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On Enabling Integrated Process Compliance with Semantic Constraints in Process Management Systems

Key to broad use of process management systems (PrMS) in practice is their ability to foster and ease the implementation, execution, monitoring, and adaptation of business processes while still being able to ensure robust and error-free process enactment. To meet these demands a variety of mechanisms has been developed to prevent errors at the structural level (e.g., deadlocks). In many application domains, however, processes often have to comply with business level rules and policies (i.e., semantic constraints) as well. Hence, to ensure error-free executions at the semantic level, PrMS need certain control mechanisms for validating and ensuring the compliance with semantic constraints. In this paper, we discuss fundamental requirements for a comprehensive support of semantic constraints in PrMS. Moreover, we provide a survey on existing approaches and discuss to what extent they are able to meet the requirements and which challenges still have to be tackled. In order to tackle the particular challenge of providing integrated compliance support over the process lifecycle, we introduce the SeaFlows framework. The framework introduces a behavioural level view on processes which serves a conceptual process representation for constraint specification approaches. Further, it provides general compliance criteria for static compliance validation but also for dealing with process changes. Altogether, the SeaFlows framework can serve as formal basis for realizing integrated support of semantic constraints in PrMS

Pop-up Maktivism: A Case Study of Organizational, Pharmaceutical, and Biohacker Narratives

abstract: The biohacker movement is an important and modern form of activism. This study broadly examines how positive-activist-oriented biohackers emerge, organize, and respond to social crises. Despite growing public awareness, few studies have examined biohacking's influence on prevailing notions of organizing and medicine in-context. Therefore, this study examines biohacking in the context of the 2016 EpiPen price-gouging crisis, and explores how biohackers communicatively attempted to constitute counter-narratives and counter-logics about medical access and price through do-it-yourself (DIY) medical device alternatives. Discourse tracing and critical case study analysis are useful methodological frameworks for mapping the historical discursive and material logics that led to the EpiPen pricing crisis, including the medicalization of allergy, the advancement of drug-device combination technologies, and role of public health policy, and pharmaceutical marketing tactics. Findings suggest two new interpretations for how non-traditional forms of organizing facilitate new modes of resistance in times of institutional crisis. First, the study considers the concept of "pop-up maktivism" to conceptualize activism as a type of connective activity rather than collective organizing. Second, findings illustrate how activities such as participation and co-production can function as meaningful forms of institutional resistance within dominant discourses. This study proposes “mirrored materiality” to describe how biohackers deploy certain dominant logics to contest others. Lastly, implications for contributions to the conceptual frameworks of biopower, sociomateriality, and alternative organizing are discussed.Dissertation/ThesisDoctoral Dissertation Communication 201

Event-Oriented Dynamic Adaptation of Workflows: Model, Architecture and Implementation

