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dc.contributor.authorVarga, Jovan
dc.contributor.authorVaisman, Alejandro
dc.contributor.authorRomero Moral, Óscar
dc.contributor.authorEtcheverry, Lorena
dc.contributor.authorBach Pedersen, Torben
dc.contributor.authorThomsen, Christian
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació
dc.identifier.citationVarga, J., Vaisman, A., Romero, O., Etcheverry, L., Bach, T., Thomsen, C. Dimensional enrichment of statistical linked open data. "Journal of web semantics", 1 Octubre 2016, vol. 40, p. 22-51.
dc.description.abstractOn-Line Analytical Processing (OLAP) is a data analysis technique typically used for local and well-prepared data. However, initiatives like Open Data and Open Government bring new and publicly available data on the web that are to be analyzed in the same way. The use of semantic web technologies for this context is especially encouraged by the Linked Data initiative. There is already a considerable amount of statistical linked open data sets published using the RDF Data Cube Vocabulary (QB) which is designed for these purposes. However, QB lacks some essential schema constructs (e.g., dimension levels) to support OLAP. Thus, the QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP. In this paper, we focus on the enrichment of an existing QB data set with QB4OLAP semantics. We first thoroughly compare the two vocabularies and outline the benefits of QB4OLAP. Then, we propose a series of steps to automate the enrichment of QB data sets with specific QB4OLAP semantics; being the most important, the definition of aggregate functions and the detection of new concepts in the dimension hierarchy construction. The proposed steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion. The user can enrich the QB data set with QB4OLAP concepts (e.g., full-fledged dimension hierarchies) by choosing among the candidate concepts automatically discovered with the steps proposed. Finally, we conduct experiments with 25 users and use three real-world QB data sets to evaluate our approach. The evaluation demonstrates the feasibility of our approach and shows that, in practice, our tool facilitates, speeds up, and guarantees the correct results of the enrichment process.
dc.format.extent30 p.
dc.subjectÀrees temàtiques de la UPC::Informàtica::Enginyeria del software
dc.subject.lcshSemantic web
dc.subject.lcshLinked data
dc.subject.lcshOLAP technology
dc.subject.otherLinked open data
dc.subject.otherMultidimensional data modeling
dc.subject.otherSemantic web
dc.titleDimensional enrichment of statistical linked open data
dc.subject.lemacWeb semàntica
dc.subject.lemacTecnologia OLAP
dc.contributor.groupUniversitat Politècnica de Catalunya. MPI - Modelització i Processament de la Informació
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorVarga, J.; Vaisman, A.; Romero, O.; Etcheverry, L.; Bach, T.; Thomsen, C.
upcommons.citation.publicationNameJournal of web semantics

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