Dirk Van Gucht

The structure of the relational database model

The book presents an overview of the most fundamental aspects of the theory that underlies the Relational Database Model. It provides exact definitions of widely accepted concepts, and also covers some recent research topics, making the book very suitable in classes on database theory.

A graph-oriented object database model

A simple, graph-oriented database model, supporting object-identity, is presented. For this model, a transformation language based on elementary graph operations is defined. This transformation language is suitable for both querying and updates. It is shown that the transformation language supports both set-operations (except for the powerset operator) and recursive functions.

A graph-oriented object model for database end-user interfaces

The current database research trend 1s towards systems whrch can deal with advanced data apphcatrons that go beyond the standard “enterprise” or “office” database apphcatron This trend 1s reflected m the research on extension architectures [5,18,21] and obJect-oriented databases [2,3,12,21] Along wrth thus trend, the need for better and easier-to-use database end-user Interfaces has been stressed [18,21] Therefore, we propose a graph-based data model, which shares many features wrth exrstmg data models, but which better facrhtates the rigorous study of graphical database end-user interfaces Graphs have been an integral part of the database design process ever since the mtroductron of semantic data models [11,12] Therr usage m data mampulatron languages, however, IS far more sparse To deal wrth data mampulatron, typically, schemes m semantrc data models are transformed mto a conceptual data model such as the relatronal model [19] The required database language features then become those of the conceptual model CbJectoriented data models, on the other hand, often offer computational complete, non-graphical data languages, usually m the style of object-oriented programmmg languages such as Smalltalk Due to their expressrveness, however, these language do not lend themselves easily as high-level data languages [2,21] The first graphical database end-user interfaces were developed for the relational model (for example Zloof’s Query-By-Example (QBE) [22])

Towards a theory of spatial database queries (extended abstract)

A general model for spatial databases is considered, which extends the relational model by allowing as tuple components not only atomic values but also geometrical figures. The model, which is inspired by the work of Kanellakis, Kuper and Revesz on constraint query languages, includes a calculus and an algebra which are equivalent. Given this framework, the concept of spatial database query is investigated. Thereto, Chandra and Harels well-known consistency criterion for classical relational queries is adapted. Various adaptations are proposed, depending on the kinds of geometry in which the spatial information in the database is to be interpreted. The consistency problem for calculus queries is studied. Expressiveness issues are examined. The main purpose of the paper is to open up new grounds for theoretical research in the area of spatial database systems. Consequently, many open problems are indicated.

Converting nested algebra expressions into flat algebra expressions

Nested relations generalize ordinary flat relations by allowing tuple values to be either atomic or set valued. The nested algebra is a generalization of the flat relational algebra to manipulate nested relations. In this paper we study the expressive power of the nested algebra relative to its operation on flat relational databases. We show that the flat relational algebra is rich enough to extract the same “flat information” from a flat database as the nested algebra does. Theoretically, this result implies that recursive queries such as the transitive closure of a binary relation cannot be expressed in the nested algebra. Practically, this result is relevant to (flat) relational query optimization.

Possibilities and limitations of using flat operators in nested algebra expressions

In 19Y7 Makmoucb [18] proposed to generahze the relational database model by removmg the first normal form assumption Jaeschke and Schek [15] mtraduced a generallzatlon of the ordmary relational model by allowmg relations with set-valued attributes and adding two restructuring operators, the nest and the unnest operators, to manipulate such (one-level) nested relations Thomas and Fischer 1261 generalized Jaeschke and Schek’s model and allowed nested relations of arbitrary (but fixed) depth Roth, Korth and Sllberschatz [23] defined a calculus like query language for the nested relational model of Thomas and Flscher Since then numerous SQL-hhe query languages [17,20,21,22], graphical-oriented query languages [13] and datalog-hke languages [3,4,7,16] have been mtroduced for this model or slight generahzatlons of It Also, various groups [5,10,11,12,19,25] have started to implement the nested relational database model, some on top of an exlstmg database management system, others from scratch

Parallel Genetic Algorithms Applied to the Traveling Salesman Problem

Genetic algorithms are adaptive search algorithms that have been shown to be robust optimization algorithms for multimodal real-valued functions and a variety of combinatorial optimization problems. In contrast to more standard search algorithms, genetic algorithms base their progress on the performance of a population of candidate solutions, rather than on a single candidate solution.The authors will concentrate on the application of genetic algorithms to the traveling salesman problem. For this problem, there exist several such algorithms, ranging from pure genetic algorithms to genetic algorithms that incorporate heuristic information. These algorithms will be reviewed and their performance contrasted.A serious drawback of genetic algorithms is their inefficiency when implemented on a sequential machine. However, due to their inherent parallel properties, they can be successfully implemented on parallel machines, resulting in considerable speedup. Parallel genetic algorithms will be reviewed and their ...

The powerset algebra as a result of adding programming constructs to the nested relational algebra

In this paper, we discuss augmentations of the nested relational algebra with programming constructs, such as while-loops and for-loops. We show that the algebras obtained in this way are equivalent to a slight extension of the powerset algebra, thus emphasizing both the strength and the naturalness of the powerset algebra as a tool to manipulate nested relations, and, at the same time, indicating more direct ways to implement this algebra.

An overview of GOOD

GOOD is an acronym, standing for Graph-Oriented Object Database. GOOD is being developed as a joint research effort of Indiana University and the University of Antwerp. The main thrust behind the project is to indicate general concepts that are fundamental to any graph-oriented database user-interface. GOOD does not restrict its attention to well-considered topics such as ad-hoc query facilities, but wants to cover the full spectrum of database manipulations. The idea of graph-pattern matching as a uniform object manipulation primitive offers a uniform framework in which this can be accomplished.

Providing better support for a class of decision support queries

Relational database systems do not effectively support complex queries containing quantifiers (quantified queries) that are increasingly becoming important in decision support applications. Generalized quantifiers provide an effective way of expressing such queries naturally. In this paper, we consider the problem of processing quantified queries within the generalized quantifier framework. We demonstrate that current relational systems are ill-equipped, both at the language and at the query processing level, to deal with such queries. We also provide insights into the intrinsic difficulties associated with processing such queries. We then describe the implementation of a quantified query processor, Q2P, that is based on multidimensional and boolean matrix structures. We provide results of performance experiments run on Q2P that demonstrate superior performance on quantified queries. Our results indicate that it is feasible to augment relational systems with query subsystems like Q2P for significant performance benefits for quantified queries in decision support applications.