DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Transforming statistical linked data for use in OLAP systems
Proceedings of the 7th International Conference on Semantic Systems
Enhancing OLAP analysis with web cubes
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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Growing amount of data are being published online in machinereadable formats, and LOD (Linked Open Data) has emerged as a way to share such data across Web resources. Since LOD data often contain numerical data, such as statistics, there is a growing demand to make OLAP (Online Analytical Processing) analysis over such data. To make it possible to apply off-the-shelf OLAP systems for analyzing LOD data, we propose a framework to streamline the Extract, Transform, and Load (ETL) process from LOD to multidimensional data models for OLAP. Unlike other related approaches, our framework does not require RDF vocabularies dedicated for specifying multidimensional model for OLAP. Instead, given an LOD dataset, we exploit the relationships among entities and external information in the referenced LOD to generate an OLAP data model. In a case study, we demonstrate that our framework can extract OLAP data models from different kinds of real LOD datasets.