On the consistency of spatial semantic integrity constraints

Geographical data are the core of any Geographical Information System (GIS) and any Geographic Information (GI) application. Because of the increasing use of decentrally held data and networked services, detailed knowledge about the existing data (i.e., its origin, structure, formats, quality, availability and reference applications) becomes more and more important. The availability of such metadata and the evaluation of the fitness for use based on these metadata are vital. With this thesis the author intents to contribute to the development of meaningful and machine-interpretable quality descriptions of GI. The work focuses on semantic integrity constraints (SIC). In general, integrity constraints define basic assumptions on the part of real world, which is represented by the data. They enable to detect inconsistencies, that is, unacceptable differences between the data and the data model. SICs are defined as specific integrity constraints, whose defined restrictions are based on the semantics of the modelled entities. They reflect business, legal and other required rules and regulations in the database. For spatial data, many SICs are based on spatial properties like topological or metric relations. Reasoning on such spatial relations and the corresponding derivation of implicit knowledge allow for many interesting applications. Currently the potential of SICs is far from being exploited and SICs are hardly supported by available GISs or spatial database systems. Their effective use mainly requires a formal description of the constraints that enables to transfer and compare the sets of SICs of different data sources. This thesis contributes to the second requirement. Currently, there is no solution for the comparison of SICs pairs and the detection of any conflicts or redundancies in sets of SICs. This also required the inference of implicit restrictions defined by the SICs. In consequence, the quality assurance of a data set is possibly more extensive than necessary, because sets of SICs might define redundant restrictions, the integration of SICs sets from multiple data sources is impossible and the assessment of the fitness for use based on the SICs cannot be supported. These are significant shortcomings for quality assurance and the knowledge sharing within the frame of spatial data infrastructures.