The Objects Interaction Graticule for Cardinal Direction Querying in Moving Objects Data Warehouses Ganesh Viswanathan & Markus Schneider ⋆ Department of Computer & Information Science & Engineering University of Florida Gainesville, FL 32611, USA {gv1, mschneid}@cise.ufl.edu Abstract. Cardinal directions have turned out to be very important qualitative spatial relations due to their numerous applications in spatial wayfinding, GIS, qualitative spatial reasoning and in domains such as cognitive sciences, AI and robotics. They are frequently used as selection criteria in spatial queries. Moving objects data warehouses can help to analyze complex multidimensional data of a spatio-temporal nature and to provide decision support. However, currently there is no available method to query for cardinal directions between spatio-temporal objects in data warehouses. In this paper, we introduce the concept of a mov- ing objects data warehouse (MODW) for storing and querying multidimensional spatio-temporal data. Further, we also present a novel two-phase approach to model and query for cardinal directions between moving objects by using the MODW framework. First, we apply a tiling strategy that determines the zone be- longing to the nine cardinal directions of each spatial object at a particular time and then intersects them. This leads to a collection of grids over time called the Objects Interaction Graticule (OIG). For each grid cell, the information about the spatial objects that intersect it is stored in an Objects Interaction Matrix. In the second phase, an interpretation method is applied to these matrices to determine the cardinal direction between the moving objects. These results are integrated into MDX queries using directional predicates. 1 Introduction For more than a decade, data warehouses have been at the forefront of informa- tion technology applications as a way for organizations to effectively use infor- mation for business planning and decision making. The data warehouse contains data that gives information about a particular, decision-making subject instead of about an organization’s ongoing operations (subject-oriented). Data is gath- ered into the data warehouse from a variety of sources and then merged into a coherent whole (integrated). All the data in a data warehouse can be identified with a particular time period (time-variant). Data is periodically added in a data ⋆ This work was partially supported by the National Aeronautics and Space Administration (NASA) under the grant number NASA-AIST-08-0081.