Big Data Visualization for Occupational Health and Security Problem in Oil and Gas Industry

  • Daniela Gorski Trevisan
  • Nayat Sanchez-PiEmail author
  • Luis Marti
  • Ana Cristina Bicharra Garcia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9172)


Association rule learning is a popular and well-researched set of methods for discovering interesting relations between entities in large databases in real-world problems. In this regard, an intelligent offshore oil industry environment is a very complex scenario and Occupational Health and Security (OHS) is a priority issue as it is an important factor to reduce the number of accidents and incidents records. In the oil industry, there exist standards to identify and record workplace accidents and incidents in order to provide guiding means on prevention efforts, indicating specific failures or reference, means of correction of conditions or circumstances that culminated in accident. OHS’s employees are in charge of analyzing the mined rules to extract knowledge. In most of cases these users has two main challenges during this process: (i) to explore the measures of interestingness (confidence, lift, support, etc.) and (ii) to understand and analyze the large number of association rules. In this sense, an intuitive visualization of mined rules becomes a key component in a decision-making process. In this paper, we propose a novel visualization of spatio-temporal rules that provides the big picture about risk analysis in a real world environment. Our main contribution lies in an interactive visualization of accident interpretations by means of well-defined spatio-temporal constraints, in the oil industry domain.


Data visualization Big data applications Decision support systems Oil and gas industry 


  1. 1.
    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 1994, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)Google 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.
    Bruzzese, D., Buono, P.: Combining visual techniques for association rules exploration. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 381–384. ACM (2004)Google Scholar
  4. 4.
    Bruzzese, D., Davino, C.: Visual post-analysis of association rules. J. Vis. Lang. Comput. 14(6), 621–635 (2003)CrossRefGoogle Scholar
  5. 5.
    Bruzzese, D., Davino, C.: Visual mining of association rules. In: Simo, S., Bhlen, M., Mazeika, A. (eds.) Visual Data Mining. Lecture Notes in Computer Science, vol. 4404, pp. 103–122. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    De Paz, J.F., Bajo, J., Lopez, V.F., Corchado, J.M.: Biomedic organizations: an intelligent dynamic architecture for KDD. Inf. Sci. 224, 49–61 (2013)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hahsler, M., Chelluboina, S.: Visualizing association rules: introduction to the r-extension package arulesviz. R Proj. Module 16, 223–238 (2011)Google Scholar
  9. 9.
    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
  10. 10.
    Hofmann, H., Siebes, A.P.J.M., Wilhelm, A.F.X.: Visualizing association rules with interactive mosaic plots. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2000, pp. 227–235. ACM, New York (2000)Google Scholar
  11. 11.
    Hofmann, H., Wilhelm, A.: Visual comparison of association rules. Comput. Stat. 16(3), 399–415 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Inselberg, A.: N-dimensional graphics, Part I - lines and hyperplanes. Harwood g. kolsky papers edn. International Business Machines Corporation (IBM). Los Angeles Scientific Center (1981)Google Scholar
  13. 13.
    Sekhavat, Y.A., Hoeber, O.: Visualizing association rules using linked matrix, graph, and detail views. Int. J. Intell. Sci. 3, 34 (2013)CrossRefGoogle Scholar
  14. 14.
    Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Daniela Gorski Trevisan
    • 2
  • Nayat Sanchez-Pi
    • 1
    Email author
  • Luis Marti
    • 3
  • Ana Cristina Bicharra Garcia
    • 2
  1. 1.Instituto de LogicaFilosofia E Teoria Da Ciência (ILTC)Niterói (RJ)Brazil
  2. 2.Computer Science InstituteFluminense Federal UniversityNiterói (RJ)Brazil
  3. 3.Department of Electrical EngineeringPontifícia Universidade Católica do Rio de JaneiroRio de Janeiro (RJ)Brazil

Personalised recommendations