Big Data Applications

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


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.


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.



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.


  1. 1.
    Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Human Factors: J. Human Factors Ergon. Soc. 37(1), 32–64 (1995)CrossRefGoogle Scholar
  2. 2.
    Borrajo, M.L., Baruque, B., Corchado, E., Bajo, J., Corchado, J.M.: Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises. Int. J. Neural Syst. 21(04), 277–296 (2011)CrossRefGoogle Scholar
  3. 3.
    De Paz, J.F., Bajo, J., López, V.F., Corchado, J.M.: Biomedic organizations: an intelligent dynamic architecture for KDD. Inf. Sci. 224, 49–61 (2013)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Conti, M., Pietro, R.D., Mancini, L.V., Mei, A.: Distributed data source verification in wireless sensor networks. Inf. Fusion 10(4), 342–353 (2009)CrossRefGoogle Scholar
  5. 5.
    Piatetsky-Shapiro, G.: Discovery, analysis and presentation of strong rules. In: Piatetsky-Shapiro G., Frawley W.J. (eds.) Knowledge Discovery in Databases, pp. 229–248. AAAI Press (1991)Google Scholar
  6. 6.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1994, pp. 487–499.
  7. 7.
    Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD Record, vol. 29, pp. 1–12. ACM (2000)Google Scholar
  9. 9.
    White, F.E.: Data Fusion Lexicon. Tech. Rep, DTIC Document (1991)Google Scholar
  10. 10.
    Luo, R.C., Chou, Y.C., Chen, O.: Multisensor fusion and integration: algorithms, applications, and future research directions. In: International Conference on Mechatronics and Automation, 2007. ICMA 2007, pp. 1986–1991. IEEE (2007)Google Scholar
  11. 11.
    Dasarathy, B.V.: Sensor fusion potential exploitation-innovative architectures and illustrative applications. Proc. IEEE 85(1), 24–38 (1997)CrossRefGoogle Scholar
  12. 12.
    Blasch, E., Llinas, J., Lambert, D., Valin, P., Das, S., Chong, C., Kokar, M., Shahbazian, E.: High level information fusion developments, issues, and grand challenges: fusion 2010 panel discussion. In: 2010 13th Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2010)Google Scholar
  13. 13.
    Chong, C.-Y., Liggins, M., et al.: Fusion technologies for drug interdiction. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI’94), pp. 435–441. IEEE (1994)Google Scholar
  14. 14.
    Gad, A., Farooq, M.: Data fusion architecture for maritime surveillance. In: Proceedings of the Fifth International Conference on Information Fusion (FUSION’02), vol. 1, pp. 448–455. IEEE (2002)Google Scholar
  15. 15.
    Liggins, M.E., Bramson, A., et al.: Off-board augmented fusion for improved target detection and track. In: 1993 Conference Record of The Twenty-Seventh Asilomar Conference on Signals, Systems and Computers, pp. 295–299. IEEE (1993)Google Scholar
  16. 16.
    Ahlberg, S., Hörling, P., Johansson, K., Jöred, K., Kjellström, H., Mårtenson, C., Neider, G., Schubert, J., Svenson, P., Svensson, P., et al.: An information fusion demonstrator for tactical intelligence processing in network-based defense. Inf. Fusion 8(1), 84–107 (2007)CrossRefGoogle Scholar
  17. 17.
    Aldinger, T., Kao, J.: Data fusion and theater undersea warfare-an oceanographer’s perspective. In: OCEANS’04. MTTS/IEEE TECHNO-OCEAN’04, vol. 4, pp. 2008–2012. IEEE (2004)Google Scholar
  18. 18.
    Corona, I., Giacinto, G., Mazzariello, C., Roli, F., Sansone, C.: Information fusion for computer security: State of the art and open issues. Inf. Fusion 10(4), 274–284 (2009)CrossRefGoogle Scholar
  19. 19.
    Giacinto, G., Roli, F., Sansone, C.: Information fusion in computer security. Inf. Fusion 10(4), 272–273 (2009)CrossRefGoogle Scholar
  20. 20.
    Little, E.G., Rogova, G.L.: Ontology meta-model for building a situational picture of catastrophic events. In: 8th International Conference on Information Fusion (FUSION’05), vol. 1, pp. 1–8. IEEE (2005)Google Scholar
  21. 21.
    Llinas, J.: Information fusion for natural and man-made disasters. In: Proceedings of the Fifth International Conference on Information Fusion (FUSION’02), vol. 1, pp. 570–576. IEEE (2002)Google Scholar
