The nature of information systems is changing as they become more sophisticated and increasingly address more complex application domains. This has in turn complicates the representation of the functional requirements of a particular problem domain in terms of its basic entities and their relationships, as expressed in conceptual data models. Conceptual data models aim at establishing a link between user and domain requirements. The process of developing conceptual data models seems to be quite straightforward; however, it is often a lengthy and iterative process, and the output models can have a significant impact on the quality of the final system. As ontologies can capture consensual commitment about domain knowledge, the goal of this thesis is to study the extent to which consensual knowledge about a certain domain that has been captured in domain ontologies can participate in developing conceptual data models. Therefore, this thesis research introduces a framework in a form of so-called Transformation-Engine to generate a possible conceptual data model from a given domain ontology. The functionality of the Transformation-Engine constitutes of two main activities: (1) generating a suggested conceptual data model from a given domain ontology using a novel set of mapping rules between an ontology language and a conceptual data modelling constructs; (2) improving the quality of the generated conceptual data model elements using the newly introduced ontological quality graph. The results of this thesis show that conceptual data models that are developed from domain ontologies are comparable to the models that are traditionally developed during the elicitation stage. The approach does not generate a comprehensive conceptual data model automatically, but suggests relevant alternatives to modellers as they capture the basic entities and their relations for the problem domain identified by a domain community. The reverse-engineered conceptual data models aid understanding and communication, and facilitate eventual system integration.

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Recent advances in science and technology promote the generation of a huge amount of data from various sources including scientific experiments, social surveys and practical observations. The availability of powerful computer hardware and software offers easier ways to store datasets. However, more efficient and accurate methodologies are required to analyse datasets and extract useful information from them. This work aims at applying mathematical programming and optimisation methodologies to analyse different forms of datasets. The research focuses on three areas including data classification, community structure identification of complex networks and DNA motif discovery. Firstly, a general data classification problem is investigated. A mixed integer optimisation-based approach is proposed to reveal the patterns hidden behind training data samples using a hyper-box representation. An efficient solution methodology is then developed to extend the applicability of hyper-box classifiers to datasets with many training samples and complex structures. Secondly, the network community structure identification problem is addressed. The proposed mathematical model finds optimal modular structures of complex networks through the maximisation of network modularity metric. Communities of medium/large networks are identified through a two-stage solution algorithm developed in this thesis. Finally, the third part presents an optimisation-based framework to extract DNA motifs and consensus sequences. The problem is formulated as a mixed integer linear programming model and an iterative solution procedure is developed to identify multiple motifs in each DNA sequence. The flexibility of the proposed motif finding approach is then demonstrated to incorporate other biological features.

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