The challenges of developing the information systems (IS) that support modern enterprises are becoming less about engineering and more about people. Many of the technical issues of the past, such as hardware size and power, connectivity, and robust software, are engineering problems that have largely been solved. In the next stage of computing, the human factor will be far more important than it has been in the past: the colors of an interface or the shape of an icon are the engineering problems of the past, and the availability and usefulness of such basic solutions is rapidly coming to a close. A new paradigm is needed that provides a roadmap of higher level conceptions and values, one about humane computing. A part of this older, mechanistic approach are quantitative, economic values whose impact on IS are readily visible and acknowledged within software engineering. However, qualitative values, and in particular consumer preferences, have been researched to a lesser degree, and there has been very little direct application.  To create the next-generation information systems, requirements engineers and systems developers need new methods to capture the real preferences of consumers, conceptualize these abstract concepts, and then relate such preferences to concrete requirements for information systems. To address this problem, this thesis establishes a conceptual link between the preferences of consumers and system requirements by accommodating the variations between them and expressing them via a conceptual model. Modeling such preferences and values so that they can be used as requirements for IS development is the primary contribution of this work. This is accomplished via a design science research paradigm to support the creation of the works’ primary artifact—the Consumer Preference-aware Meta-Model (CPMM). CPMM is intended to improve the alignment between business and information systems by capturing and concretizing the real preferences of consumers and then expressing such preferences via the requirements engineering process, with the eventual output being information systems. CPMM’s development relies on theoretical research contributions within three areas in information systems—Business Strategy, Enterprise Architecture, and Requirements Engineering—whose relationships to consumer values have been under-researched and under-applied. The case studies included in this thesis each demonstrate the significance of consumer preferences to each of these three areas.  In the first, a set of logical mappings between CPMM and a common approach to business strategy (strategy maps/balanced scorecards) is produced. In the second, CPMM provides the conceptual undergirding to process a massive amount of unstructured consumer-generated text to generate system requirements for the airline industry. In the concluding case, an investigation of foreign and domestic students at Swedish universities is structured through CPMM, one that first discovers the requirements for a consumer preference-based online education and then produces feature models for such a software product line-based system. The significance of CPMM as a lens for discovering new concepts and highlighting important information within consumer preference data is clearly seen, and the usefulness of the meta-model is demonstrated by its broad and beneficial applicability within information systems practice and research.

Organizations in many domains such as healthcare, finances, telecommunications, educa-tion, and software development, must comply with an ever-increasing number of regula-tions, including laws and policies. In order to measure compliance to regulation, several recent approaches propose modelling regulations using goals and indicators. However, creating goal models for regulations is time consuming and prone to errors, especially as this is usually done manually. This thesis tackles this issue by automating some of the steps for creating goal models, and by offering better ways to create graphical views of goal models than what is currently available nowadays in goal modelling tools. The notation used in this thesis is the Goal-oriented Requirement Language (GRL), which is part of the User Requirements Notation standard and is supported by the jUCMNav tool. The concepts of regulations and their indicators are captured using a tab-ular presentation in Comma-Separated Value (CSV) files. An import mechanism is added to jUCMNav to automatically create regulation goal models from such files. The imported GRL model can then by visualized using novel features that enable the addition of multiple views/diagrams in an efficient and usable way.

Made available in DSpace on 2016-08-29T15:33:17Z (GMT). No. of bitstreams: 1 tese_5084_.pdf: 1868850 bytes, checksum: 22f46d96f48ef171f653cbcd277c6088 (MD5) Previous issue date: 2012-08-22 / Epidemiology is a field of study in Medicine which seeks to understand the factors that determine the frequency and distribution of diseases in humans. This field allows one to understand the phenomena of health and disease of a particular population by generating this populations epidemiological profile. The knowledge provided in this profile allows a shift in the focus from treating to preventing diseases, which is an important aim of the current Brazilian health care program. This dissertation proposes a system to study the epidemiological profile in a basic health care unit. This system applies an ontology as basis for modeling and querying the epidemiological information. An ontology is a conceptual model which captures an specific view of a domain of discourse. This model may be used to structure the systems information, which later can be queried also with basis on this same ontology. A preliminary validation of this systems prototype has shown that it is able to successfully generate the health care units epidemiological profile, providing new knowledge about the patients and treatments involved in this unit. Such prototype may now be applied in this real setting to guide the actions of health care professionals in dealing with hypertension and other health conditions. In order to develop the system, a goal-oriented methodology based on Tropos is applied. This methodology guides software development since an early stage of organizational modeling until the systems implementation by using current standards for ontology implementation. Many of the available ontology engineering methodologies presuppose the existence of a set of questions which provide the objective and scope of the ontology under development. However, these so-called competency questions are not always clear from start. The highlight of the proposed methodology is applying goal analysis to assist the ontology engineer to reason about and model competency questions. Following this view, such competency questions are comparable to system requirements, elicited and modeled during the requirements engineering stage of a software development process. Both the developed system and the proposed methodology are contributions of this work. However, while the former has proven to be useful in practice, further steps must be carried out in order to properly validate the latter, by applying it to other cases.

