Dynamic Addition and Removal of Attributes in Bang Files�

Computer Scienc

Index of Dimensional Projection : an Index Supporting Search for Spatial Objects by Region�

In image databases and other spatial data retrieval systems, the techniques for storing and indexing data objects require different kinds of search and query from those in traditional databases and data retrieval systems. In order to handle spatial data more efficiently, a new index structure supporting search for spatial objects by region, the Index by Dimensional Projection is proposed in this thesis. By this method, the number of pages accessed for searching a point region has a logarithmic relationship with the number of objects in data space and the number of comparisons required for searching an entry within a disk page has logarithmic relationship with the number of entries in the disk page.Computer Scienc

Geometric Issues in Spatial Indexing

We address a number of geometric issues in spatial indexes. One area of interest is spherical data. Two main examples are the locations of stars in the sky and geodesic data. The first part of this dissertation addresses some of the challenges in handling spherical data with a spatial database. We show that a practical approach for integrating spherical data in a conventional spatial database is to use a suitable mapping from the unit sphere to a rectangle. This allows us to easily use conventional two-dimensional spatial data structures on spherical data. We further describe algorithms for handling spherical data. In the second part of the dissertation, we introduce the areal projection, a novel projection which is computationally efficient and has low distortion. We show that the areal projection can be utilized for developing an efficient method for low distortion quantization of unit normal vectors. This is helpful for compact storage of spherical data and has applications in computer graphics. We introduce the QuickArealHex algorithm, a fast algorithm for quantization of surface normal vectors with very low distortion. The third part of the dissertation deals with a CPU time analysis of TGS, an R-tree bulkloading algorithm. And finally, the fourth part of the dissertation analyzes the BV-tree, a data structure for storing multi-dimensional data on secondary storage. Contrary to the popular belief, we show that the BV-tree is only applicable to binary space partitioning of the underlying data space

New Efficient Spatial Index Structures, PML-Tree and SMR-Tree, for Spatial Databases

Computer Scienc

Heuristics and multi-dimensional physical database design

An expert system approach has recently been used in parameter selection for VSAM (Virtual Storage Access Method) file organisation [AL87a]. This system has been developed to aid in-house users to apply relevant facts and heuristics to optimise VSAM file design. Multi-dimensional physical database design is more sophisticated and complicated than VSAM file design. The expert system approach can be applied to select and tune physical database design for various applications. A great deal of work has been done in developing diverse algorithms or access methods to organise automated information on secondary storage devices [FA86b] [FR86] [FR88] [GU84] [HU88a] [KS88a] [KS86] [L087] [NI84] [OR88b] [OR86] [OT85] [R081], etc. However, little work has been done to enable designers to select an access method which matches a projected application profile (features and requirements) and perceived strengths and weaknesses of candidate algorithms. This thesis considers a number of grid based algorithms and makes expert assessments of each according to its strengths and weaknesses. It analyses features of various access methods and using expert knowledge matches features for a range of m-d (multi­ dimensional) algorithms with corresponding characteristics of an application. The knowledge-based system presented in this thesis can be applied either manually or computerised to give a systematic approach to m-d algorithm selection. A system is proposed to (1) heuristically select an initial algorithm; (2) describe how the selection process is evaluated against actual m-d algorithm performance and (3) show how the results of the evaluation can be used to refine expert knowledge embodied in the selection system. Heuristic assessments are given for several m-d access algorithms. Examples are presented to show how these heuristics are used to select a m-d access algorithm for a specific application. It is reasonable to suppose that the initial heuristic assessments are not entirely accurate. A tuning mechanism for the system heuristics is given in section 4.9. The system selection process is thereby, able to adjust to real world results. Finally, we present a simple example to illustrate how the proposed system works