Modeling and Simulating Causal Dependencies on Process-aware Information Systems from a Cost Perspective

Providing effective IT support for business processes has become crucial for enterprises to stay competitive in their market. Business processes must be defined, implemented, enacted, monitored, and continuously adapted to changing situations. Process life cycle support and continuous process improvement become critical success factors in contemporary and future enterprise computing. In this context, process-aware information systems (PAISs) adopt a key role. Thereby, organization-specific and generic process support systems are distinguished. In the former case, the PAIS is build "from scratch" and incorporates organization-specific information about the structure and processes to be supported. In the latter case, the PAIS does not contain any information about the structure and processes of a particular organization. Instead, an organization needs to configure the PAIS by specifying processes, organizational entities, and business objects. To enable the realization of PAISs, numerous process support paradigms, process modeling standards, and business process management tools have been introduced. The application of these approaches in PAIS engineering projects is not only influenced by technological, but also by organizational and project-specific factors. Between these factors there exist numerous causal dependencies, which, in turn, often lead to complex and unexpected effects in PAIS engineering projects. In particular, the costs of PAIS engineering projects are significantly influenced by these causal dependencies. What is therefore needed is a comprehensive approach enabling PAIS engineers to systematically investigate these causal dependencies as well as their impact on the costs of PAIS engineering projects. Existing economic-driven IT evaluation and software cost estimation approaches, however, are unable to take into account causal dependencies and resulting effects. In response, this thesis introduces the EcoPOST framework. This framework utilizes evaluation models to describe the interplay of technological, organizational, and project-specific evaluation factors, and simulation concepts to unfold the dynamic behavior of PAIS engineering projects. In this context, the EcoPOST framework also supports the reuse of evaluation models based on a library of generic, predefined evaluation patterns and also provides governing guidelines (e.g., model design guidelines) which enhance the transfer of the EcoPOST framework into practice. Tool support is available as well. Finally, we present the results of two online surveys, three case studies, and one controlled software experiment. Based on these empirical and experimental research activities, we are able to validate evaluation concepts underlying the EcoPOST framework and additionally demonstrate its practical applicability

Domain independent strategies in an affective tutoring system

There have been various attempts to develop an affective tutoring system (ATS) framework that considers and reacts to a student’s emotions while learning. However, there is a gap between current systems and the theory underlying human appraisal models. The current frameworks rely on a single appraisal and reaction phase. In contrast, the human appraisal process (Lazarus, 1991) involves two phases of appraisal and reaction (i.e. primary and secondary appraisal phases). This thesis proposes an affective tutoring (ATS) framework that introduces two phases of appraisal and reaction (i.e. primary and secondary appraisal and reaction phases). This proposed framework has been implemented and evaluated in a system to teach Data Structures. In addition, the system employs both domain-dependent and domain-independent strategies for coping with students’ affective states. This follows the emotion regulation model (Lazarus, 1991) that underpins the ATS framework which argues that individuals use both kinds of strategies in solving daily life problems. In comparison, current affective (ITS) frameworks concentrate on the use of domain-dependent strategies to cope with students’ affective states. The evaluation of the system provides some support for the idea that the ATS framework is useful both in improving students’ affective states (i.e. during and by the end of a learning session) and also their learning performance

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

Realist evaluation for programs designed to reduce demand and harms of substance misuse at the community level in Australian remote Indigenous community settings

This thesis reviewed NHMRC-funded, community-level substance misuse interventions, documenting: outcomes; study designs; implicit program theory; and assumptions. Data from one intervention project, targeting cannabis misuse in Cape York, exemplified common evaluation constraints, and informed hypothetical context-mechanism-outcome clusters for a plausible program theory and proposed theory-driven evaluation

