Besides topological relationships and approximate relationships, cardinal directions like north and southwest have turned out to be an important class of qualitative spatial relationships. They are of interdisciplinary interest in fields like cognitive science, robotics, artificial intelligence, and qualitative spatial reasoning. In spatial databases and Geographic Information Systems (GIS) they are frequently used as join and selection criteria in spatial queries. However, the available computational models of cardinal directions suffer from a number of problems like the use of too coarse approximations of the two spatial operand objects in terms of single representative points or minimum bounding rectangles, the lacking property of converseness of the cardinal directions computed, and the limited applicability to simple instead of complex regions only. This article proposes and formally defines a novel two-phase model, called the Objects Interaction Matrix (OIM ) model, that solves these problems, and determines cardinal directions for even complex regions. The model consists of a tiling phase and an interpretation phase. In the tiling phase, a tiling strategy first determines the zones belonging to the nine cardinal directions of each individual region object and then intersects them. The result leads to a bounded grid called objects interaction grid. For each grid cell the information about the region objects that intersect it is stored in an objects interaction matrix. In the subsequent interpretation phase, a well defined interpretation method is applied to such a matrix and determines the cardinal direction. Spatial example queries illustrate our new cardinal direction concept that is embedded in a spatial extension of SQL and provides user-defined cardinal direction predicates.

Abstract. Besides topological relations and approximate relations, cardinal directions have turned out to be an important class of qualitative spatial relations. In spatial databases and GIS they are frequently used as selection criteria in spatial queries. But the available models of cardinal relations suffer from a number of problems like the unequal treatment of the two spatial objects as arguments of a cardinal direction relation, the use of too coarse approximations of the two spatial operand objects in terms of single representative points or minimum bounding rectangles, the lacking property of converseness of the cardinal directions computed, the partial restriction and limited applicability to simple spatial objects only, and the computation of incorrect results in some cases. This paper proposes a novel two-phase method that solves these problems and consists of a tiling phase and an interpretation phase. In the first phase, a tiling strategy first determines the zones belonging to the nine cardinal directions of each spatial object and then intersects them. The result leads to a bounded grid called objects interaction grid. 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 such a matrix and determines the cardinal direction. These results are integrated into spatial queries using directional predicates.

Abstract. Data warehouses and OLAP systems help to analyze complex multidimensional data and provide decision support. With the availability of large amounts of spatial data in recent years, several new models have been proposed to enable the integration of spatial data in data warehouses and to help analyze such data. This is often achieved by a combination of GIS and spatial analysis tools with OLAP and database systems, with the primary goal of supporting spatial analysis dimensions, spatial measures and spatial aggregation operations. However, this poses several new challenges related to spatial data modeling in a multidimensional context, such as the need for new spatial aggregation operations and ensuring consistent and valid results. In this paper, we review the existing modeling strategies for spatial data warehouses and SOLAP in all three levels: conceptual, logical and implementation. While studying these models, we gather the most essential requirements for handling spatial data in data warehouses and use insights from spatial databases to provide a "meta-framework" for modeling spatial data warehouses. This strategy keeps the user as the focal point and achieves a clear abstraction of the data for all stakeholders in the system. Our goal is to make analysis more user-friendly and pave the way for a clear conceptual model that defines new multidimensional abstract data types (ADTs) and operations to support spatial data in data warehouses.

Abstract. New emerging applications including genomic, multimedia, and geospatial technologies have necessitated the handling of complex application objects that are highly structured, large, and of variable length. Currently, such objects are handled using filesystem formats like HDF and NetCDF as well as the XML and BLOB data types in databases. However, some of these approaches are very application specific and do not provide proper levels of data abstraction for the users. Others do not support random updates or cannot manage large volumes of structured data and provide their associated operations. In this paper, we propose a novel two-step solution to manage and query application objects within databases. First, we present a generalized conceptual framework to capture and validate the structure of application objects by means of a type structure specification. Second, we introduce a novel data type called Intelligent Binary Large Object (iBLOB) that leverages the traditional BLOB type in databases, preserves the structure of application objects, and provides smart query and update capabilities. The iBLOB framework generates a type structure specific application programming interface (API) that allows applications to easily access the components of complex application objects. This greatly simplifies the ease with which new type systems can be implemented inside traditional DBMS.

The increased availability of spatial data in recent years has lead to new challenges in the analysis of large multidimensional datasets. One solution is to integrate GIS with OLAP and relational databases. Another strategy has been to leverage existing spatial capabilities of databases to perform spatial OLAP. In this article, we review existing modelling strategies for spatial data warehousing at all three levels: conceptual, logical and implementation. We gather the most essential requirements for handling spatial data and use insights from spatial databases and GIS systems to design a meta-framework that would enable a user-centric modelling of complex data. Our strategy is to keep the user as the focal point in the analysis process and lay the foundation for clear data abstraction at different levels using multidimensional abstract data types and operations and thus support complex spatial data in data warehouses.