Spatio-Temporal OLAP Queries Similarity Measure and Algorithm

Spatio-temporal data warehouses store large volumes of consolidated and historized multidimensional data, to be explored and analyzed by various users in order to make the best decision. A spatio-temporal OLAP user interactively navigates a spatio-temporal data cube (Geo-cube) by launching a sequence of spatio-temporal OLAP queries (GeoMDX queries) in order to analyze the data. One important class of spatio-temporal analysis is computing spatio-temporal queries similarity. In this article, the authors focus on assessing the similarity between spatio-temporal OLAP queries in term of their GeoMDX queries. The problem of measuring spatio-temporal OLAP queries similarities has not been studied so far. Therefore, this article aims at filling this gap by proposing a new similarity measure and its corresponding algorithm. The proposed measure and algorithm can be used either in developing query recommendation, personalization systems or speeding-up query evolution. It takes into account the temporal similarity and the basic components of spatial similarity assessment relationships.