  22. 22.
    Llinas, J., Moskal, M., McMahon, T.: Information fusion for nuclear, chemical, biological & radiological (NCBR) battle management support/disaster response management support. Tech. Rep., Center for MultiSource Information Fusion, School of Engineering and Applied Sciences, University of Buffalo, USA (2002)Google Scholar
  23. 23.
    Mattioli, J., Museux, N., Hemaissia, M., Laudy, C.: A crisis response situation model. In: 10th International Conference on Information Fusion (FUSION’07), pp. 1–7. IEEE (2007)Google Scholar
  24. 24.
    Bashi, A.: Fault detection for systems with multiple unknown modes and similar units. Ph.D. Thesis, University of New Orleans (2010)Google Scholar
  25. 25.
    Bashi, A., Jilkov, V.P., Li, X.R.: Fault detection for systems with multiple unknown modes and similar units-part i. In: 12th International Conference on Information Fusion (FUSION’09), pp. 732–739. IEEE (2009)Google Scholar
  26. 26.
    Basir, O., Yuan, X.: Engine fault diagnosis based on multi-sensor information fusion using dempster-shafer evidence theory. Inf. Fusion 8(4), 379–386 (2007)CrossRefGoogle Scholar
  27. 27.
    Heiden, U., Segl, K., Roessner, S., Kaufmann, H.: Ecological evaluation of urban biotope types using airborne hyperspectral hymap data. In: 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, pp. 18–22. IEEE (2003)Google Scholar
  28. 28.
    Khalil, A., Gill, M.K., McKee, M.: New applications for information fusion and soil moisture forecasting. In: 8th International Conference on Information Fusion (FUSION’05), vol. 2, p. 7. IEEE (2005)Google Scholar
  29. 29.
    Hubert-Moy, L., Corgne, S., Mercier, G., Solaiman, B.: Land use and land cover change prediction with the theory of evidence: a case study in an intensive agricultural region of france. In: Proceedings of the Fifth International Conference on Information Fusion (FUSION’02), vol. 1, pp. 114–121. IEEE (2002)Google Scholar
  30. 30.
    Gómez-Romero, J., Garcia, J., Kandefer, M., Llinas, J., Molina, J., Patricio, M., Prentice, M., Shapiro, S.: Strategies and techniques for use and exploitation of contextual information in high-level fusion architectures. In: 2010 13th Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2010)Google Scholar
  31. 31.
    Sanchez-Pi, N., Martí, L., Molina, J.M., Garcia, A.C.B.: High-level information fusion for risk and accidents prevention in pervasive oil industry environments. In: Highlights of Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection, pp. 202–213. Springer (2014)Google Scholar
  32. 32.
    Sanchez-Pi, N., Martí, L., Molina, J.M., Garcia, A.C.B.: An information fusion framework for context-based accidents prevention. In: 2014 Proceedings of the 17th International Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2014)Google Scholar
  33. 33.
    Gómez-Romero, J., Patricio, M.A., García, J., Molina, J.M.: Ontological representation of context knowledge for visual data fusion. In: 12th International Conference on Information Fusion (FUSION’09), pp. 2136–2143. IEEE (2009)Google Scholar
  34. 34.
    Sanchez-Pi, N., Martí, L., Bicharra Garcia, A.C.: Text classification techniques in oil industry applications. In: Herrero A., Baruque B., Klett F., Abraham A., Snášel V., Carvalho A.C., García Bringas P., Zelinka I., Quintián H., Corchado E. (eds.) International Joint Conference SOCO’13-CISIS’13-ICEUTE’13, vol. 239 of Advances in Intelligent Systems and Computing, pp. 211–220. Springer International Publishing (2014).
  35. 35.
    Berberidis, C., Angelis, L., Vlahavas, I.: Inter-transaction association rules mining for rare events prediction. In: Proceedings 3rd Hellenic Conference on Artificial Intelligence (2004)Google Scholar
  36. 36.
    Tharp, A.L.: File organization and processing. Wiley (1988)Google Scholar
  37. 37.
    Sayood, K.: Introduction to Data Compression, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco (2000)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Nayat Sanchez-Pi
    • 1
    Email author
  • 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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