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The objective of this work is to develop a posteriori error estimates and adaptive strategies for the numerical solution to nonlinear systems of partial differential equations with uncertain data. Areas of application cover problems in fluid mechanics including a Bayesian model selection study of turbulence comparing different uncertainty models. Accounting for uncertainties in model parameters may significantly increase the computational time when simulating complex problems. The premise is that using error estimates and adaptively refining the solution process can reduce the cost of such simulations while preserving their accuracy within some tolerance. New insights for goal-oriented error estimation for deterministic nonlinear problems are first presented. Linearization of the adjoint problems and quantities of interest introduces higher-order terms in the error representation that are generally neglected. Their effects on goal-oriented adaptive strategies are investigated in detail here. Contributions on that subject include extensions of well-known theoretical results for linear problems to the nonlinear setting, computational studies in support of these results, and an extensive comparative study of goal-oriented adaptive schemes that do, and do not, include the higher-order terms. Approaches for goal-oriented error estimation for PDEs with uncertain coefficients have already been presented, but lack the capability of distinguishing between the different sources of error. A novel approach is proposed here, that decomposes the error estimate into contributions from the physical discretization and the uncertainty approximation. Theoretical bounds are proven and numerical examples are presented to verify that the approach identifies the predominant source of the error in a surrogate model. Adaptive strategies, that use this error decomposition and refine the approximation space accordingly, are designed and tested. All methodologies are demonstrated on benchmark flow problems: Stokes lid-driven cavity, 1D Burger’s equation, 2D incompressible flows at low Reynolds numbers. The procedure is also applied to an uncertainty quantification study of RANS turbulence models in channel flows. Adaptive surrogate models are constructed to make parameter uncertainty propagation more efficient. Using surrogate models and adaptivity in a Bayesian model selection procedure, it is shown that significant computational savings can be gained over the full RANS model while maintaining similar accuracy in the predictions. / text

The thesis proposes a number of techniques for elaborating requirements constructively from high-level goals. The techniques are based on the KAOS goal-oriented method for requirements engineering. This method consists in identifying goals and refining them into subgoals until the latter can be assigned as responsibilities of single agents such as humans, devices and software. Domain properties and assumptions about the software environment are also used during the goal refinement process. The method supports the exploration of alternative goal refinements and alternative responsibility assignments of goals to agents. It also supports the identification and resolution of conflicts between goals, and the identification and resolution of exceptional agent behaviors, called obstacles, that violate goals and assumptions produced during the goal refinement process. The thesis enriches the KAOS framework through three kinds of techniques: (a) techniques for identifying agents, goal refinements, and alternative responsibility assignments, and for deriving agent interfaces from such responsibility assignments; (b) techniques for deriving operational requirements from goal specifications; (c) techniques for generating obstacles to the satisfaction of idealized goals and assumptions, and for generating alternative obstacle resolutions. The result is a coherent body of systematic techniques for requirements elaboration that are both theoretically well-founded (a formal model of agent is defined) and effective in practice (the techniques are validated on two real case studies of significant size: the London ambulance despatching system, and the Bay Area Rapid Transit train system).

The thesis proposes a number of techniques for elaborating requirements constructively from high-level goals. The techniques are based on the KAOS goal-oriented method for requirements engineering. This method consists in identifying goals and refining them into subgoals until the latter can be assigned as responsibilities of single agents such as humans, devices and software. Domain properties and assumptions about the software environment are also used during the goal refinement process. The method supports the exploration of alternative goal refinements and alternative responsibility assignments of goals to agents. It also supports the identification and resolution of conflicts between goals, and the identification and resolution of exceptional agent behaviors, called obstacles, that violate goals and assumptions produced during the goal refinement process. The thesis enriches the KAOS framework through three kinds of techniques: (a) techniques for identifying agents, goal refinements, and alternative responsibility assignments, and for deriving agent interfaces from such responsibility assignments; (b) techniques for deriving operational requirements from goal specifications; (c) techniques for generating obstacles to the satisfaction of idealized goals and assumptions, and for generating alternative obstacle resolutions. The result is a coherent body of systematic techniques for requirements elaboration that are both theoretically well-founded (a formal model of agent is defined) and effective in practice (the techniques are validated on two real case studies of significant size: the London ambulance despatching system, and the Bay Area Rapid Transit train system).