A causal model to explain data reuse in science: a study in health disciplines

[EN] Investments in data infrastructures, data management, data repositories, and Open Data sharing policies and recommendations are viewed as increasingly important for scientific knowledge production. One of the underlying assumptions justifying these investments is that the more available Open Data becomes, then the greater the possibilities for creating new knowledge that can advance both science and human wellbeing. Yet efforts and investments in Open Data and other ways of data sharing only have value if data are actually reused. Recent scholarly efforts have brought forth some of the challenges and facilitators related to the reuse of data, in order to inform current and future policies and investments. However, despite these efforts, we still do not know why and how some researchers are successful in reusing data, despite the challenges they face, and why some researchers abandon the process of reusing data when facing such challenges. This dissertation aims to fill this gap by focusing on a causal explanation of the data reuse process, which it understands as being nested in broader patterns of researchers' motivations, scientific goals and decision-making strategies. The dissertation is comprised of three main elements. First, it proposes a heuristic model of the scientific actor, the bounded individual horizon (BIH) model, which understands that, on the one hand, researchers' work and careers are structured by their motivation to produce scientific contributions and rewards systems that prioritizes certain types of contributions. On the other hand, researchers' struggles to achieve their objective of creating new findings that accrue recognition and rewards occur within a frame of limited information and resources, conditioned by multiple institutional, social, and other factors. Second, the study proposes a mechanistic causal theoretical explanation that enables us to understand the data reuse process and its effects (outcomes). The data-reuse mechanism as it is called, enables us to understand how the satisficing behavior that characterizes scientific decision-making applies to the specific conditions and processes of data reuse. Third, a set of ten empirical case studies of data reuse in health research were conducted and are reported in the dissertation. These cases are analyzed and interpreted using the complementary theoretical lenses of the bounded individual horizon and the data-reuse mechanism approaches. The main findings explain that there is an apparent association between the extent and types of efforts required to reuse data, researchers' contextualized motivations, and broader goal-setting and decision-making frames. Access to data is a necessary condition for the reuse of data, yet is not sufficient for the reuse to happen. Characteristics of available data, including the context of their production, the extent of the preparation and stewarding of these data and their potential value in relation to researchers' motivations to make new scientific claims or generate background knowledge are found to be essential elements for understanding why some data reuse processes persist and succeed, while others do not. The thesis concludes that efforts and investments designed to reap the benefits of data reuse should also be expanded to include training researchers in data reuse, including to efficiently recognize opportunities, navigate the challenges of the reuse process, and be aware of and acknowledge the limitations of the use of secondary data. Without such investments, the promises and expectations linked to emerging data infrastructures, data repositories, data management guidelines and open science practices are argued to be far less likely to reach their full potential.[ES] Las inversiones en infraestructuras de datos, gestión de datos, repositorios de datos y políticas y recomendaciones de intercambio de Datos Abiertos (Open Data) se consideran cada vez más importantes para la producción del conocimiento científico. Una de las razones que justifica estas inversiones es que cuanto más Datos Abiertos haya, mayores serán las posibilidades de crear nuevo conocimiento que pueda hacer avanzar tanto la ciencia como el bienestar humano. Sin embargo, los esfuerzos y la inversión en Datos Abiertos y otras formas de compartirlos sólo tienen valor si se reutilizan realmente. Recientes trabajos académicos han puesto de manifiesto algunos de los retos y factores facilitadores relacionados con la reutilización de los datos, a fin de asesorar las políticas e inversiones actuales y futuras. Sin embargo, a pesar de esos esfuerzos, todavía desconocemos por qué y cómo algunos/as investigadores/as logran reutilizar los datos, a pesar de los retos a los que enfrentan, y por qué otros/as investigadores/as abandonan el proceso de reutilización de los datos. La presente tesis tiene por objeto llenar este vacío centrándose en una explicación causal del proceso de reutilización de los datos, que se entiende está inmersa en pautas de conducta más amplias que se relacionan con las motivaciones, los objetivos científicos y las estrategias de toma de decisiones de los/as investigadores/as. Este estudio consta de tres elementos principales. En primer lugar, propone un modelo heurístico del actor científico, el modelo del horizonte individual delimitado (BIH por su nombre en inglés, bounded individual horizon). En él se entiende que, por una parte, el trabajo y la carrera de los/as investigadores/as se estructuran en función de su motivación para producir contribuciones científicas y de los sistemas de recompensa que dan prioridad a determinados tipos de contribuciones. Por otra parte, los esfuerzos de los/as investigadores/as para lograr su objetivo de crear nuevos hallazgos que acumulen reconocimiento y recompensas se producen en un marco de información y recursos limitados, condicionados por múltiples factores institucionales, sociales y de otra índole. En segundo lugar, esta tesis propone una explicación teórica causal mecanicista que permite comprender el proceso de reutilización de los datos y sus efectos (resultados). El mecanismo de reutilización de datos (datareuse mechanism), como se denomina, nos permite comprender cómo la toma de decisiones científicas está caracterizada por una conducta que tiende a satisfacer esos objetivos en unas condiciones y procesos específicos de reutilización de datos. En tercer lugar, este estudio incluye los resultados