Workflow management is widely accepted as a core technology to support long-term business processes in heterogeneous and distributed environments. However, conventional workflow management systems do not provide sufficient flexibility support to cope with the broad range of failure situations that may occur during workflow execution. In particular, most systems do not allow to dynamically adapt a workflow due to a failure situation, e.g., to dynamically drop or insert execution steps. As a contribution to overcome these limitations, this dissertation introduces the agent-based workflow management system AgentWork. AgentWork supports the definition, the execution and, as its main contribution, the event-oriented and semi-automated dynamic adaptation of workflows. Two strategies for automatic workflow adaptation are provided. Predictive adaptation adapts workflow parts affected by a failure in advance (predictively), typically as soon as the failure is detected. This is advantageous in many situations and gives enough time to meet organizational constraints for adapted workflow parts. Reactive adaptation is typically performed when predictive adaptation is not possible. In this case, adaptation is performed when the affected workflow part is to be executed, e.g., before an activity is executed it is checked whether it is subject to a workflow adaptation such as dropping, postponement or replacement. In particular, the following contributions are provided by AgentWork: A Formal Model for Workflow Definition, Execution, and Estimation: In this context, AgentWork first provides an object-oriented workflow definition language. This language allows for the definition of a workflow\u92s control and data flow. Furthermore, a workflow\u92s cooperation with other workflows or workflow systems can be specified. Second, AgentWork provides a precise workflow execution model. This is necessary, as a running workflow usually is a complex collection of concurrent activities and data flow processes, and as failure situations and dynamic adaptations affect running workflows. Furthermore, mechanisms for the estimation of a workflow\u92s future execution behavior are provided. These mechanisms are of particular importance for predictive adaptation. Mechanisms for Determining and Processing Failure Events and Failure Actions: AgentWork provides mechanisms to decide whether an event constitutes a failure situation and what has to be done to cope with this failure. This is formally achieved by evaluating event-condition-action rules where the event-condition part describes under which condition an event has to be viewed as a failure event. The action part represents the necessary actions needed to cope with the failure. To support the temporal dimension of events and actions, this dissertation provides a novel event-condition-action model based on a temporal object-oriented logic. Mechanisms for the Adaptation of Affected Workflows: In case of failure situations it has to be decided how an affected workflow has to be dynamically adapted on the node and edge level. AgentWork provides a novel approach that combines the two principal strategies reactive adaptation and predictive adaptation. Depending on the context of the failure, the appropriate strategy is selected. Furthermore, control flow adaptation operators are provided which translate failure actions into structural control flow adaptations. Data flow operators adapt the data flow after a control flow adaptation, if necessary. Mechanisms for the Handling of Inter-Workflow Implications of Failure Situations: AgentWork provides novel mechanisms to decide whether a failure situation occurring to a workflow affects other workflows that communicate and cooperate with this workflow. In particular, AgentWork derives the temporal implications of a dynamic adaptation by estimating the duration that will be needed to process the changed workflow definition (in comparison with the original definition). Furthermore, qualitative implications of the dynamic change are determined. For this purpose, so-called quality measuring objects are introduced. All mechanisms provided by AgentWork include that users may interact during the failure handling process. In particular, the user has the possibility to reject or modify suggested workflow adaptations. A Prototypical Implementation: Finally, a prototypical Corba-based implementation of AgentWork is described. This implementation supports the integration of AgentWork into the distributed and heterogeneous environments of real-world organizations such as hospitals or insurance business enterprises

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

Evaluating the Impact of Defeasible Argumentation as a Modelling Technique for Reasoning under Uncertainty

Limited work exists for the comparison across distinct knowledge-based approaches in Artificial Intelligence (AI) for non-monotonic reasoning, and in particular for the examination of their inferential and explanatory capacity. Non-monotonicity, or defeasibility, allows the retraction of a conclusion in the light of new information. It is a similar pattern to human reasoning, which draws conclusions in the absence of information, but allows them to be corrected once new pieces of evidence arise. Thus, this thesis focuses on a comparison of three approaches in AI for implementation of non-monotonic reasoning models of inference, namely: expert systems, fuzzy reasoning and defeasible argumentation. Three applications from the fields of decision-making in healthcare and knowledge representation and reasoning were selected from real-world contexts for evaluation: human mental workload modelling, computational trust modelling, and mortality occurrence modelling with biomarkers. The link between these applications comes from their presumptively non-monotonic nature. They present incomplete, ambiguous and retractable pieces of evidence. Hence, reasoning applied to them is likely suitable for being modelled by non-monotonic reasoning systems. An experiment was performed by exploiting six deductive knowledge bases produced with the aid of domain experts. These were coded into models built upon the selected reasoning approaches and were subsequently elicited with real-world data. The numerical inferences produced by these models were analysed according to common metrics of evaluation for each field of application. For the examination of explanatory capacity, properties such as understandability, extensibility, and post-hoc interpretability were meticulously described and qualitatively compared. Findings suggest that the variance of the inferences produced by expert systems and fuzzy reasoning models was higher, highlighting poor stability. In contrast, the variance of argument-based models was lower, showing a superior stability of its inferences across different system configurations. In addition, when compared in a context with large amounts of conflicting information, defeasible argumentation exhibited a stronger potential for conflict resolution, while presenting robust inferences. An in-depth discussion of the explanatory capacity showed how defeasible argumentation can lead to the construction of non-monotonic models with appealing properties of explainability, compared to those built with expert systems and fuzzy reasoning. The originality of this research lies in the quantification of the impact of defeasible argumentation. It illustrates the construction of an extensive number of non-monotonic reasoning models through a modular design. In addition, it exemplifies how these models can be exploited for performing non-monotonic reasoning and producing quantitative inferences in real-world applications. It contributes to the field of non-monotonic reasoning by situating defeasible argumentation among similar approaches through a novel empirical comparison