Intelligent agents are goal-oriented software entities which exhibit a number of desirable characteristics, such as flexibility and robustness, which are suitable for complex, dynamic, and failure-prone environments. However, these characteristics of individual agents are not exhibited by their interactions with each other since traditional approaches to interaction design are message-centric, and these message-centric approaches force the intelligent agents to follow prescribed message sequences in order to achieve their interactions, thus usually resulting in interactions which have limited flexibility and robustness. In this thesis an alternative to the traditional message-centric interaction design approaches is presented. In this approach, the interactions are designed based on interaction goals, and message sequences are not prescribed. Instead, message sequences emerge from the interactions as the intelligent agents attempt to achieve the interaction goals. The main contribution of this work is Hermes, a methodology for the design and implementation of goal-oriented interactions. An important motivation for Hermes is to not only allow for the design and implementation of goal-oriented interactions, but to also be pragmatic and usable by practicing software engineers. To that end, Hermes has a clear and guided design process with a notation explicitly created for the design of goal-oriented interactions. Furthermore, Hermes, which covers the design and implementation of agent interactions only, has been integrated with Prometheus, a full agent system design methodology. Guidelines for the integration are provided so that, in future, Hermes may also be integrated with other existing methodologies if desired. Hermes also provides guidelines for mapping its design artifacts to an implementation. As Hermes is goal-oriented, the implementation platform should be one that is goal-based. The guidelines help developers map the design to skeleton code. This contributes to the pragmatism of Hermes. To further ensure that Hermes is pragmatic, two prototype software support tools have been developed. The design support tool allows for the graphical design of Hermes artifacts and the implementation support tool produces skeleton code for the Jadex agent platform based on a structured textual representation of Hermes design artifacts. Although only the Jadex agent platform is currently supported, the implementation tool can be extended to accommodate other goal-based agent platforms. An empirical evaluation was carried out, and its results show that Hermes designs are significantly more flexible and robust than message-centric designs, although more time is required to design Hermes interactions. This suggests that Hermes is suitable for interactions which are complex and/or error-prone.

A successful information system is the one that meets its design goals. Expressing these goals and subsequently translating them into a working solution is a major challenge for information systems engineering. This thesis adopts the concepts and techniques from goal-oriented (software) requirements engineering research for conceptual database design, with a focus on data quality issues. Based on a real-world case study, a goal-oriented process is proposed for database requirements analysis and modeling. It spans from analysis of high-level stakeholder goals to detailed design of a conceptual databases schema. This process is then extended specifically for dealing with data quality issues: data of low quality may be detected and corrected by performing various quality assurance activities; to support these activities, the schema needs to be revised by accommodating additional data requirements. The extended process therefore focuses on analyzing and modeling quality assurance data requirements. A quality assurance activity supported by a revised schema may involve manual work, and/or rely on some automatic techniques, which often depend on the specification and enforcement of data quality rules. To address the constraint aspect in conceptual database design, data quality rules are classified according to a number of domain and application independent properties. This classification can be used to guide rule designers and to facilitate building of a rule repository. A quantitative framework is then proposed for measuring and comparing DQ rules according to one of these properties: effectiveness; this framework relies on derivation of formulas that represent the effectiveness of DQ rules under different probabilistic assumptions. A semi-automatic approach is also presented to derive these effectiveness formulas.

