Ken J. McDonell's research while affiliated with Monash University (Australia) and other places
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Publications (2)
In spatial database systems, data objects are of non-zero size and are associated with space coordinates. Efficient query retrieval on spatial relationships relies upon auxiliary data structures to support spatial indexing of these objects. The underlying indexing structures must support efficient spatial operations, such as locating the neighbors...
A method is presented for extending a conventional DBMS (database
management system) for geographic applications. The interface language
SQL is augmented to allow formulation of queries involving both spatial
and nonspatial selection criteria. A novel indexing structure is
supported to facilitate query retrieval that is based on spatial
proximity....
Citations
... During this process, the evaluation and adaptation of query languages for retrieving geometries (Frank 1982) and several proposals for indexing spatial data structures (e.g., Stonebraker et al. 1983, Guttman 1984 were also significant milestones. These works evolved into the Dual (Schilcher 1985, Ooi et al. 1989, Aref and Samet 1991 and Integrated architectures (Dayal et al. 1987). The latter represented a crucial instant in the development of spatial database architectures and resulted in several Spatial Database Management Systems (SDMS) such as PROBE (Orenstein 1986, Orenstein andManola 1988) and POSTGRES (Stonebraker and Rowe 1986). ...
Reference: A Survey of Modelling Trends in Temporal GIS
... Other variants of Kd-tree worth mentioning are K-d-B-Trees due to [16], hB-Trees due to [17,18], Extended Kd-tree due to [19], BD-Tree due to [20], SKD-Tree due to [21], GBD-Tree due to [22], LSD-Tree due to [23], KD2B-Tree due to [24], G-Tree due to [25]. The k-d-B-Trees exhibit a forced split effect, which does not allow one to give any space utilization guarantees. ...
