Spatial query processing for high resolutions

Modern database applications including computer-aided design (CAD), medical imaging, or molecular biology impose new requirements on spatial query processing. Particular problems arise from the need of high resolutions for very large spatial objects, including cars, space stations, planes and industrial plants, and from the design goal to use general purpose database management systems in order to guarantee industrial-strength. In the past two decades, various stand-alone spatial index structures have been proposed but their integration into fully-fledged database systems is problematic. Most of these approaches are based on decomposition of spatial objects leading to replicating index structures. In contrast to common black-and-white decompositions which suffer from the lack of intermediate solutions, we introduce grey approximations as a new and general concept. We demonstrate the benefits of grey approximations in the context of encoding spatial objects by space filling curves resulting in grey interval sequences. Spatial intersection queries are then processed by a filter and refine architecture which, as an important design goal, can purely be expressed by means of the SQL: 1999 standard. Our new High Resolution Indexing (HRI) method can easily be integrated into general purpose DBMSs. The experimental evaluation on real-world test data from car and plane design projects points out that our new concept outperforms competitive techniques that are implementable on top of a standard object-relational DBMS by an order of magnitude with respect to secondary storage space and overall query response time.