Towards Analytical MD Stars from Linked Data
Victoria Nebot, Rafael Berlanga · 2014
While the Linked Data (LD) initiative has given place to open, large amounts of semi-structured and rich data published on the Web, effective analytical tools that go beyond browsing and querying are still lacking. To address this issue, we propose the automatic generation of multidimensional (MD) analytical stars. The success of the MD model for data analysis has been in great part due to its simplicity. Therefore, in this paper we aim at automatically discovering MD conceptual patterns that summarize LD. These patterns resemble the MD star schema typical of relational data warehousing. Our method is based on probabilistic graphical models and makes use of the statistics about the instance data to generate the MD stars. We present a first implementation, and the preliminary results with large LD sets are encouraging to further work in this direction.