Abstract. In this paper, we present a spatial logic which can be used to reason about topological and spatial relationships among objects in spatial databases. The main advantages of such a formalism are its rigorousness, clear semantics and sound inference mechanism. We also show how the formalism can be extended to include orientation and metrical information. Comparisons with other formalisms are discussed. I n t r o d u c t i o nA formal theory of space and time has always been an important issue in Artificial Intelligence. Recently, its importance in spatial databases has been recognized (Egenhofer 1989, Egenhofer and Herring 1990, and Pullar and Egenhofer 1988. Advances of database technology have required a database not only to store, to retrieve and to update data, but also to reason about the relationships among its data, and to have production rules and triggers. A formal theory of data models is important in securing the data consistency in such situations. A deductive database should allow its users to formulate complex queries based on simple facts (relations), otherwise it is difficult to meet the requirements of many applications.Geographic information systems, image data bases, and CAD/CAM systems are often based upon the relationships among spatial objects. Although some query languages support queries with some spatial relationships; however, the diversity, semantics, completeness and terminology of these relationships vary dramatically (eg Egenhofer and Frank 1988, Roussopoulos, Faloutsos and Sellis 1988, Guenther and Buchmann 1990, Giiting 1988. In general, the underlying basis of most existing spatial databases seems to be that of point set geometry, perhaps with some application specific ontology in addition (eg Rawlings 1985).However, many explanations of phenomena and descriptions of the relationship between objects in informal discourse appeal to relatively high level qualitative spatial information, in particular, topological information. Much of this , This work has been partially funded by the SERC under grant no. GR/G36852

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Analysing data to predict market trends of products and services and to improve performances of enterprise business systems has always been part of running a competitive business. But it is becoming essential nowadays that not only is the analysis done on real-time data, but also actions in response to analysis results can be performed in real time and instantaneously change parameters of business processes. This paper discusses issues and problems of current business intelligence systems, and then outlines our vision of future real-time business intelligence. We present a list of emerging technologies which could contribute to the realisation of real-time business intelligence and some examples of applying them to improve BT's systems and services.

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