SeaFlows – A Compliance Checking Framework for Supporting the Process Lifecycle

Compliance-awareness is undoubtedly of utmost importance for companies nowadays. Even though an automated approach to compliance checking and enforcement has been advocated in recent literature as a means to tame the high costs for compliance-awareness, the potential of automated mechanisms for supporting business process compliance is not yet depleted. Business process compliance deals with the question whether business processes are designed and executed in harmony with imposed regulations. In this thesis, we propose a compliance checking framework for automating business process compliance verification within process management systems (PrMSs). Such process-aware information systems constitute an ideal environment for the systematic integration of automated business process compliance checking since they bring together different perspectives on a business process and provide access to process data. The objective of this thesis is to devise a framework that enhances PrMSs with compliance checking functionality. As PrMSs enable both the design and the execution of business processes, the designated compliance checking framework must accommodate mechanisms to support these different phases of the process lifecycle. A compliance checking framework essentially consists of two major building blocks: a compliance rule language to capture compliance requirements in a checkable manner and compliance checking mechanisms for verification of process models and process instances. Key to the practical application of a compliance checking framework will be its ability to provide comprehensive and meaningful compliance diagnoses. Based on the requirements analysis and meta-analyses, we developed the SeaFlows compliance checking framework proposed in this thesis. We introduce the compliance rule graph (CRG) language for modeling declarative compliance rules. The language provides modeling primitives with a notation based on nodes and edges. A compliance rule is modeled by defining a pattern of activity executions activating a compliance rule and consequences that have to apply once a rule becomes activated. In order to enable compliance verification of process models and process instances, the CRG language is operationalized. Key to this approach is the exploitation of the graph structure of CRGs for representing compliance states of the respective CRGs in a transparent and interpretable manner. For that purpose, we introduce execution states to mark CRG nodes in order to indicate which parts of the CRG patterns can be observed in a process execution. By providing rules to alter the markings when a new event is processed, we enable to update the compliance state for each observed event. The beauty of our approach is that both design and runtime can be supported using the same mechanisms. Thus, no transformation of compliance rules in different representations for process model verification or for compliance monitoring becomes necessary. At design time, the proposed approach can be applied to explore a process model and to detect which compliance states with respect to imposed CRGs a process model is able to yield. At runtime, the effective compliance state of process instances can be monitored taking also the future predefined in the underlying process model into account. As compliance states are encoded based on the CRG structure, fine-grained and intelligible compliance diagnoses can be derived in each detected compliance state. Specifically, it becomes possible to provide feedback not only on the general enforcement of a compliance rule but also at the level of particular activations of the rule contained in a process. In case of compliance violations, this can explain and pinpoint the source of violations in a process. In addition, measures to satisfy a compliance rule can be easily derived that can be seized for providing proactive support to comply. Altogether, the SeaFlows compliance checking framework proposed in this thesis can be embedded into an overall integrated compliance management framework

