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Information Fusion for Improving Decision-Making in Big Data Applications

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Resource Management for Big Data Platforms

Part of the book series: Computer Communications and Networks ((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.

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References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. http://dl.acm.org/citation.cfm?id=645920.672836

  7. Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)

    Article  MathSciNet  Google Scholar 

  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. White, F.E.: Data Fusion Lexicon. Tech. Rep, DTIC Document (1991)

    Google Scholar 

  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. Dasarathy, B.V.: Sensor fusion potential exploitation-innovative architectures and illustrative applications. Proc. IEEE 85(1), 24–38 (1997)

    Article  Google Scholar 

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  19. Giacinto, G., Roli, F., Sansone, C.: Information fusion in computer security. Inf. Fusion 10(4), 272–273 (2009)

    Article  Google Scholar 

  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. 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. 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. 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. Bashi, A.: Fault detection for systems with multiple unknown modes and similar units. Ph.D. Thesis, University of New Orleans (2010)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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. 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. 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. 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. 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. 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). http://dx.doi.org/10.1007/978-3-319-01854-6_22

  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. Tharp, A.L.: File organization and processing. Wiley (1988)

    Google Scholar 

  37. Sayood, K.: Introduction to Data Compression, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco (2000)

    MATH  Google Scholar 

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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.

Author information

Authors and Affiliations

  1. Institute of Mathematics and Statistics, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil

    Nayat Sanchez-Pi

  2. Institute of Computing, Universidade Federal Fluminense, Rio de Janeiro, Brazil

    Luis Martí & Ana C. Bicharra García

  3. Computer Science Department, Universidad Carlos III de Madrid, Leganes, Spain

    José Manuel Molina

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 C. Bicharra García
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Corresponding author

Correspondence to Nayat Sanchez-Pi .

Editor information

Editors and Affiliations

  1. University Politehnica of Bucharest, Bucharest, Romania

    Florin Pop

  2. Cracow University of Technology, Cracow, Poland

    Joanna Kołodziej

  3. Second University of Naples, Naples, Caserta, Italy

    Beniamino Di Martino

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Sanchez-Pi, N., Martí, L., Molina, J.M., Bicharra García, A.C. (2016). Information Fusion for Improving Decision-Making in Big Data Applications. In: Pop, F., Kołodziej, J., Di Martino, B. (eds) Resource Management for Big Data Platforms. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-44881-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-44881-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44880-0

  • Online ISBN: 978-3-319-44881-7

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