LogLInc: LoG Queries of Linked Open Data Investigator for Cube Design

By avoiding the ‘data not invented here’ syndrome (NIH) (Data not invented here (NIH) syndrome is a mindset that consists in focusing solely on using data created inside the walls of a business (https://​urlz.​fr/​9Yo9)), companies realized the benefit of including external sources in their data cube. In this context, Linked Open Data (LOD) is a promising external source that may contain valuable data and query-logs materializing the exploration of data by end users. Paradoxically, the dataset of this external source is structured whereas logs are “ugly”, and in the case, they are turned into rich structured data, they will contribute to building valuable data cubes. In this paper, we claim that the NIH syndrome must be also considered for query-logs. As a consequence, we propose an approach that investigates the particularity of SPARQL query logs performed on the LOD and augmented by the LOD to discover multidimensional patterns when leveraging and enriching a data cube. To show the effectiveness of our approach, different scenarios are proposed and evaluated using DBpedia.