Jan Paredaens

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.

Spatial Databases, The Final Frontier

During the last decade database systems are used to represent spatial information mainly in the twodimensional plane or the three-dimensional space. Applications that rely on spatial databases can be found in CAD-CAM, VLSI, robotics, historical databases, geographical information systems, architectural sciences, visual perception and autonomous navigation, tracking, environmental protection and medical imaging.

Concepts for Graph-Oriented Object Manipulation

We propose a number of fundamental concepts for graph-oriented database user interfaces. For both schemes and instances we use abstract directed graphs. We represent different kinds of database interactions, such as querying, constraint specification, updating, restructuring, and schema transformation, by means of a uniform graph-transformation framework based on pattern matching. Staying within the same framework, we incorporate viewing and give a formal definition of browsing.

On the Expressive Power of the Relational Algebra on Finite Sets of Relation Pairs

We give a language-independent characterization of the expressive power of the relational algebra on finite sets of source-target relation instance pairs. The associated decision problem is shown to be co-graph-isomorphism hard and in co NP. The main result is also applied in providing a new characterization of the generic relational queries.

A Language for Generic Graph-Transformations

We define a class of graphs that represent object-oriented databases. Queries, updates and restructurings in such databases are performed by transformations of these graphs. We therefore first define five transformation-operations on this graph-class. Then we define generic transformations as a natural generalization of the concept of BP-completeness, originally defined for the relational database model. Our main result states that the language consisting of the five transformation-operations expresses exactly the generic transformations.

Euclid, Tarski and Engeler encompassed

The research presented in this paper is situated in the framework of constraint databases that was introduced by Kanellakis, Kuper, and Revesz in their seminal paper of 1990. In this area, databases and query languages are defined using read polynomial constraints. As a consequence of a classical result by Tarski, first-order queries in the constraint database model are effectively computable, and their result is within the constraint model.The research presented in this paper is situated in the framework of constraint databases that was introduced by Kanellakis, Kuper, and Revesz in their seminal paper of 1990. In this area, databases and query languages are defined using read polynomial constraints. As a consequence of a classical result by Tarski, first-order queries in the constraint database model are effectively computable, and their result is within the constraint model.

Expressiveness and complexity of generic graph machines

Abstract. The Generic Graph Machine (GGM) model is a Turing machine-like model for expressing generic computations working directly on graph structures. In this paper we present a number of observations concerning the expressiveness and complexity of GGMs. Our results comprise the following: (i) an intrinsic characterization of the pairs of graphs that are an input—output pair of some GGM; (ii) a comparison between GGM complexity and TM complexity; and (iii) a detailed discussion on the connections between the GGM model and other generic computation models considered in the literature, in particular the generic complexity classes of Abiteboul and Vianu, and the Database Method Schemes of Denninghoff and Vianu.

A simple but formal semantics for XML manipulation languages

XML is a modern format that is nowadays used to store many documents, expecially on the Web. Since a set of documents can be considered as a semi-structured dataset or database, we want to query these documents. Moreover in many applications we need to transform documents into other documents, containing the same information, but with a different structure. Finally, most documents are represented in HTML on the Web, which can be seen as XML-documents. n nWe discuss sublanguages of XPath, XQuery and XSLT. The latter are wellused manipulation languages for XML that were developed during the last decade in which we can express these queries and transformations. XPath is a simple language that enables us to navigate through a document. XQuery is a powerful query language for XML and XSLT is a transformation language. These three languages are conceptually totally different. For each of them we have defined an upward compatible sublanguage respectively MiXPath, MiXQuery and MiXSLT. These are three manipulation languages for XML, whose semantics is defined in a formal, uniform, compact and elegant way. n nThese three languages will enable us later on to investigate more easily certain aspects such as the expressive power of certain types of expressions found in XPath, XQuery and XSLT, the expressive power of recursion and possible syntactical restrictions that let us control this power, the complexity of deciding equivalence of expressions for purposes such as query optimization, the functional character in comparison with functional languages such as LISP and ML, the role of XPath 1.0 and 2.0 in XQuery in terms of expressive power and query optimization, and the relationship between queries on XML and the classical well-understood concept of generic database queries. The contribution of MiXPath, MiXQuery and MiXSLT is their relatively simple syntax and semantics that is appropriate both for educational and research purposes. Indeed, we are convinced that these languages have a number of interesting properties, that can be proved formally, and that can be transposed to XPath, XQuery and XSLT.

Advances in Database Systems

FOUNDATIONS OF DATABASE SYSTEMS: AN INTRODUCTORY TUTORIAL J. Paredaens University of Antwerp, Antwerp, Belgium and Technical University of Eindhoven, Eindhoven, The Netherlands A very short overview is given of the principles of databases. The entity relationship model is used to define the conceptual base. Furthermore file management, the hierarchical model, the network model, the relation al model and the object oriented model are discussed During the second world war, computers were used for encoding and decoding messages of the English and German army. Only after the war the computers were used for more reasonable and economical reasons. In the beginning of the fifties problems with typically a small amount of input data but for which complex calculations have to be made, were solved using computers. This was due to the small amount of memory and the long access time that was needed. Typical programming languages for this kind of problems emerged: Fortran, Algol and later on Pascal, C and C++. These languages however, were not user friendly enough, nor were they designed to handle huge amounts of complex data. So in the fifties we switched from simple file management to the use of a data structure that was more complex, but that enabled us to solve problems and ans wer questions that needed a huge quantity of data. We will first introduce the concept of a database and then we will look to a number of different kinds of databases that have been used from 1960 until now.

The Nested Relational Database Model

Up to this point, we have been dealing with the standard relational model introduced by Codd. Probably, one of the most fundamental assumptions made in this model is that data is represented in the form of flat tables; this is the so called first normal form assumption. In many practical circumstances, however, data is not represented as flat tables but rather in the form of hierarchically organized tables. For example consider the table shown in Figure 7.1. A row in that table represents a person, the set of his or her natural children and for each child, the set of his or her toys. In the relational model this table would be represented as shown in Figure 7.2. The argument made by many people is that the hierarchically organized table is a more natural representation of this data. So there is a need to represent and manipulate such data.