Ontological Foundations of Data Modeling in Information Systems

In this paper we present propositions which we have argued elsewhere concerning ontology and data models. Additionally, we present evidence relating to our propositions. We have found that Chisholm’s ontology has the potential to be a unifying theory for data models. In addition, our research has lead us to the position that ontologies founded in the philosophical tradition of realism seem to serve the purpose of a unifying framework for data models. Further, we have seen the realistic ontologies by Mario Bunge and Roderick Chisholm used in information systems. We believe that realistic ontologies have a role to play in understanding information systems

Ontology as Meta-Theory: A Perspective

been underway for the past two decades to examine the theoretical underpinnings of information systems. In recent years there has been increasing doubt about founding such a program on a single ontology. This has culminated in an article by Boris Wyssusek in which the whole program of (philosophical) ontology in information systems more generally has been questioned. In this paper we address the question: Is there a role for ontology in information systems? We return to basic principles in addressing this question and in so doing we address the issues raised by Wyssusek’s article

Data Modelling Languages: An Ontological Study

There are many data modelling languages used in today’s information systems engineering environment. Some of the data modelling languages used have a degree of hype surrounding their quality and applicability. We would like to understand exactly what makes some data modelling languages successful and in some way suggest how useful data modelling languages will be in the context of an organisation and why. We are also interested in a theory capable of unifying the disparate range of languages. To do these things we select a theory based on ontology using which data modelling languages can be investigated. In this context theory should allow us to understand, compare, evaluate, and strengthen data modelling languages. The theory may also be used to suggest how useful various data modelling languages may be in an organisational setting. In this paper we present Chisholm’s ontology which we use to investigate data modelling languages. We show how Chisholm’s ontology can be used as a unifying theory of data models, develop methods for comparing data modelling languages based on this theory and summarise our findings. In conclusion, we evaluate the methods and the theory and examine avenues for future research. In this paper we present a deeper understanding of method together with analysis of new data modelling languages

An ontology of data modelling languages: a study using a common-sense realistic ontology

Data modelling languages are used in today’s information systems engineering environments. Many have a degree of hype surrounding their quality and applicability with narrow and specific justification often given in support of one over another. We want to more deeply understand the fundamental nature of data modelling languages. We thus propose a theory, based on ontology, that should allow us to understand, compare, evaluate, and strengthen data modelling languages. In this paper we present a method (conceptual evaluation) and its extension (conceptual comparison), as part of our theory. Our methods are largely independent of a specific ontology. We introduce Chisholm’s ontology and apply our methods to analyse some data modelling languages using it. We find a good degree of overlap between all of the data modelling languages analysed and the core concepts of Chisholm’s ontology, and conclude that the data modelling languages investigated reflect an ontology of commonsense-realism

Verifying model oriented specifications through animation

In this paper we demonstrate how light weight tools can be used to increase the level of confidence in Z specifications. In particular we outline the Pipedream approach to exploring Z specifications through animation, and illustrate the range of analyses that can be performed. We argue that, while a light weight approach does not give the same levels of assurance that an automated reasoning system would, it does give levels of assurance which are adequate for most projects and with significantly less overhead. We illustrate how animation can be used to perform verification using the example of a simple dependency management system. 1

Animating Z Using Logic Programming Techniques

: One method for detecting errors in a formal specification is animation. It is complementary to theorem proving and can be highly cost-effective, particularly earlier in development. In my talk I'll discuss animation of the specification notation Z. I'll argue that: 1. it is desirable to perform analysis prior to execution; 2. logic programming languages are an attractive target for animations of Z; 3. mode analysis can help bridge the gap between Z and Mercury. The details of a mode analysis algorithm will be presented. What is Animation? "To execute the unexecutable spec" Automatically deriving prototypes from specifications. Exploration: more general than execution. 1 One of the main differences between animation and compilation is that animation is a partial function. 1-1 Why Animation? ffl Iterative construction of mathematical models ffl Early feedback -- useful for verification ffl Prototype can be demonstrated -- useful for validation ffl More accessable to developers (l..

Rapid Prototyping using Formal Specifications

. There is growing interest in animating formal specifications for the purpose of better understanding the requirements and for validating the specification. Formal specifications in a non-executable language offer challenges for animation systems, for example, dealing effectively with infinite data sets, sensibly animating functions which are not computable and determining a sensible set of inputs and outputs for arbitrary relations. In this paper we examine these issues in the context of animating Z specifications in the logic programming language Mercury. In particular we outline how information for making a non-executable Z specification executable can be derived using static analysis techniques from logic programming. We present analysis algorithms for deriving control (mode) and representation (subtype) information and show how these analyses are used in a tool for deriving Mercury programs from Z specifications. Finally we compare our approach with existing systems for animating..