Raciocínio baseado na ética médica na decisão em grupo

Tese de doutoramento em Engenharia BiomédicaO processo de tomada de decisões, em qualquer que seja a área envolvente, tem sempre por base regras baseadas no consentimento, que foram previamente trabalhadas por vários conselhos. Quando focamos em processos de decisões éticas, a chave para o sucesso deste processo está assente na partilha de valores (sejam eles morais ou não) e objetivos. A ética aplicada à saúde surge nos anos 70 em resposta aos conflitos morais relacionados com os cuidados aos doentes, com a investigação médica e com as novas tecnologias. Desde então a ética, na relação médico-doente, evoluiu para várias outras áreas como a genética, a medicina reprodutiva, a alocação de recursos, entre outros. Cada vez mais, as evoluções tecnológicas fazem com que a esperança média de vida aumente e, consequentemente, a população envelheça. Este facto faz com que, como consequência do aumento das doenças crónica, da interação da saúde com outras áreas como a educação e o ambiente, na igualdade de acessos a serviços e medicamentos, sejam levantados desafios éticos aos profissionais da saúde que têm como tarefa diária a tomada de decisões. As agravantes disparam ainda mais quando aparece um vírus desconhecido, tal como o SARS-Cov-2, pondo em risco o principio da medicina baseado na evidência, o que torna esta tarefa de tomar decisões ainda mais árdua. Assim sendo, o estudo de modelos para o raciocínio ético tem sido fonte de estudo e de discussão em diferentes comunidades científicas. No entanto nenhum modelo revelou superioridade indiscutível sobre os restantes. O contexto de aplicação da moral leva a preferir uma metodologia em relação a outras. Em áreas tais como a Medicina, onde a qualidade de vida e a própria vida de um paciente estão em jogo, a capacidade de tornar o processo de raciocínio transparente, justificável e dinâmico tem uma importância central. Neste documento, apresentam-se linhas orientadoras do raciocínio ético aplicado à Medicina e defende-se que a programação em Lógica Contínua apresenta grande potencial para o desenvolvimento de sistemas de suporte à decisão confiáveis, com base na moralidade. Apresentam-se ainda casos de estudo relativos à Unidade de Cuidados Intensivos, um serviço crítico onde a complexidade moral das decisões diárias é uma motivação para a análise e o desenvolvimento de metodologias para o suporte à decisão com base na moralidade.The decision-making process in any given area is always based on consent-based rules that have been previously worked out by various councils. When we focus on ethical decision-making processes, the key to success lies in shared values (whether moral or otherwise) and goals. Besides, this process must be based on transparency, trust, and above all, conscience. Health ethics emerged in the 1970s in response to moral conflicts related to patient care, medical research, and new technologies. Since then, ethics, in the doctor-patient relationship, has evolved into several other areas such as genetics, reproductive medicine, resource allocation, among others. Today, in health care institutions, ethics focuses on respect for the person. This overlaps with the overall health of the population, which in turn is more determined by public health policies. Increasingly, technological developments are causing the average life expectancy to increase and, consequently, the population to age. As a consequence of the increase in chronic diseases, the interaction of health with other areas such as education and the environment, and equal access to services and medicines, ethical challenges are raised for health professionals whose daily task is to make decisions. The aggravating factors get even worse when an unknown virus, such as SARS-Cov-2, appears, jeopardizing the principle of evidence-based medicine, which makes this task of making decisions even more arduous. Therefore, the study of models for an ethical reasoning has been a source of study and discussion in different scientific communities. However, no model has revealed indisputable superiority over the others. The context in which morality is applied leads to a preference for one methodology over others. In areas such as medicine, where the quality of life and the very life of a patient are at stake, the ability to make the reasoning process transparent, justifiable, and dynamic is of central importance. In this paper, guidelines for ethical reasoning applied to Medicine are presented and it is argued that Continuous Logic programming has great potential for the development of reliable decision support systems based on morality. As an example, case studies are presented of situations related to an Intensive Care Unit, a critical service where the moral complexity of daily decisions is a motivation for the analysis and development of methodologies for decision support based on morality