AuthorsRodriguez-Garcia, Miguel Angel
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2016-06-01
Print Publication Date2016
Permanent link to this recordhttp://hdl.handle.net/10754/622151
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AbstractNowadays 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.
CitationRodríguez-García MÁ, Medina-Moreira J, Lagos-Ortiz K, Luna-Aveiga H, García-Sánchez F, et al. (2016) Ontology-Based Platform for Conceptual Guided Dataset Analysis. Advances in Intelligent Systems and Computing: 155–163. Available: http://dx.doi.org/10.1007/978-3-319-40162-1_17.
Conference/Event name13th International Conference on Distributed Computing and Artificial Intelligence, DCAI 2016