@conference {13332,
	title = {Spatial indexing on tetrahedral meshes},
	booktitle = {Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems},
	series = {GIS {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {506 - 509},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {We address the problem of performing spatial queries on tetrahedral meshes. These latter arise in several application domains including 3D GIS, scientific visualization, finite element analysis. We have defined and implemented a family of spatial indexes, that we call tetrahedral trees. Tetrahedral trees subdivide a cubic domain containing the mesh in an octree or 3D kd-tree fashion, with three different subdivision criteria. Here, we present and compare such indexes, their memory usage, and spatial queries on them.},
	keywords = {kd-trees, octrees, spatial indexes, tetrahedral meshes},
	isbn = {978-1-4503-0428-3},
	doi = {10.1145/1869790.1869873},
	url = {http://doi.acm.org/10.1145/1869790.1869873},
	author = {De Floriani, Leila and Fellegara,Riccardo and Magillo,Paola}
}
@article {16884,
	title = {Object-based and image-based object representations},
	journal = {ACM Comput. Surv.},
	volume = {36},
	year = {2004},
	month = {2004/06//},
	pages = {159 - 217},
	abstract = {An overview is presented of object-based and image-based representations of objects by their interiors. The representations are distinguished by the manner in which they can be used to answer two fundamental queries in database applications: (1) Feature query: given an object, determine its constituent cells (i.e., their locations in space). (2) Location query: given a cell (i.e., a location in space), determine the identity of the object (or objects) of which it is a member as well as the remaining constituent cells of the object (or objects). Regardless of the representation that is used, the generation of responses to the feature and location queries is facilitated by building an index (i.e., the result of a sort) either on the objects or on their locations in space, and implementing it using an access structure that correlates the objects with the locations. Assuming the presence of an access structure, implicit (i.e., image-based) representations are described that are good for finding the objects associated with a particular location or cell (i.e., the location query), while requiring that all cells be examined when determining the locations associated with a particular object (i.e., the feature query). In contrast, explicit (i.e., object-based) representations are good for the feature query, while requiring that all objects be examined when trying to respond to the location query. The goal is to be able to answer both types of queries with one representation and without possibly having to examine every cell. Representations are presented that achieve this goal by imposing containment hierarchies on either space (i.e., the cells in the space in which the objects are found), or objects. In the former case, space is aggregated into successively larger-sized chunks (i.e., blocks), while in the latter, objects are aggregated into successively larger groups (in terms of the number of objects that they contain). The former is applicable to image-based interior-based representations of which the space pyramid is an example. The latter is applicable to object-based interior-based representations of which the R-tree is an example. The actual mechanics of many of these representations are demonstrated in the VASCO JAVA applets found at http://www.cs.umd.edu/\~{}hjs/quadtree/index.html.},
	keywords = {Access methods, feature query, geographic information systems (GIS), image space, location query, object space, octrees, Pyramids, quadtrees, R-trees, space-filling curves, Spatial databases},
	isbn = {0360-0300},
	doi = {10.1145/1031120.1031123},
	url = {http://doi.acm.org/10.1145/1031120.1031123},
	author = {Samet, Hanan}
}
