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A Proposal for Self-Service OLAP Endpoints for Linked RDF Datasets

Author: M. Hilal
Paper: Hila16a (2016)
Citation: Proceedings of the 20th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2016), Doctoral Consortium, November 19-23, 2016 Bologna, Italy, Springer Verlag, Lecture Notes in Computer Science (LNCS), Volume Title "Knowledge Engineering and Knowledge Management", 2016.
Resources: Copy  (In order to obtain the copy please send an email with subject  Hila16a  to dke.win@jku.at)


Abstract (English):

Leveraging external RDF data for OLAP analysis opens a wide variety of possibilities that enable analysts to gain interesting insights related to their businesses. While statistical linked data are easily accessible to OLAP systems, exploiting non-statistical linked data, such as DBpedia, for OLAP analysis is not trivial. An OLAP system for these data should, on the one hand, take into account the big volume, heterogeneity, graph nature, and semantics of the RDF data. On the other hand, dealing with external RDF data requires a degree of self-sufficiency of the analyst, which could be met via self-service OLAP, without assistance of specialists. In this paper, we argue the need for self-service OLAP endpoints for linked RDF datasets. We review the related literature and sketch an approach. We further discuss research methodology and preliminary results. In particular, we propose the use of multidimensional schemas and analysis graphs over linked RDF datasets, which will empower users to perform self-service OLAP analysis on the linked RDF datasets.

Keywords: Resource Description Framework (RDF), Online Analytical Processing (OLAP), Self-Service Business Intelligence