Advertisement

RVO - The Research Variable Ontology

  • Madhushi BandaraEmail author
  • Ali Behnaz
  • Fethi A. Rabhi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11503)

Abstract

Enterprises today are presented with a plethora of data, tools and analytics techniques, but lack systems which help analysts to navigate these resources and identify best fitting solutions for their analytics problems. To support enterprise-level data analytics research, this paper presents Research Variable Ontology (RVO), an ontology designed to catalogue and explore essential data analytics design elements such as variables, analytics models and available data sources. RVO is specialised to support researchers with exploratory and predictive analytics problems, popularly practiced in economics and social science domains. We present the RVO design process, its schema, how it links and extends existing ontologies to provide a holistic view of analytics related knowledge and how data analysts at the enterprise level can use it. Capabilities of RVO are illustrated through a case study on House Price Prediction.

Keywords

Data analytics Semantic modeling Research variables 

Notes

Acknowledgment

We are grateful to Capsifi, especially Dr. Terry Roach, for sponsoring the research which led to this paper and Gilles Sainte-Marie for developing RVO web page.

References

  1. 1.
    Bandara, M., Behnaz, A., Rabhi, F.A., Demirors, O.: From requirements to data analytics process: an ontology-based approach. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) BPM 2018. LNBIP, vol. 342, pp. 543–552. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-11641-5_43CrossRefGoogle Scholar
  2. 2.
    Bandara, M., Rabhi, F.A.: Semantic modeling for engineering data analytic solutions. Semant. Web J. (2019, in press)Google Scholar
  3. 3.
    Behnaz, A., Natarajan, A., Rabhi, F.A., Peat, M.: A semantic-based analytics architecture and its application to commodity pricing. In: Feuerriegel, S., Neumann, D. (eds.) FinanceCom 2016. LNBIP, vol. 276, pp. 17–31. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-52764-2_2CrossRefGoogle Scholar
  4. 4.
    Brazdil, P., Carrier, C.G., Soares, C., Vilalta, R.: Metalearning: Applications to Data Mining. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-73263-1CrossRefzbMATHGoogle Scholar
  5. 5.
    Crankshaw, D., Gonzalez, J., Bailis, P.: Research for practice: prediction-serving systems. Commun. ACM 61(8), 45–49 (2018).  https://doi.org/10.1145/3190574CrossRefGoogle Scholar
  6. 6.
    Diamantini, C., Potena, D., Storti, E.: KDDONTO: an ontology for discovery and composition of KDD algorithms. In: Third Generation Data Mining: Towards Service-Oriented Knowledge Discovery (SoKD 2009), pp. 13–24 (2009)Google Scholar
  7. 7.
    Esteban, M., Altuzarra, A.: A model of the spanish housing market. J. Post Keynes. Econ. 30(3), 353–373 (2008)CrossRefGoogle Scholar
  8. 8.
    Gil, Y., Kim, J., Ratnakar, V., Deelman, E.: Wings for Pegasus: a semantic approach to creating very large scientific workflows. In: OWLED (2006)Google Scholar
  9. 9.
    Grüninger, M., Fox, M.S.: Methodology for the design and evaluation of ontologies (1995)Google Scholar
  10. 10.
    Imbert, S.: Mathematical Modelling Ontology (2017). http://bioportal.bioontology.org/ontologies/MAMO. Accessed 30 Nov 2018
  11. 11.
    Magdon-Ismail, M.: No free lunch for noise prediction. Neural Comput. 12(3), 547–564 (2000).  https://doi.org/10.1162/089976600300015709CrossRefGoogle Scholar
  12. 12.
    Noy, N.F., McGuinness, D.L., et al.: Ontology development 101: a guide to creating your first ontology (2001)Google Scholar
  13. 13.
    UrbanGrowth NSW: Funding cities community of practice. Review and development of a predictive housing price model for the Sydney housing market (2017)Google Scholar
  14. 14.
    Panov, P., Džeroski, S., Soldatova, L.: OntoDM: an ontology of data mining. In: IEEE International Conference on Data Mining Workshops, ICDMW 2008, pp. 752–760 (2008)Google Scholar
  15. 15.
    Rabhi, F., Bandara, M., Namvar, A., Demirors, O.: Big data analytics has little to do with analytics. In: Beheshti, A., Hashmi, M., Dong, H., Zhang, W.E. (eds.) ASSRI 2015/2017. LNBIP, vol. 234, pp. 3–17. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-76587-7_1CrossRefGoogle Scholar
  16. 16.
    Shmueli, G., Koppius, O.R.: Predictive analytics in information systems research. Mis Q. 35, 553–572 (2011)CrossRefGoogle Scholar
  17. 17.
    Suárez-Figueroa, M.C.: NeOn Methodology for building ontology networks: specification, scheduling and reuse. Ph.D. thesis, Informatica (2010)Google Scholar
  18. 18.
    Uschold, M., King, M.: Towards a methodology for building ontologies. Citeseer (1995)Google Scholar
  19. 19.
    Yang, S., Lin, S., Carlson, J.R., Ross Jr., W.T.: Brand engagement on social media: will firms’ social media efforts influence search engine advertising effectiveness? J. Mark. Manag. 32(5–6), 526–557 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.University of New South WalesSydneyAustralia

Personalised recommendations