Abstract
Nowadays organizations should handle a huge amount of both internal and external data from structured, semi-structured, and unstructured sources. This constitutes a major challenge (and also an opportunity) to current Business Intelligence solutions. The complexity and effort required to analyse such plethora of data implies considerable execution times. Besides, the large number of data analysis methods and techniques impede domain experts (laymen from an IT-assisted analytics perspective) to fully exploit their potential, while technology experts lack the business background to get the proper questions. In this work, we present a semantically-boosted platform for assisting layman users in (i) extracting a relevant subdataset from all the data, and (ii) selecting the data analysis technique(s) best suited for scrutinising that subdataset. The outcome is getting better answers in significantly less time. The platform has been evaluated in the music domain with promising results.
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References
Gantz, J., Reinsel, D.: The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. In: McAfee, A., Brynjolfsson, E. (eds.) IDC iView: IDC Analyze the Future, vol. 2007, pp. 1–16 (2012)
McAfee, A., Brynjolfsson, E.: Big Data: The Management Revolution. Harvard Business Review 90(10), 60–68 (2012)
Ruiz-Martínez, J.M., Valencia-García, R., Martínez-Béjar, R., Hoffmann, A.G.: BioOnto-Verb: A top level ontology based framework to populate biomedical ontologies from texts. Knowl.-Based Syst. 36, 68–80 (2012)
Rodríguez-González, A., Alor-Hernández, G.: An approach for solving multi-level diagnosis in high sensitivity medical diagnosis systems through the application of semantic technologies. Computers in Biology and Medicine 43(1), 51–62 (2013)
Hernández-González, Y., García-Moreno, C., Rodríguez-García, M.Á., Valencia-García, R., García-Sánchez, F.: A semantic-based platform for R&D project funding management. Comput. Ind. 65(5), 850–861 (2014)
Rodríguez-García, M.Á., Valencia-García, R., García-Sánchez, F., Samper-Zapater, J.J.: Ontology-based annotation and retrieval of services in the cloud. Knowl.-Based Syst. 56, 15–25 (2014)
Rodríguez-García, M.Á., Valencia-García, R., García-Sánchez, F., Samper-Zapater, J.J.: Creating a semantically-enhanced cloud services environment through ontology evolution. Future Gener. Comput. Syst. 32, 295–306 (2014)
Cheng, H., Lu, Y.C., Sheu, C.: An ontology-based business intelligence application in a financial knowledge management system. Expert Systems with Applications 36(2), 3614–3622 (2009)
Raimond, Y., Abdallah, S.A., Sandler, M.B., Giasson, F.: The music ontology. In: ISMIR 2007, pp. 417–422 (2007)
Keet, C.M., Ławrynowicz, A., d’Amato, C., Kalousis, A., Nguyen, P., Palma, R., Stevens, R., Hilario, M.: The data mining OPtimization ontology. Web Semantics: Science, Services and Agents on the World Wide Web 32, 43–53 (2009)
Jareevongpiboon, W., Janecek, P.: Ontological approach to enhance results of business process mining and analysis. Business Process Management Journal 19(3), 459–476 (2013)
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Rodríguez-García, M.Á., Medina-Moreira, J., Lagos-Ortiz, K., Luna-Aveiga, H., García-Sánchez, F., Valencia-García, R. (2016). Ontology-Based Platform for Conceptual Guided Dataset Analysis. In: Omatu, S., et al. Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-319-40162-1_17
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DOI: https://doi.org/10.1007/978-3-319-40162-1_17
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