Organizations in many domains such as healthcare, finances, telecommunications, educa-tion, and software development, must comply with an ever-increasing number of regula-tions, including laws and policies. In order to measure compliance to regulation, several recent approaches propose modelling regulations using goals and indicators. However, creating goal models for regulations is time consuming and prone to errors, especially as this is usually done manually. This thesis tackles this issue by automating some of the steps for creating goal models, and by offering better ways to create graphical views of goal models than what is currently available nowadays in goal modelling tools. The notation used in this thesis is the Goal-oriented Requirement Language (GRL), which is part of the User Requirements Notation standard and is supported by the jUCMNav tool. The concepts of regulations and their indicators are captured using a tab-ular presentation in Comma-Separated Value (CSV) files. An import mechanism is added to jUCMNav to automatically create regulation goal models from such files. The imported GRL model can then by visualized using novel features that enable the addition of multiple views/diagrams in an efficient and usable way.

Conceptual modeling allows abstraction, communication and consensus building in system development. It is challenging to expand and improve the accuracy of models in an iterative process, producing models able to facilitate analysis. Modeling and analysis can be especially challenging in early Requirements Engineering (RE), where high-level system requirements are discovered. In this stage, hard-to-measure non-functional requirements are critical; understanding the interactions between systems and stakeholders is a key to system success. Goal models have been introduced as a means to ensure stakeholder needs are met in early RE. Because of the high-level, social nature of early RE models, it is important to provide procedures which prompt stakeholder involvement (interaction) and model improvement (iteration). Most current approaches to goal model analysis require quantitative or formal information that is hard to gather in early RE, or produce analysis results automatically over models. Approaches are needed which balance automated analysis over complex models with the need for interaction and iteration. This work develops a framework for iterative, interactive analysis for early RE using agent-goal models. We survey existing approaches for goal model analysis, providing guidelines using domain characteristics to advise on procedure selection. We define requirements for an agent-goal model framework specific to early RE analysis, using these requirements to evaluate the appropriateness of existing work and to motivate and evaluate the components of our analysis framework. We provide a detailed review of forward satisfaction procedures, exploring how different model interpretations affect analysis results. A survey of agent-goal variations in practice is used to create a formal definition of the i* modeling framework which supports sensible syntax variations. This definition is used to precisely define analysis procedures and concepts throughout the work. The framework consists of analysis procedures, implemented in the OpenOME requirements modeling tool, which allow users to ask “What if?” and “Is this goal achievable, and how?” questions. Visualization techniques are introduced to aid analysis understanding. Consistency checks are defined over the interactive portion of the framework. Implementation, performance and potential optimizations are described. Group and individual case studies help to validate framework effectiveness in practice. Contributions are summarized in light of the requirements for early RE analysis. Finally, limitations and future work are described.

Conceptual modeling allows abstraction, communication and consensus building in system development. It is challenging to expand and improve the accuracy of models in an iterative process, producing models able to facilitate analysis. Modeling and analysis can be especially challenging in early Requirements Engineering (RE), where high-level system requirements are discovered. In this stage, hard-to-measure non-functional requirements are critical; understanding the interactions between systems and stakeholders is a key to system success. Goal models have been introduced as a means to ensure stakeholder needs are met in early RE. Because of the high-level, social nature of early RE models, it is important to provide procedures which prompt stakeholder involvement (interaction) and model improvement (iteration). Most current approaches to goal model analysis require quantitative or formal information that is hard to gather in early RE, or produce analysis results automatically over models. Approaches are needed which balance automated analysis over complex models with the need for interaction and iteration. This work develops a framework for iterative, interactive analysis for early RE using agent-goal models. We survey existing approaches for goal model analysis, providing guidelines using domain characteristics to advise on procedure selection. We define requirements for an agent-goal model framework specific to early RE analysis, using these requirements to evaluate the appropriateness of existing work and to motivate and evaluate the components of our analysis framework. We provide a detailed review of forward satisfaction procedures, exploring how different model interpretations affect analysis results. A survey of agent-goal variations in practice is used to create a formal definition of the i* modeling framework which supports sensible syntax variations. This definition is used to precisely define analysis procedures and concepts throughout the work. The framework consists of analysis procedures, implemented in the OpenOME requirements modeling tool, which allow users to ask “What if?” and “Is this goal achievable, and how?” questions. Visualization techniques are introduced to aid analysis understanding. Consistency checks are defined over the interactive portion of the framework. Implementation, performance and potential optimizations are described. Group and individual case studies help to validate framework effectiveness in practice. Contributions are summarized in light of the requirements for early RE analysis. Finally, limitations and future work are described.

