This tutorial presents an introduction to the BORO methodology, an ontology-based systems engineering approach. The authors present both the ontological foundations of the approach as well as business examples of the application of this approach.

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.

When we embed the building of an ontology into an information system development or maintenance process, then the question arises as to how one should construct the content of the ontology. One of the choices is whether the construction process should focus on the mining of the ontology from existing resources or should be the result of speculation (‘starting with a blank sheet of paper’). I present some arguments for choosing mining over speculation and then look at the implications this has for legacy modernisation.

More and more enterprises are currently undertaking projects to integrate their applications. They are finding that one of the more difficult tasks facing them is determining how the data from one application matches semantically with the other applications. Currently there are few methodologies for undertaking this task – most commercial projects just rely on experience and intuition. Taking semantically heterogeneous databases as the prototypical situation, this paper describes how ontology (in the traditional metaphysical sense) can contribute to delivering a more efficient and effective process of matching by providing a framework for the analysis, and so the basis for a methodology. It delivers not only a better process for matching, but the process also gives a better result. This paper describes a couple of examples of this: how the analysis encourages a kind of generalisation that reduces complexity. Finally, it suggests that the benefits are not just restricted to individual integration projects: that the process produces models which can be used as to construct a universal reference ontology – for general use in a variety of types of projects.

From the perspective of enterprise computing, ontology is seen as a kind detached pure science. When enterprise computing ventures into ontological topics it does not look to ontology to provide it with theories - it devises its own theory-lite solutions. This keynote aims to make a case for joining up these two by identifying an area where enterprise computing can usefully apply ontological theory. It does this using an example; immutability, a current concern in big data. It argues that ontology’s theories about change, in particular McTaggart’s analysis of ways of viewing time in terms of series, provide a strong explanatory framework for enterprise computing’s immutability and have the potential to lead to better solutions. This approach also reveals that there is an aspect of change in computing systems – the epistemic aspect – where a mutable approach (McTaggart’s Series A) provides a better explanatory framework.

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.