Data structures for Dynamic Queries: An analytical and experimental evaluation

Dynamic Queries is a querying technique for doing range search on multi-key data sets. It is a direct manipulation mechanism where the query is formulated using graphical widgets and the results are displayed graphically preferably within 100 millisec onds. This paper evaluates four data structures, the multilist, the grid file, k-d tree and the quad tree used to organize data in high speed storage for dynamic queries. The effect of factors like size, distribution and dimensionality of data on the storage o verhead and the speed of search is explored. Analytical models for estimating the storage and the search overheads are presented, and verified to be correct by empirical data. Results indicate that multilists are suitable for small (few thousand points) data sets irrespective of the data distribution. For large data sets the grid files are excellent for uniformly distriubuted data, and trees are good for skewed data distributions. There was not significant difference in performance between the tree st ructures.%X additional reference numbers in the format of the next line Also cross-referenced as CAR-TR-715 Also cross-referenced as ISR-TR-94-47 Also cross-referenced as CS-TR-3133 Also cross-referenced as CAR-TR-685 Also cross-referenced as ISR-TR-93-7

Data Structures for Dynamic Queries: An Analytical and Experimantal Evaluation.

Dynamic Queries is a querying technique for doing range search on multi-key data sets. It is a direct manipulation mechanism where the query is formulated using graphical widgets and the results are displayed graphically preferably within 100 milliseconds. This paper evaluates four data structures, the multilist, the grid file, k-d tree and the quad tree used to organize data in high speed storage for dynamic queries. The effect of factors like size, distribution and dimensionality of data on the storage overhead and the speed of search is explored. Analytical models for estimating the storage and search overheads are presented, and verified to be correct by empirical data. Results indicate that multilists are suitable for small (few thousand points) data sets irrespective of the data distribution. For large data sets the grid files are excellent for uniformly distributed data, and trees are good for skewed data distributions. There was no significant difference in performance between the tree structures. (Also cross-referenced as CAR-TR-685) (Also cross-referenced as ISR-TR-93-73

Implementation and Evaluation of Balanced and Nested Grid (Bang) File Structures

Computinq and Information Science

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