The desire to obtain value and justify investments from the different Information Systems in place in organizations has been around for a long time. Organizations constantly theorize and implement different approaches that provide some sort of alignment between their different business objectives and Information Systems. Unfortunately, the environments in which these organizations operate are often dynamic, constantly changing with influence from external and internal factors that require continual realignment of the Information Systems with business objectives to provide value. When businesses evolve, leading to changes in business requirements, it is hard to know what direct Information System changes are needed to respond to the new requirements. Similarly, when there are changes in the Information System, it is not often easy to discern which business objectives are directly affected. Whilst the different Enterprise Architecture frameworks available today provide and propose some form of alignment, in their implementation, they do not show links between business objectives and Information Systems, i.e., indicating what Information System is directly responsible for different business objectives thereby allowing for anticipation and support of changes as the business evolves. This thesis utilizes insights from Business Intelligence and uses the User Requirements Notation (URN), which enables modeling of business processes and goals, to provide a framework that exploits links between business objectives and Information Systems. This Business Intelligence - Enabled Adaptive Enterprise Architecture framework allows for anticipating and supporting proactively the adaptation of Enterprise Architecture as and when the business evolves. The thesis also identifies and models levels within the enterprise where responses to change as the business evolves are needed and the ways the changes are presented. The tool-supported framework is evaluated against the different levels and types of changes on a realistic Enterprise Architecture at a Government of Canada department, with encouraging results.

The desire to obtain value and justify investments from the different Information Systems in place in organizations has been around for a long time. Organizations constantly theorize and implement different approaches that provide some sort of alignment between their different business objectives and Information Systems. Unfortunately, the environments in which these organizations operate are often dynamic, constantly changing with influence from external and internal factors that require continual realignment of the Information Systems with business objectives to provide value. When businesses evolve, leading to changes in business requirements, it is hard to know what direct Information System changes are needed to respond to the new requirements. Similarly, when there are changes in the Information System, it is not often easy to discern which business objectives are directly affected. Whilst the different Enterprise Architecture frameworks available today provide and propose some form of alignment, in their implementation, they do not show links between business objectives and Information Systems, i.e., indicating what Information System is directly responsible for different business objectives thereby allowing for anticipation and support of changes as the business evolves. This thesis utilizes insights from Business Intelligence and uses the User Requirements Notation (URN), which enables modeling of business processes and goals, to provide a framework that exploits links between business objectives and Information Systems. This Business Intelligence - Enabled Adaptive Enterprise Architecture framework allows for anticipating and supporting proactively the adaptation of Enterprise Architecture as and when the business evolves. The thesis also identifies and models levels within the enterprise where responses to change as the business evolves are needed and the ways the changes are presented. The tool-supported framework is evaluated against the different levels and types of changes on a realistic Enterprise Architecture at a Government of Canada department, with encouraging results.

