From Requirements to Data Analytics Process: An Ontology-Based Approach
Comprehensively describing data analytics requirements is becoming an integral part of developing enterprise information systems. It is a challenging task for analysts to completely elicit all requirements shared by the organization’s decision makers. With a multitude of data available from e-commerce sites, social media and data warehouses selecting the correct set of data and suitable techniques for an analysis itself is difficult and time-consuming. The reason is that analysts have to comprehend multiple dimensions such as existing analytics techniques, background knowledge in the domain of interest and the quality of available data. In this paper, we propose to use semantic models to represent different spheres of knowledge related to data analytics space and use them to assist in analytics requirements definition. By following this approach users can create a sound analytics requirements specification, linked with concepts from the operation domain, available data, analytics techniques and their implementations. Such requirements specifications can be used to drive the creation and management of analytics solutions, well aligned with organizational objectives. We demonstrate the capabilities of the proposed method by applying on a data analytics project for house price prediction.
KeywordsAnalytics process Requirements Ontology
We are grateful to Capsifi, especially Dr. Terry Roach, for sponsoring the research which led to this paper.
- 3.Bandara, M., Rabhi, F.A.: Semantic modeling for engineering data analytic solutions (2018, Under Review)Google Scholar
- 4.Bandara, M., Rabhi, F.A., Meymandpour, R.: Semantic model based approach for knowledge intensive processes. In: Stamelos, I., O’Connor, R.V., Rout, T., Dorling, A. (eds.) SPICE 2018. CCIS, vol. 918, pp. 215–229. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00623-5_15CrossRefGoogle Scholar
- 5.Behnaz, A., Bandara, M., Rabhi, F.A., Maurice, P.: A statistical learning ontology for managing analytics knowledge. In: Proceedings of Workshop on Enterprise Applications, Markets and Services in the Finance Industry (2018)Google Scholar
- 6.Bellatreche, L., Khouri, S., Berkani, N.: Semantic data warehouse design: from ETL to deployment à la Carte. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) DASFAA 2013. LNCS, vol. 7826, pp. 64–83. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37450-0_5CrossRefGoogle Scholar
- 7.Brijs, B.: Business Analysis for Business Intelligence. Auerbach Publications, Boca Raton (2016)Google Scholar
- 8.Colazzo, D., Goasdoué, F., Manolescu, I., Roatiş, A.: RDF analytics: lenses over semantic graphs. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 467–478. ACM (2014)Google Scholar
- 12.Siegemund, K., Thomas, E.J., Zhao, Y., Pan, J., Assmann, U.: Towards ontology-driven requirements engineering. In: Workshop Semantic Web Enabled Software Engineering at 10th International Semantic Web Conference (ISWC), Bonn (2011)Google Scholar
- 13.Taylor, J.: Framing requirements for predictive analytic projects with decision modeling (2015)Google Scholar
- 14.Wiegers, K., Beatty, J.: Business analytic projects. Software Requirements (2013)Google Scholar