Advertisement

An Infrastructure and Approach for Inferring Knowledge Over Big Data in the Vehicle Insurance Industry

  • Aikaterini K. KalouEmail author
  • Dimitrios A. Koutsomitropoulos
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 529)

Abstract

In recent years, insurance organizations have turned their attention to tapping into massive amounts of data that are continuously produced across their IT ecosystem. Even though the concept of Big Data provides the needed infrastructure for efficient data management, especially in terms of storage and processing, the aspects of Value and Variety still remain a topic for further investigation. To this end, we propose an infrastructure that can be deployed on top of the legacy databases of insurance companies. The ultimate aim of this attempt is to provide an efficient manner to access data on-the-fly and derive new value. In our work, we propose a Property and Casualty ontology and then exploit an OBDA system in order to leverage its power.

Keywords

Insurance sector Big data Ontology Linked data OBDA 

References

  1. 1.
    Barbieri, D.F., Braga, D., Ceri, S., Della Valle, E., Grossniklaus, M.: Querying RDF streams with C-SPARQL. SIGMOD Rec. 39(1), 20–26 (2010)CrossRefzbMATHGoogle Scholar
  2. 2.
    Bechhofer, S., Van Harmelen, F., Hendler, J., Horrocks, I., Mc Guinness, D.L., Patel-Schneider, P.F., Stein, L.A.: OWL Web Ontology Language Reference, W3C Recommendation http://www.w3.org/TR/2004/REC-owl-ref-20040210/
  3. 3.
    Broekstra, J., Kampman, A., van Harmelen, F.: Sesame: a generic architecture for storing and querying RDF and RDF Schema. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 54–68. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Calvanese, D., Cogrel, B., Komla-Ebri, S., Kontchakov, R., Lanti, D., Rezk, M., Rodriguez-Muro, M., Xiao, G.: Ontop: answering SPARQL queries over relational databases. Semantic Web – Interoperability, Usability, Applicability (2016, in Press). ISSN:1570-0844Google Scholar
  5. 5.
    Jenkins, W., Molnar, R., Wallman, B., Ford, T.: Property and Casualty Data Model Specification (2011)Google Scholar
  6. 6.
    Kalou, A.K., Koutsomitropoulos, D.A.: Linking data in the insurance sector: a case study. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H., Sioutas, S., Makris, C. (eds.) AIAI 2014. IFIP AICT, vol. 437, pp. 320–329. Springer, Heidelberg (2014)Google Scholar
  7. 7.
    Lanti, D., Rezk, M., Xiao, G., Calvanese, D.: The NPD benchmark: reality check for OBDA systems. In: Proceedings of the 18th International Conference on Extending Database Technology (EDBT), pp. 617–628 (2015)Google Scholar
  8. 8.
    Llull, E.: Big data analysis to transform insurance industry. Technical article, Financial Times (2016)Google Scholar
  9. 9.
    Marr, B.: How Big Data is changing insurance forever. Technical article, Forbes (2015)Google Scholar
  10. 10.
    Michel, F., Faron-Zucker, C., Montagnat, J.: A mapping-based method to query MongoDB documents with SPARQL. In: Hartmann, S., Ma, H. (eds.) DEXA 2016. LNCS, vol. 9828, pp. 52–67. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-44406-2_6 CrossRefGoogle Scholar
  11. 11.
    Mitchell, I., Wilson, M.: Linked Data: Connecting and exploiting big data. White paper. Fujitsu UK (2012)Google Scholar
  12. 12.
    World Bank Group. Transport and ICT: Open Data for Sustainable Development. Technical report (2015)Google Scholar
  13. 13.
    Poggi, A., Lembo, D., Calvanese, D., De Giacomo, G., Lenzerini, M., Rosati, R.: Linking data to ontologies. In: Spaccapietra, S. (ed.) Journal on Data Semantics X. LNCS, vol. 4900, pp. 133–173. Springer, Heidelberg (2008)Google Scholar
  14. 14.
    Soares, S.: IBM InfoSphere: A Platform for Big Data Governance and Process Data Governance. MC Press Online, LLC, February 2013Google Scholar
  15. 15.
    Sodenkamp, M., Kozlovskiy, I., Staake, T.: Gaining IS business value through big data analytics: a case study of the energy sector. In: Proceedings of the Thirty Sixth International Conference on Information Systems (ICIS), Fort Worth, USA, pp. 13–16 (2015)Google Scholar
  16. 16.
    The Object Management Group (OMG). MDA Guide Version 1.0.1 (2003)Google Scholar
  17. 17.
    Tsai, C.W., Lai, C.F., Chao, H.C., Vasilakos, A.C.: Big data analytics: a survey. J. Big Data 2(21), 1–32 (2015)Google Scholar
  18. 18.
    Ylijoki, O., Porras, J.: Perspectives to definition of big data: a mapping study and discussion. J. Innov. Manag. 4(1), 69–91 (2016)Google Scholar
  19. 19.
    Lenzerini, M.: Ontology-based data management. In: Proceedings of CIKM 2011, pp. 5–6 (2011)Google Scholar
  20. 20.
    Rodriguez-Muro, M., Calvanese, D.: Quest, an OWL 2 QL reasoner for ontology-based data access. In: Proceedings of the 9th International Workshop on OWL: Experiences and Directions (OWLED 2012). CEUR Electronic Workshop Proceedings, vol. 849 (2012)Google Scholar
  21. 21.
    Laney, D.: 3D data management: Controlling data volume, velocity and variety. META Group Research Note 6, 70 (2001)Google Scholar
  22. 22.
    Press, G.: Top 10 hot big data technologies. Technical article. Forbes (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Aikaterini K. Kalou
    • 1
    Email author
  • Dimitrios A. Koutsomitropoulos
    • 1
  1. 1.High Performance Information Systems Laboratory (HPCLab), Computer Engineering and Informatics Department, School of EngineeringUniversity of PatrasPatras-RioGreece

Personalised recommendations