del estudio empírico de diez estudios de casos de reutilización de datos en ciencias de la salud. Estos casos se han analizado e interpretado utilizando el modelo teórico del horizonte individual delimitado y los enfoques del mecanismo de reutilización de datos. Los resultados principales explican que existe una aparente asociación entre el alcance el alcance y tipo de esfuerzo requerido para reutilizar datos, las motivaciones contextualizadas de los/as investigadores/as y marcos más amplios de fijación de objetivos y toma de decisiones. El acceso a los datos es una condición necesaria para su reutilización, pero no es suficiente para que ésta se produzca. Para comprender por qué algunos procesos de reutilización de datos persisten y tienen éxito, mientras que otros no,son elementos esenciales: las características de los datos disponibles, incluido el contexto de su producción; el grado de preparación y administración de esos datos; y su potencial valor en relación con las motivaciones de los investigadores para hacer nuevas afirmaciones científicas o generar conocimientos de base. Este estudio concluye que los esfuerzos e inversiones destinados a aprovechar los beneficios de la reutilización de los datos también deberían ampliarse para incluir la capacitación de los/as investigadores/as en materia de reutilización de datos. En particular, debe insistirse en la capacidad para reconocer eficientemente las oportunidades, sortear los problemas del proceso de reutilización y ser conscientes y reconocer las limitaciones de la utilización de datos secundarios. Sin estas inversiones, las promesas y expectativas vinculadas a las emergentes infraestructuras de datos, los repositorios de datos, las directrices de gestión de datos y las prácticas científicas abiertas tienen muchas menos probabilidades de alcanzar su pleno potencial.[CA] Les inversions en infraestructures de dades, gestió de dades, repositoris de dades i polítiques i recomanacions d'intercanvi de Dades Obertes (Open Data) es consideren cada vegada més importants per a la producció del coneixement científic. Un dels supòsits subjacents que justifiquen aquestes inversions és que com més disponibles siguen les Dades Obertes, majors seran les possibilitats de crear nou coneixement que pugui fer avançar tant la ciència com el benestar humà. No obstant això, els esforços i les inversions en les Dades Obertes i altres maneres de compartir dades només tenen valor si les dades es reutilitzen realment. Recents investigacions acadèmics han posat de manifest alguns dels reptes i dels factors facilitadors relacionats amb la reutilització de les dades, a fi d'informar les polítiques i inversions actuals i futures. No obstant això, encara desconeixem per què i com alguns/es investigador(e)s aconsegueixen reutilitzar les dades, malgrat els reptes als quals s’enfronten, i per què altres investigador(e)s abandonen el procés de reutilització de les dades quan s'enfronten a aquests reptes. La present tesi té com a objectiu omplir aquest buit centrant-se en una explicació causal del procés de reutilització de dades, que s'entén que està associada amb pautes més àmplies derivades de les motivacions, els objectius científics i les estratègies de presa de decisions d’els/les investigador(e)s. La tesi consta de tres elements principals. En primer lloc, proposa un model heurístic de l'actor científic, el model de l'horitzó individual delimitat (BIH pel nom anglès, bounded individual horizon), que entén que, d'una banda, el treball i la carrera d’els/les investigador(e)s s'estructuren en funció de la seua motivació per a produir contribucions científiques i dels sistemes de recompensa que prioritzen determinats tipus de contribucions. D'altra banda, els esforços d’els/les investigador(e)s per aconseguir el seu objectiu d’obtenir nous resultats que acumulin reconeixement i recompenses es produeixen en un marc d'informació i recursos limitats, condicionats per múltiples factors institucionals, socials i d'altra índole. En segon lloc, aquesta tesi proposa una explicació teòrica causal mecanicista que permet comprendre el procés de reutilització de les dades i els seus efectes (resultats). El mecanisme de reutilització de dades (data-reuse mechanism), com es denomina, ens permet comprendre com el comportament satisfactori que caracteritza la presa de decisions científiques s'aplica a les condicions i processos específics de reutilització de dades. En tercer lloc, aquesta tesi inclou l'estudi empíric d'un conjunt de deu estudis de casos de reutilització de dades en ciències de la salut, així com també els resultats d’aquest estudi.. Aquests casos s'han analitzat i interpretat utilitzant les lents teòriques complementàries de l'horitzó individual delimitat i els enfocaments del mecanisme de reutilització de dades. Les principals conclusions expliquen que existeix una aparent associació entre l'abast i els tipus d'esforços necessaris per a reutilitzar dades, les motivacions contextualitzades d’els/les investigador(e)s i els marcs més amplis de fixació d'objectius i presa de decisions. L'accés a les dades és una condició necessària per a la seua reutilització, però no és suficient perquè aquesta es produeixi. Es considera que les característiques de les dades disponibles, inclòs el context de la seua producció, el grau de preparació i administració d'aquestes dades i el seu potencial valor en relació amb les motivacions d’els/les investigador(e)s per a fer noves afirmacions científiques o generar coneixements de base, són elements essencials per a comprendre per què alguns processos de reutilització de dades persisteixen i tenen èxit, mentre que uns altres no. Aquest estudi conclou que els esforços i inversions destinats a aprofitar els beneficis de la reutilització de dades també haurien d'ampliar-se per a incloure la capacitació d’els/les investigador(e)s en matèria de reutilització de dades, en particular per a reconèixer eficientment les oportunitats, superar els problemes del procés de reutilització i ser conscients i reconèixer les limitacions de la reutilització de dades secundàries. Sense aquests esforços i inversions, les promeses i expectatives vinculades a les infraestructures, repositoris i directrius de gestió de dades i les pràctiques científiques obertes tenen moltes menys probabilitats d'aconseguir el seu ple potencial.Aleixos Borrás, MI. (2020). A causal model to explain data reuse in science: a study in health disciplines [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/153164TESI