object-relational management of complex geographical objects
Modern database applications including computer-aided design, multimedia information systems, medical imaging, molecular biology, or geographical information systems impose new requirements on the effective and efficient management of spatial data. Particular problems arise from the need of high resolutions for large spatial objects 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 the 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 gray intervals which are stored in a spatial index. Additionally, we store the exact information of these gray intervals in a compressed way. These gray intervals are created by using a cost-based decompositioning algorithm which takes the access probability and the decompression cost of them into account. Furthermore, we exploit statistical information of the database objects to find a cost-optimal decomposition of the query objects. The experimental evaluation on the SEQUOIA benchmark test points out that our new concept outperforms the Relational Interval Tree by more than one order of magnitude with respect to overall query response time.
spatial object decomposition object-relational database data management