Context: Process mining is an approach that exploits event logs to discover real processes executed in organizations, enabling them to (re)design and improve process models. Goal modelling, on the other hand, is a requirements engineering (RE) approach mainly used to analyze what-if situations and support decision making. Problem: Common problems with process mining include the complexity of discovered “spaghetti” processes and a lack of goal-process alignment. Current process mining practices mainly focus on activities and do not benefit from considering stakeholder goals and requirements to manage complexity and alignment. The critical artifact that process mining practices rely on is the event log. However, using a raw version of real-life event logs will typically result in process models being too complex, unstructured, difficult to understand and, above all, not aligned with stakeholders’ goals. Method: Involving goal-related factors can augment the precision and interpretability of mined models and help discover better opportunities to satisfy stakeholders. This thesis proposes three algorithms for goal-oriented process enhancement and discovery (GoPED) that show synergetic effects achievable by combining process mining and goal-oriented modelling. With GoPED, good historical experiences will be found within the event log to be used as a basis for inferring good process models, and bad experiences will be found to discover models to avoid. The goodness is defined in terms of alignment with regards to three categories of goal-related criteria: • Case perspective: satisfaction of individual cases (e.g., patient, costumer) in terms of some goals; • Goal perspective: overall satisfaction of some goals (e.g., to decrease waiting time) rather than individual cases; and • Organization perspective: a comprehensive satisfaction level for all goals over all cases. GoPED first adds goal-related attributes to conventional event characteristics (case identifier, activities, and timestamps), selects a subset of cases concerning goal-related criteria, and finally discovers a process model from that subset. For each criterion, an algorithm is developed to enable selecting the best subset of cases where the criterion holds. The resulting process models are expected to reproduce the desired level of satisfaction. The three GoPED algorithms were implemented in a Python tool. In addition, three other tools were implemented to complete a line of actions whose input is a raw event log and output is a subset of the event log selected with respect to the goal-related criteria. GoPED was used on real healthcare event logs (an illustrative example and a case study) to discover processes, and the performance of the tools was also assessed. Results: The performance of the GoPED toolset for various sizes and configurations of event logs was assessed through extensive experiments. The results show that the three GoPED algorithms are practical and scalable for application to event logs with realistic sizes and types of configurations. The GoPED method was also applied to the discovery of processes from the raw event log of the trajectories of patients with sepsis in a Dutch hospital, from their registration in the emergency room until their discharge. Although the raw data does not explicitly include goal-related information, some reasonable goals were derived from the data and a related research paper in consultation with a healthcare expert. The method was applied, and the resulting models were i) substantially simpler than the model dis-covered from the whole event log, ii) free from the drawbacks that using the whole event log causes, and iii) aligned with the predefined goals. Conclusion: GoPED demonstrates the benefits of exploiting goal modelling capabilities to enhance event logs and select a subset of events to discover goal-aligned and simplified process models. The resulting process model can also be compared to a model discovered from the original event log to reveal new insights about the ability of different forms of process models to satisfy the stakeholders’ goals. Learning from good behaviours that satisfy goals and detecting bad behaviours that hurt them is an opportunity to redesign models, so they are simpler, better aligned with goals, and free from drawbacks that using the whole event log may cause.

Background: Business process compliance management has recently attracted a lot of attention in both business and academia as it enables organizations to not only control and monitor their business processes from a legal point of view but also to avoid financial penalties and undesirable consequences to their reputation. Objective: This thesis aims to provide a framework that would enable organizations to: 1- Discover business processes that violate regulations, laws and policies; 2- Discover the importance level of business processes based on the organization’s goals; 3- Determine the impact of compliance-related process modifications on business goals, including conflicting goals between stakeholders, and on policies; and 4- Enable organizations to measure the level of business process compliance for one or multiple policies. Methodology: A systematic literature review in the area of goal-oriented business process compliance management and measurement has been conducted, which showed that balancing legal compliance obligations with business objectives remains a difficult challenge. A new Indicator-based Policy Compliance Framework (IPCF), which combines policy and rule models together with models capturing business goals (with their relative importance to the organization) and business processes, has been proposed. This framework builds on the User Requirements Notation (URN), which is the first international standard to combine goal modeling with scenario modeling. The intents and objectives of policies have been modeled, as well as the goals and business processes of organizations, and indicators are used to measure the compliance level of policies. This enables the detection of non-compliant business processes and the evaluation of the impact of compliance-related process modifications on business goals. Human resource policies and business processes are used as an example to illustrate the method. Aerodrome security regulations and business processes are then used to validate the method in a real-life environment. Comparisons to related work, evaluation against different sets of criteria, and tool support complement the framework validation. Results: The Indicator-based Policy Compliance Framework enables organizations to discover business processes that violate policies as well as other types of rules, regulations, and laws. Guidelines for modeling legal text with URN’s Goal-oriented Requirement Language (GRL) are proposed. Furthermore, IPCF helps determine the impact of compliance-related process modifications on business goals, including conflicting goals between stakeholders, and on policies. In addition, as policies sometimes apply differently to different types of organizations, a new profile for GRL, with suitable stereotypes, well-formedness constraints, and a modified analysis algorithm defined for GRL model families is used to evaluate the satisfaction level of individual goal models that are members of a larger family model. Finally, the proposed IPCF enables organizations to measure the level of business process compliance for one or multiple policies, and such measures can be visualized directly in URN models but also through interactive Business Intelligence portals, for a wider diffusion.