Combining CRM Strength and Big Data Tools for Customers Profile Analysis

  • Z. Elyusufi
  • Y. Elyusufi
  • M. AitkbirEmail author
Conference paper
Part of the Lecture Notes in Intelligent Transportation and Infrastructure book series (LNITI)


Today Big Data tools are not just a phenomenon of the massive information collection; they are also the best way to approach a customer target. These technologies allow the profiling of the customers of an organization thanks to the histories of purchases, the products that they consult; the data that they share through the social networks. They also make it possible to anticipate the purchase of actions via behavioral analysis. Therefore, the combination of the power of CRM and the performance of BIG DATA tools brings a great added value for customers profile analysis, especially if it is about events triggered in real time. It is in this context that the present work is positioned. Our goal is to intercept events (customer behaviors) and analyze them in real time. We will use the Complex Events Process (CEP) architecture that perfectly meets this need. In order to successfully implement our CEP architecture, we will use the ontology approach.


Profiling CEP CRM Ontology Big data 


  1. 1.
    Cugola, G., Margara, A.: Processing Flows of Information: From Data Stream to Complex Event Processing. ACM Computing Surveys (2012)Google Scholar
  2. 2.
    Tacke: Taxonomien & Ontologien. Soziales Retrieval im Web 2.0. Germany, Oct 2008Google Scholar
  3. 3.
    Teymourian, K.: Semantic Complex Event Processing, Sept 2010Google Scholar
  4. 4.
    Sack, H.: Ontologien. University of Jena, Germany (2006)Google Scholar
  5. 5.
    Hella, L.: Ontologies for Big Data. Norwegian University of Science and Technologies, Norway (2014)Google Scholar
  6. 6.
    Wang, W., Guo, W., Yingwei Luo, X.W., Xu, Z.: Ontological model of event for integration of inter-organization applications, pp. 301–310 (2005)Google Scholar
  7. 7.
    Astrova, A., Koschel, A., Lukanowski, J., Martinez, J.L.M., Procenko, V., Schaaf, M: Ontologies for complex event processing. World Acad. Sci. Eng. Technol. Int. J. Comput. Inf. Eng. 8(5) (2014)Google Scholar
  8. 8.
    Demers, A., Gehrke, J., Hong, M., Riedewald, M., White, W.: Towards Expressive Publish/Subscribe Systems EDBT 2006, numéro 3896 de Lecture Notes in Computer Science. Springer, Berlin, Heidelberg. Scholar
  9. 9.
    Suhothayan, S., Gajasinghe, K., Loku Narangoda, I., Chaturanga, S., Perera, S., Nanayakkara, V.: Siddhi: A second look at complex event processing architectures. In: Proceedings of the 2011 ACM Workshop on Gateway Computing Environments, GCE’11. ACM, New York, NY, USA (2011)Google Scholar
  10. 10.
    Cugola, G., Margara, A.: Complex event processing with T-REX. J. Syst. Softw. 85(8), 1709–1728 (2012)CrossRefGoogle Scholar
  11. 11.
    Cugola, G., Margara, A.: TESLA: a formally defined event specification language. In: Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems, DEBS’10 (2010)Google Scholar
  12. 12.
    Bass, T.: CISSP, Next Generation Security Event Management (SEM) with Complex Event Processing (CEP), CDIC (2007)Google Scholar
  13. 13.
    Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, NSDI’12 (2012)Google Scholar
  14. 14.
    Anett, H., Ana, R., Christophe, N.: Ontology-based user profile learning from heterogeneous web resources in a big data context. CheckSem Research Group, Laboratoire Electronique, Informatique et Image (LE2I), Université de BourgogneGoogle Scholar
  15. 15.
    Hoppe: Automatic ontology based user profile learning from heterogeneous web resources in a big data context, 26–30 Aug 2013, Riva del Garda, Trento, Italy. Proc. VLDB Endowm. 6(12) (2013)Google Scholar
  16. 16.
    Twardowski, B., Ryzko, D.: Multi-agent architecture for real-time big data processing. In: International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACMGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.LIST LaboratoryDoctoral Studies Center STI, Abdelmalek Essaadi UniversityTétouanMorocco

Personalised recommendations