A successful information system is the one that meets its design goals. Expressing these goals and subsequently translating them into a working solution is a major challenge for information systems engineering. This thesis adopts the concepts and techniques from goal-oriented (software) requirements engineering research for conceptual database design, with a focus on data quality issues. Based on a real-world case study, a goal-oriented process is proposed for database requirements analysis and modeling. It spans from analysis of high-level stakeholder goals to detailed design of a conceptual databases schema. This process is then extended specifically for dealing with data quality issues: data of low quality may be detected and corrected by performing various quality assurance activities; to support these activities, the schema needs to be revised by accommodating additional data requirements. The extended process therefore focuses on analyzing and modeling quality assurance data requirements. A quality assurance activity supported by a revised schema may involve manual work, and/or rely on some automatic techniques, which often depend on the specification and enforcement of data quality rules. To address the constraint aspect in conceptual database design, data quality rules are classified according to a number of domain and application independent properties. This classification can be used to guide rule designers and to facilitate building of a rule repository. A quantitative framework is then proposed for measuring and comparing DQ rules according to one of these properties: effectiveness; this framework relies on derivation of formulas that represent the effectiveness of DQ rules under different probabilistic assumptions. A semi-automatic approach is also presented to derive these effectiveness formulas.

Under the severe competition environment, it is very important for enterprise to create various goals and install process oriented information system to support it to reach business visions on business growth and expansion. For keeping the enterprise competitive advantage the business goals needed to be modified continuously under the change of competition environment. But the business information systems could still support the modified business goals? Information system could become less efficiency than before after business goals changed. The purpose of this study is to develop a business goals oriented information system evaluation approach to understand the degree of information system supporting the business goals. The approach first abstracts business goal and process oriented information system by business management methods and utilizes use case as the evaluation tool. Quantitative index and diagram as the result of evaluation explains the relationship between business goals and information systems. The results of evaluation could provide advantages on the information technology decision making for business leaders.

Loop invariants have traditionally been used in proofs of correctness (e.g. program verification) and program derivation. Given that a loop invariant is all that is required to derive a provably correct program, the loop invariant can be thought of as being the essence of a loop. Being the essence of a loop, we ask the question “What other information is embedded within a loop invariant?” This dissertation provides evidence that in the domain of dense linear algebra, loop invariants can be used to determine the behavior of the loops. This dissertation demonstrates that by understanding how the loop invariant describes the behavior of the loop, a goal-oriented approach can be used to derive loops that are not only provably correct, but also have the desired performance behavior. / text

When creating and administering regulations, regulators have to demonstrate that regulations accomplish intended societal outcomes at costs that do not outweigh their benefits. While regulators have this responsibility as custodians of the regulatory ecosystem, they are also required to create and administer regulations transparently and impartially, addressing the needs and concerns of all stakeholders involved. This is in addition to regulators having to deal with various administrative bottlenecks, competing internal priorities, as well as financial and human resource limitations. Nonetheless, governments, regulated parties, citizens and interest groups can each express different views on the relevance and performance of a piece of regulation. These views range from too many regulations burdening business operations to perceptions that crises in society are the results of insufficient regulations. As such, regulators have to be innovative, employing methods that show that regulations are effective, and justify the introduction, evolution or repeal of regulations. The regulatory process has been the topic of various studies with several such studies exploring the use of information systems at the software level to confirm compliance with regulations and evaluate issues related to non-compliance. The rationale is that if information systems can improve operational functions in organizations, they can also help measure compliance. However, the research focus has been on enabling regulated parties to comply with regulations rather than on enabling regulators to assess or enforce compliance or show that regulations are effective. Regulators need to address concerns of too much regulations or too little regulations with data-driven evidence especially in this age of big data and artificial intelligence enhanced tools. A method that facilitates evidencebased decision-making using data for enacting, implementing and reviewing regulations is now inevitable. In response to the above challenges, this thesis explores the use of a goaloriented modelling method and a data analytics software, to create a method that enables monitoring, assessing and reporting on the effectiveness of regulations and regulatory initiatives. This Goal-oriented Regulatory Intelligence Method (GoRIM) provides an intelligent approach to regulatory management, as well as a feedback loop in the use of data from and within the regulatory ecosystem to create and administer regulations. To demonstrate its applicability, GoRIM was applied to three case studies involving regulators in three different real regulatory scenarios, and its feasibility and utility were evaluated. The results indicate that regulators found GoRIM promising in enabling them to show, with evidence, whether their regulations are effective.