George Ren-Zheng Wang

Bio: George Ren-Zheng Wang is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Graph (abstract data type) & Directed graph. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Representation and manipulation of heterogenous graph data objects

Abstract: This dissertation addresses the data modeling, organization, storage, and accessing of a class of data objects called graph data objects. All graph data objects have one thing in common--their underlying graph structure. A graph data object can be mapped into a graph by mapping its components into the nodes and edges of the graph. The study concentrates on the representation and manipulation of graph data objects. In the representation problem, the objective is to find a suitable representation system for modeling and storing graph objects. For the manipulation part, the goal is to design a query language for manipulating graph data objects. A new data model, based on the mathematical concept of graph, is proposed and defined. The model is shown to provide a natural and direct representation for graph objects. In order to query and manipulate graph objects represented in the new data model, this dissertation provides the design and formal specification of a graph manipulation language. The query language generalizes an existing structured query language (the SQL) with the capability to support a wide range of operations on graph objects. The 'physical appearance' of graph objects varies from application to application and depends entirely on application domains. Therefore, there does not exist a single, unique way of displaying all graph objects. This dissertation studies this problem and develops two approaches to the display (layout) of graph objects. The first is a rule-based approach, in which the display characteristics of a class of graph objects are stated in a set of rules. These rules can then be used by an inference engine to derive a layout for any data instance within the graph class. The second approach is based on the idea of repeatedly splitting a graph object into smaller ones until each is small enough to be solved effectively by the first approach. This dissertation also studies the problem of the evaluation and optimization of a component of the new query manipulation language. Algorithms for query evaluation and optimization are developed and implemented. The performance of the algorithms is studied using simulation.

Selected M-Related Dissertations Bibliography

Abstract: The following are citations selected by title and abstract as being related to AI, resulting from a computer search, using BRS Information Technologies, of the Dissertation Abstracts Online database produced by University Microfilms International (UMI).The online file includes abstracts, which are not published in this listing, but the citations below do include reference to the published Dissertation Abstracts International (DAI), which contains the abstracts. Other elements of the citation are author; university, degree, and, if available, number of pages; title; UMI order number and year-month of DAI; and one or more DAI subject descriptors chosen by the author of the dissertation. The listing may include masters abstracts, denoted by MAI (Masters Abstracts International) instead of DAI. References are sorted by subject descriptor; in the event that there is more than one descriptor, the first is used. Within each descriptor, entries are sorted by author.Unless otherwise specified, paper or microform copies of dissertations may be ordered from University Microfilms International, Dissertation Copies, Post Office Box 1764, Ann Arbor, MI 48106; telephone for U.S. (except Michigan, Hawaii, Alaska): 1-800-521-3042, for Canada: 1-800-268-6090. Price lists and other ordering and shipping information are in the introduction to the published DAI. Agriculture, Forestry and Wildlife