Spatio-temporal data warehouses store enormous amount of data. They are usually exploited by spatio-temporal OLAP systems to extract relevant information. For extracting interesting information, the current user launches spatio-temporal OLAP (ST-OLAP) queries to navigate within a geographic data cube (Geo-cube). Very often choosing which part of the Geo-cube to navigate further, and thus designing the forthcoming ST-OLAP query, is a difficult task. So, to help the current user refine his queries after launching in the geo-cube his current query, we need a ST-OLAP queries suggestion by exploiting a Geo-cube. However, models that focus on adapting to a specific user can help to improve the probability of the user being satisfied. In this chapter, first, the authors focus on assessing the similarity between spatio-temporal OLAP queries in term of their GeoMDX queries. Then, they propose a personalized query suggestion model based on users' search behavior, where they inject relevance between queries in the current session and current user' search behavior into a basic probabilistic model.
TopIntroduction
Spatio-Temporal data warehouse has recently become an active research area. This is due to the explosive growth in the use of the recent ubiquitous location technologies devices (Vaisman et al., 2014) such as GPS, smart phones, PDA, etc. The concept of a spatio-temporal data warehouse appeared in order to store moving data objects and temporal data information. Moving objects are geometries that change their position and shape continuously over the time; the time could be an instant or a set of time intervals. In order to support spatio-temporal data, a data model and associated query language is needed for supporting moving objects.
Spatio-Temporal data warehouse stores large volumes of consolidation and historized multidimensional data, to be explored and analyzed by various users in order to make the best decision. In order to analyze and explore a spatio-temporal data warehouse, users need a Spatio-Temporal OLAP (ST-OLAP) system to help them to make the best decisions. The ST-OLAP system is realized in order to analyze and explore spatio-temporal data warehouse. A ST-OLAP explores a spatio-temporal data warehouse, which we find a moving data type for the movement of an object in the time. It's obtained after the combination of Geographic Information Systems (GIS) with OLAP tools and operations. A ST-OLAP system is the enhancement for an OLAP system to consider moving objects, so it takes into account the spatial objects which evolve over time. For the exploration of a data cube, the user should have operations and a manipulation language. Bardar (Badard, 2011) defined that the language used for a spatial data warehouse and a spatio-temporal data warehouse is the MDX (Multi-dimensional eXpressions) with spatial functions language, called also GeoMDX. In this case, a query can contain spatial, no spatial and temporal data types. The GeoMDX language represents the evolution of MDX language to support spatial relationships: topological, direction and metric distance relationships and temporal data. Users interactively navigate a spatio-temporal data cube (Geo-cube) by launching sequences of ST-OLAP queries over a spatio-temporal data warehouse. The problem appeared when the current user may have no idea of what the forthcoming ST-OLAP queries should be and if it's relevant for him or not. Adding to that, spatio-temporal data cubes store a big amount of data that have become increasingly complex to be explored and analyzed. The notion of similarity has been considered as an important component for the development of recommendation and personalization systems. Personalization has been mentioned for many years in various domains such as information retrieval, web search, e-commerce, query suggestions in databases and data warehouses. In these domains, personalization usually consists of exploiting user preferences to provide pertinent answers to users. In our chapter, we focus on providing users with queries suggestion based on the exploration of a spatio-temporal data warehouse in order to obtain pertinent set of ST-OLAP queries results to address their information needs. Queries suggestion help users refine their queries after they input an initial query. Previous work mainly concentrated on similarity-based and context-based query suggestion approaches. However, models that focus on adapting to a specific user can help to improve the probability of the user being satisfied. In this chapter, we propose a personalized ST-OLAP queries suggestion model based on users' search behavior (UB model), where we inject relevance between queries and users' search behavior into a basic probabilistic model. For that purpose and as a solution for helping the current user in his navigation and his exploration, in this chapter, we propose a behavior-based model for ST-OLAP queries suggestion personalization that incorporates the current user's short-term search context in his current session to detect search interests. But before, we need a similarity measure to compare between ST-OLAP queries. In our context, similarity measures are used to identify the degree of similarity between two ST-OLAP queries. To the best of our knowledge, there is no proposed similarity measure between ST-OLAP queries (GeoMDX queries (Tranchant, 2011)) and no proposed approach for ST-OLAP queries suggestion personalization. So, in this chapter, we aim at filling this gap.