Legacy Information Systems (LIS) pose a challenge for many organizations. On one hand, LIS are viewed as aging systems needing replacement; on the other hand, years of accumulated business knowledge have made these systems mission-critical. Current approaches however are often criticized for being overtly dependent on technology and ignoring the business knowledge which resides within LIS. In this light, this paper proposes a means of capturing the business knowledge in a technology agnostic manner and transforming it in a way that reaps the benefits of clear semantic expression – this transformation is achieved via the careful use of ontology. The approach called Content Sophistication (CS) aims to provide a model of the business that more closely adheres to the semantics and relationships of objects existing in the real world. The approach is illustrated via an example taken from a case study concerning the renovation of a large financial system and the outcome of the approach results in technology agnostic models that show improvements along several dimensions.

The software stability approach is required to balance the seemingly contradictory goals of stability over the software lifecycle with the need for adaptability, extensibility and interoperability. This workshop paper addresses the issue of how software stability can be achieved over time by outlining an approach to evolving General Business Patterns (GBPs) from the empirical data contained within legacy systems. GBPs are patterns of business objects that are (directionally) stable across contexts of use. The work explains, via a small worked example, how stability is achieved via a process of ‘sophistication’. The outcome of the process demonstrates how the balance that stability seeks can be achieved.

The organizational environment increasingly demands that computer-based information systems are responsive to change and can work with each other seamlessly (ideally from a dynamic perspective). Given the large investment that organizations have in mission-critical legacy systems, evolutionary maintenance and systems integration now form a very significant part of the cost and effort profile of systems development. In terms of the integration issue, much of the difficulty lies in the fact that different systems often contain different ‘representations’ of the world. In the development process, it is generally accepted that the ‘information’ an information system contains about its business domain(s) is an essential intellectual part of the system, and the domain of fundamental concern. This concern is generally regarded as unitary, however, requiring no further breakdown into parts and it is commonly perceived that its relation to the business information system is simple and direct.

This paper examines the development of a conceptual model that defines Shell’s information requirements - the Downstream Data Model (DDM). The model has its roots in a framework based on the notion of ontological commitment and the focus of the analysis seeks to provide useful insights into the metaphysical aspects relevant to the creation and deployment of the DDM – primarily that related to the extensional nature of the model. The impact of this choice and the methodology employed in the production of the model is examined through example patterns covering spatial and temporal dissectiveness and the use of powerclasses. Having been through the experience of conceptual model development, the work concludes that the separation of the implementational and epistemological ‘gloss’ from a studied understanding of ontological commitment is a necessary evolution of practice in conceptual modelling.

With re-engineering of software systems becoming quite pronounced amongst organisations, a software stability approach is required to balance the seemingly contradictory goals of stability over the software lifecycle with the need for adaptability, extensibility and interoperability. This paper addresses the issue of how software stability can be achieved over time by outlining an approach to evolving General Business Patterns (GBPs) from the empirical data contained within legacy systems. GBPs are patterns of business objects that are (directionally) stable across contexts of use. The approach is rooted in developing patterns by extracting the business knowledge embedded in existing software systems. The process of developing this business knowledge is done via the careful use of ontology, which provides a way to reap the benefits of clear semantic expression. A worked example is presented to show how stability is achieved via a process of ‘interpretation’ and ‘sophistication’. The outcome of the process demonstrates how the balance that stability seeks can be achieved.