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Electricity has always had a great impact on our daily life. It plays an important role in every aspect  of  society,  economy,  and  technology  of  every  nation.  Sweden  among  other  Nordic countries has always strived to improve its energy landscape. Currently, Nuclear power and Hydroelectricity  are  the  main  methods  of  energy  generation  in  this  country.  Together  with exploring  new  ways  of  generating  energy  without  dependency  on  nuclear  power,  Sweden also  expresses  an  interest  in  encouraging  households  and  companies  to  use  energy  in  an efficient way in order to reduce energy  consumption and its associated costs. The scope of this thesis is to review and evaluate various state-of-the-art data analysis tools and algorithms to  generate  a  meaningful  consumer  behaviour  model  based  on  the  electricity  usage  data collected   from   households   in   several   areas   of   Sweden.   Understanding   the   demand characteristics  for  electricity  would  give  electric  suppliers  more  power  in  shaping  their marketing strategies as well as setting appropriate electricity pricing.

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This thesis researches the problems concerning the specification and implementation of the temporal requirements in data warehouses. The thesis focuses on two areas, firstly, the methods for identifying and capturing the business information needs and associated temporal requirements at the conceptual level and; secondly, methods for classifying and implementing the requirements at the logical level using the relational model. At the conceptual level, eight candidate methodologies were investigated to examine their suitability for the creation of data models that are appropriate for a data warehouse. The methods were evaluated to assess their representation of time, their ability to reflect the dimensional nature of data warehouse models and their simplicity of use. The research found that none of the methods under review fully satisfied the criteria. At the logical level, the research concluded that the methods widely used in current practice result in data structures that are either incapable of answering some very basic questions involving history or that return inaccurate results. Specific proposals are made in three areas. Firstly, a new conceptual model is described that is designed to capture the information requirements for dimensional models and has full support for time. Secondly, a new approach at the logical level is proposed. It provides the data structures that enable the requirements captured in the conceptual model to be implemented, thus enabling the historical questions to be answered simply and accurately. Thirdly, a set of rules is developed to help minimise the inaccuracy caused by time. A guide has been produced that provides practitioners with the tools and instructions on how to implement data warehouses using the methods developed in the thesis.

Robert Maynard, lead developer for E2V0 (MIRARCO), describes ParaViewGeo - a version of ParaView that they have modified heavily to support data modelling and visualization in the mining and geoscience fields - as well as the open source development process used by E2V0.

Feature based modelling represents the future of CAD systems. However, operations such as modelling and editing can corrupt the validity of a feature-based model representation. Feature interactions are a consequence of feature operations and the existence of a number of features in the same model. Feature interaction affects not only the solid representation of the part, but also the functional intentions embedded within features. A technique is thus required to assess the integrity of a feature-based model from various perspectives, including the functional intentional one, and this technique must take into account the problems brought about by feature interactions and operations. The understanding, reasoning and resolution of invalid feature-based models requires an understanding of the feature interaction phenomena, as well as the characterisation of these functional intentions. A system capable of such assessment is called a feature-based representation validation system. This research studies feature interaction phenomena and feature-based designer's intents as a medium to achieve a feature-based representation validation system.

Understanding and managing change is a strategic objective for many organisations to successfully compete in a market place; as a result, organisations are leveraging their data asset and implementing data warehouses to gain business intelligence necessary to improve their businesses. Data warehouses are expensive initiatives, one-half to two-thirds of most data warehousing efforts end in failure. In the absence of well-formalised design methodology in the industry and in the context of the debate on data architecture in data warehousing, this thesis examines why multidimensional and relational data models define the data architecture landscape in the industry. The study develops a number of propositions from the literature and empirical data to understand the factors impacting the choice of logical data model in data warehousing. Using a comparative case study method as the mean of collecting empirical data from the case organisations, the research proposes a conceptual model for logical data model adoption. The model provides a framework that guides decision making for adopting a logical data model for a data warehouse. The research conceptual model identifies the characteristics of business requirements and decision pathways for multidimensional and relational data warehouses. The conceptual model adds value by identifying the business requirements which a multidimensional and relational logical data model is empirically applicable.