Enhance DBMS capabilities using semantic data modelling approach.

by Yip Wai Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 1990.Bibliography: leaves 132-135.ABSTRACTACKNOWLEDGEMENTSPART IChapter 1 --- OVERVIEW ON SEMANTIC DATA MODELLING APPROACH … --- p.1Chapter 2 --- SCOPE OF RESEARCH --- p.4Chapter 3 --- CONCEPTUAL STRUCTURE OF SAM* --- p.7Chapter 3.1 --- Concepts and Associations --- p.7Chapter 3.1.1 --- Membership Association --- p.8Chapter 3.1.2 --- Aggregation Association --- p.8Chapter 3.1.3 --- Generalization Association --- p.9Chapter 3.1.4 --- Interaction Association --- p.10Chapter 3.1.5 --- Composition Association --- p.11Chapter 3.1.6 --- Cross-Product Association --- p.12Chapter 3.1.7 --- Summary Association --- p.13Chapter 3.2 --- An Example --- p.14Chapter 3.3 --- Occurrences --- p.15PART IIChapter 4 --- SYSTEM OVERVIEW --- p.17Chapter 4.1 --- System Objectives --- p.17Chapter 4.1.1 --- Data Level --- p.17Chapter 4.1.2 --- Meta-Data Level --- p.18Chapter 4.2 --- System Characteristics --- p.19Chapter 4.3 --- Design Considerations --- p.20Chapter 5 --- IMPLEMENTATION CONSIDERATIONS --- p.23Chapter 5.1 --- Introduction --- p.23Chapter 5.2 --- Data Definition Language for Schema --- p.24Chapter 5.3 --- Construction of Directed Acyclic Graph --- p.27Chapter 5.4 --- Query Manipulation Language --- p.28Chapter 5.4.1 --- Semantic Manipulation Language --- p.29Chapter 5.4.1.1 --- Locate Concepts --- p.30Chapter 5.4.1.2 --- Retrieve Information About Concepts --- p.30Chapter 5.4.1.3 --- Find a Path Between Two Concepts --- p.31Chapter 5.4.2 --- Occurrence Manipulation Language --- p.32Chapter 5.5 --- Examples --- p.35Chapter 6 --- RESULTS AND DISCUSSIONS --- p.41Chapter 6.1 --- Allow Non-Homogeneity of Facts about Entities --- p.41Chapter 6.2 --- Field Name is Information --- p.42Chapter 6.3 --- Description of Group of Information --- p.43Chapter 6.4 --- Explicitly Description of Interaction --- p.43Chapter 6.5 --- Information about Entities --- p.44Chapter 6.6 --- Automatically Joining Tables --- p.45Chapter 6.7 --- Automatically Union Tables --- p.45Chapter 6.8 --- Automatically Select Tables --- p.46Chapter 6.9 --- Ambiguity --- p.47Chapter 6.10 --- Normalization --- p.47Chapter 6.11 --- Update --- p.50PART IIIChapter 7 --- SCHEMA VERIFICATION --- p.55Chapter 7.1 --- Introduction --- p.55Chapter 7.2 --- Need of Schema Verification --- p.57Chapter 7.3 --- Integrity Constraint Handling Vs Schema Verification --- p.58Chapter 8 --- AUTOMATIC THEOREM PROVING --- p.60Chapter 8.1 --- Overview --- p.60Chapter 8.2 --- A Discussion on Some Automatic Theorem Proving Methods --- p.61Chapter 8.2.1 --- Resolution --- p.61Chapter 8.2.2 --- Natural Deduction --- p.63Chapter 8.2.3 --- Tableau Proof Methods --- p.65Chapter 8.2.4 --- Connection Method --- p.67Chapter 8.3 --- Comparison of Automatic Theorem Proving Methods --- p.70Chapter 8.3.1 --- Proof Procedure --- p.70Chapter 8.3.2 --- Overhead --- p.70Chapter 8.3.3 --- Unification --- p.71Chapter 8.3.4 --- Heuristics --- p.72Chapter 8.3.5 --- Getting Lost --- p.73Chapter 8.4 --- The Choice of Tool for Schema Verification --- p.73Chapter 9 --- IMPROVEMENT OF CONNECTION METHOD --- p.77Chapter 9.1 --- Motivation of Improving Connection Method --- p.77Chapter 9.2 --- Redundancy Handled by the Original Algorithm --- p.78Chapter 9.3 --- Design Philosophy of the Improved Version --- p.82Chapter 9.4 --- Primary Connection Method Algorithm --- p.83Chapter 9.5 --- AND/OR Connection Graph --- p.89Chapter 9.6 --- Graph Traversal Procedure --- p.91Chapter 9.7 --- Elimination Redundancy Using AND/OR Connection Graph --- p.94Chapter 9.8 --- Further Improvement on Graph Traversal --- p.96Chapter 9.9 --- Comparison with Original Connection Method Algorithm --- p.97Chapter 9.10 --- Application of Connection Method to Schema Verification --- p.98Chapter 9.10.1 --- Express Constraint in Well Formed Formula --- p.98Chapter 9.10.2 --- Convert Formula into Negation Normal Form --- p.101Chapter 9.10.3 --- Verification --- p.101PART IVChapter 10 --- FURTHER DEVELOPMENT --- p.103Chapter 10.1 --- Intelligent Front-End --- p.103Chapter 10.2 --- On Connection Method --- p.104Chapter 10.3 --- Many-Sorted Calculus --- p.104Chapter 11 --- CONCLUSION --- p.107APPENDICESChapter A --- COMPARISON OF SEMANTIC DATA MODELS --- p.110Chapter B --- CONSTRUCTION OP OCCURRENCES --- p.111Chapter C --- SYNTAX OF DDL FOR THE SCHEMA --- p.113Chapter D --- SYNTAX OF SEMANTIC MANIPULATION LANGUAGE --- p.116Chapter E --- TESTING SCHEMA FOR FUND INVESTMENT DBMS --- p.118Chapter F --- TESTING SCHEMA FOR STOCK INVESTMENT DBMS --- p.121Chapter G --- CONNECTION METHOD --- p.124Chapter H --- COMPARISON BETWEEN RESOLUTION AND CONNECTION METHOD --- p.128REFERENCES --- p.13

Workshop on Database Programming Languages

These are the revised proceedings of the Workshop on Database Programming Languages held at Roscoff, Finistère, France in September of 1987. The last few years have seen an enormous activity in the development of new programming languages and new programming environments for databases. The purpose of the workshop was to bring together researchers from both databases and programming languages to discuss recent developments in the two areas in the hope of overcoming some of the obstacles that appear to prevent the construction of a uniform database programming environment. The workshop, which follows a previous workshop held in Appin, Scotland in 1985, was extremely successful. The organizers were delighted with both the quality and volume of the submissions for this meeting, and it was regrettable that more papers could not be accepted. Both the stimulating discussions and the excellent food and scenery of the Brittany coast made the meeting thoroughly enjoyable. There were three main foci for this workshop: the type systems suitable for databases (especially object-oriented and complex-object databases,) the representation and manipulation of persistent structures, and extensions to deductive databases that allow for more general and flexible programming. Many of the papers describe recent results, or work in progress, and are indicative of the latest research trends in database programming languages. The organizers are extremely grateful for the financial support given by CRAI (Italy), Altaïr (France) and AT&T (USA). We would also like to acknowledge the organizational help provided by Florence Deshors, Hélène Gans and Pauline Turcaud of Altaïr, and by Karen Carter of the University of Pennsylvania