Mohammed Odeh

Bridging the gap between business models and system models

Abstract This paper discusses links that may be made between process models and Unified Modelling Language (UML) software specification techniques, working from an argument that the whole complexity of organisational activity cannot be captured by UML alone. The approach taken is to develop a set of use cases, which would be capable of providing information support to a pre-defined organisational process. The nature of the thinking, which is necessary to derive the use cases, is outlined using the pre-defined process as a case study. The grouping of transactions and state changes into Use Cases is shown to require design choices, which may vary between particular organisational contexts. Conclusions are drawn about the direction of further investigation of links between process modelling and UML.

Mining breast cancer data with XCS

In this paper, we describe the use of a modern learning classifier system to a data mining task. In particular, in collaboration with a medical specialist, we apply XCS to a primary breast cancer data set. Our results indicate more effective knowledge discovery than with C4.5.

Experiences of engineering Grid-based medical software

OBJECTIVES Grid-based technologies are emerging as potential solutions for managing and collaborating distributed resources in the biomedical domain. Few examples exist, however, of successful implementations of Grid-enabled medical systems and even fewer have been deployed for evaluation in practice. The objective of this paper is to evaluate the use in clinical practice of a Grid-based imaging prototype and to establish directions for engineering future medical Grid developments and their subsequent deployment. METHOD The MammoGrid project has deployed a prototype system for clinicians using the Grid as its information infrastructure. To assist in the specification of the system requirements (and for the first time in healthgrid applications), use-case modelling has been carried out in close collaboration with clinicians and radiologists who had no prior experience of this modelling technique. A critical qualitative and, where possible, quantitative analysis of the MammoGrid prototype is presented leading to a set of recommendations from the delivery of the first deployed Grid-based medical imaging application. RESULTS We report critically on the application of software engineering techniques in the specification and implementation of the MammoGrid project and show that use-case modelling is a suitable vehicle for representing medical requirements and for communicating effectively with the clinical community. This paper also discusses the practical advantages and limitations of applying the Grid to real-life clinical applications and presents the consequent lessons learned. CONCLUSIONS The work presented in this paper demonstrates that given suitable commitment from collaborating radiologists it is practical to deploy in practice medical imaging analysis applications using the Grid but that standardization in and stability of the Grid software is a necessary pre-requisite for successful healthgrids. The MammoGrid prototype has therefore paved the way for further advanced Grid-based deployments in the medical and biomedical domains.

Revisiting genetic selection in the XCS learning classifier system

The XCS learning classifier system has traditionally used roulette wheel selection within its genetic algorithm component. Recently, tournament selection has been suggested as providing a number of benefits over the original scheme, particularly a robustness to parameter settings and problem noise. This paper revisits the comparisons made between the behavior of tournament and roulette wheel selection within XCS in a number of different situations. Results indicate that roulette wheel selection is competitive in terms of performance, stability and generated solution size if the appropriate parameters are used.

Ontology-driven relational query formulation using the semantic and assertional capabilities of OWL-DL

This work investigates the extent to which domain knowledge, expressed in a domain ontology, can assist end-users in formulating relational queries that can be executed over a complex relational database. In this regard, an ontology-driven query formulation architectural framework has been devised, namely OntoQF, that implements a two-phased approach - the pre-processing and translation phases. In the pre-processing phase, a new database-to-ontology transformation approach has been synthesised where domain ontology is populated and enriched with problem domain concepts and semantic relationships specified using OWL-DL. Once domain ontology has been formulated, end-users can write sophisticated ontology-based queries that are then translated, in the translation phase, into the corresponding relational query statements. In order to validate the correctness of translating single or multiple OWL-DL constructs into their corresponding relational ones, a set of test cases have been derived from the medical domain. Our results demonstrated that the OntoQF framework enriches domain ontology and its associated algorithms drive the process of relational query formulation without the need to both replicate transactional data into the associated domain ontology and have knowledge of the underlying database schema.

Engineering Conceptual Data Models from Domain Ontologies: A Critical Evaluation

This article studies the differences and similarities between domain ontologies and conceptual data models and the role that ontologies can play in establishing conceptual data models during the process of developing information systems. A mapping algorithm has been proposed and embedded in a special purpose transformation engine to generate a conceptual data model from a given domain ontology. Both quantitative and qualitative methods have been adopted to critically evaluate this new approach. In addition, this article focuses on evaluating the quality of the generated conceptual data model elements using Bunge-Wand-Weber and OntoClean ontologies. The results of this evaluation indicate that the generated conceptual data model provides a high degree of accuracy in identifying the substantial domain entities, along with their relationships being derived from the consensual semantics of domain knowledge. The results are encouraging and support the potential role that this approach can take part in the process of information system development.

Using use case patterns to estimate reusability in software systems

Abstract The applicability of using use case patterns as a basis for software cost estimation in the early stages of software development is described. This required the construction of a use case patterns catalogue using a novel process. The catalogue has been analysed to estimate the potential reusability in different software applications. This has shown that 43% of system functions are generally application domain independent, whereas 57% are application domain dependent. Statistical tests showed that the level of specialisation in software systems could be as low as 20%, which supports the direction taken in this research to build a use case patterns catalogue as a basis for the development of use case based software cost estimation models.

Ontology-driven requirements engineering with reference to the aerospace industry

This paper provides an overview regarding the application of an ontology-driven requirements engineering methodology, namely OntoREM, in the aerospace industry with the objective to assess the extent to which this approach has the potential to develop better quality requirements in less time and at less cost compared to traditional requirements engineering processes, taking the Airbus wing-engineering requirements as the case study. The OntoREMs Metamodel is briefly introduced, which embodies both the key process and task ontologies that guide the OntoREM related requirements engineering activities along with the supporting tooling environments, both existing and newly developed ones. Finally, the paper provides an overview of a small number of focused case studies in the aerospace context that are currently being undertaken and will help to further evolve and critically evaluate OntoREM with the objective to investigate the potential of a more knowledge-driven as opposed to process-driven approach to RE.

Deriving Conceptual Data Models from Domain Ontologies for Bioinformatics

This paper studies the role that ontologies can play in establishing conceptual data models during the process of information systems development. A mapping algorithm has been proposed and embedded in a special purpose Transformation-Engine to generate a conceptual data model from a given domain ontology. In addition, this paper focuses on applying the proposed approach to a bioinformatics context as the nature of biological data is considered a barrier in representing biological conceptual data models. Both quantitative and qualitative methods have been adopted to critically evaluate this new approach. The results of this evaluation indicate that the quality of the generated conceptual data models can reflect the problem domain entities and the associations between them. The results are encouraging and support the potential role that this approach can play in providing a suitable starting point for conceptual data model development.

Software Cost Estimation using Use-Case Models: a Critical Evaluation

In an attempt to compensate for the unavailability of software cost estimation models that fit the different software development environments in the early stages of the software development life cycle, this paper reports on the development of three novel use case model based software cost estimation methods: use case rough estimation method, use case patterns estimation method, and object points extraction estimation method. The accuracy of the proposed methods has been investigated using a wide spectrum of software projects. Two findings emerge. First, the accuracy of the proposed methods surpasses that of the well-know software cost estimation models and varies between 15-53% of the actual effort. Second, the application of these software cost estimation methods to the three different use-case model levels of detail namely, brief, casual, and fully-specified has strengthened the basis of this research hypothesis that the more detailed a use case model is, the more accurate the estimate of the software development effort