A systematic review of data quality issues in knowledge discovery tasks

Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust

Organisational forgetting:The food safety risk associated with unintentional knowledge loss

Background: Organisational forgetting is associated with unintentional knowledge loss that makes both food businesses and consumers vulnerable to a food safety incident. It is essential that food businesses have strategies and processes in place to minimise unintentional knowledge loss to ensure that essential knowledge is retained, maintained and stays valid. Scope and approach: The aim of this paper is to consider the risk associated with unintentional food safety knowledge loss at individual, organisational and inter-organisational levels. The research approach employed was to undertake a review of existing literature to frame the conceptual research. Screening of both academic and grey literature demonstrated a distinct knowledge gap i.e., there is limited previous research considering the concept of unintentional knowledge loss and its impact on food safety. Case study examples explore the academic theory in more depth. Key findings and conclusions: Three aspects of organisational forgetting are considered in the context of food safety: organisational amnesia, organisational memory decay, and supply chain déjà-vu. The first two aspects operate at the organisational level and the third at the supply chain level. To overcome the risk of unintentional loss, organisational and interorganisational knowledge needs to be effectively mapped and a knowledge retention policy needs to be developed, implemented and maintained that addresses all types of organisational and interorganisational knowledge, but especially food safety knowledge

Care, Compassion and Self-compassion: A Mixed Methods, Realistic Evaluation of a Massive Open Online Course (MOOC)

Abstract Background Compassion is intrinsic within modern healthcare and is heavily debated and discussed within current news and literature (Dewar and Nolan, 2013; Mills et al, 2015). Massive Open Online Courses (MOOCs) are a relatively new phenomenon not just in healthcare but also in education, as a whole (Sarabia-Cobo et al, 2015; Parkinson, 2015). Their main purpose is to capture the attention of a diverse and global audience in order to increase knowledge and understanding through the provision of university level education (Sneddon et al, 2018; Hebdon et al, 2016). The Scottish Improvement Science Collaborating Centre, University of Dundee, developed a care and compassion MOOC hosted by FutureLearn, a digital education platform. This five-week MOOC provided learning resources, activities and information to healthcare professionals and the public in order to raise awareness and understanding of compassion and help improve the provision of compassionate care. Aim The aim of this research was to (1) evaluate a new educational intervention, delivered by a MOOC, focused on compassion, and (2) consider how and why it could help facilitate change in the attitudes, behaviours, and practices of healthcare professionals. Design A Realistic Evaluation approach was taken which allowed for an understanding of how an educational intervention could facilitate change in healthcare professionals by asking how and what, worked for whom, in what circumstances. The realistic evaluation design was underpinned by the philosophical principle of pragmatism which allowed for the who, what and why questions to be answered through a combination of both qualitative and quantitative methods. Sample/Data Collection Quantitative research was undertaken and created two sets of data. Data set 1 (3888 registered learners): fundamental demographics and attrition/retention rates (which were collected automatically via the FutureLearn platform). Data set 2 (957 completed the pre-course, 84 completed the post-course and 42 completed both): relating to those who had completed the pre and post MOOC survey for this project (initiated, designed, and managed by the researcher for the purpose of this research). Qualitative data were collected via two methods: MOOC discussion board (112 participants) and MOOC participant interviews (14 participants). Results Findings from quantitative data set 1 (MOOC demographics) – these data provided a contextual background to the online learning and demonstrated participation and engagement. 3888 learners originally registered at the beginning of the course with only 8% of this number making the conscious choice to no longer be part of the course at some point during the learning. Of those that remained registered 49% were described as active learners, 18% as social learners and the rest falling out with either of these descriptors. Additionally, findings relating to the pre, and post course survey data also demonstrated that the MOOC learning was met positively overall by the learners with the majority of those indicating that it was interactive, well balanced, and useful to their work and lives going forward. Data set 2 (pre and post course surveys relating to compassion and self-compassion) - these data demonstrated little significant change amongst all tested categories. In keeping with the theoretical underpinning of realistic evaluation and its need to be more considered than simply if something works or not, this research considers what changes have been made within what conditions. These data could imply that, although not statistically significant, there is a trend in changes to survey responses that could suggest that learning could be providing an opportunity for deeper thought. Findings from the qualitative analysis exposed 4 overarching themes: Changes to attitudes and behaviours around compassion; Compassionate care changes reflected in practice; The emotional burden of compassion; and Experiences of the MOOC. Conclusion In conclusion, it is important to acknowledge that realistic evaluation does not aim to merely prove or disprove theories but rather unearth observable patterns which can explain what works and why. Each area of analysis; learning analytics, quantitative and qualitative, demonstrated a degree of change from pre to post course. Overall, this MOOC was acknowledged as being a potentially valuable educational tool due to its flexibility, content and most importantly the availability of discussion forums in which learners could share differing narratives and stories in order to enhance their learning. In summarising, the author identified two valuable conclusions: Self-compassion – results demonstrated that through the MOOC learning, participants were able to link the way in which they care for themselves with the way they can care for others including colleagues and patients. Discussion boards - provided a valuable opportunity for healthcare professionals, lay people and the general public to share thoughts, experiences, opinions and anecdotes. This rich learning environment was particularly poignant in a world in which healthcare education is restricted to learning “within healthcare”. Recommendations Further in depth research needs to be undertaken in order to better understand the connection between online learning in complex subject areas such as compassion and possible improvement in healthcare practices. An observational study undertaken over a prolonged period of time would provide research to strengthen the argument for the use of MOOCs in healthcare education. Additionally, data which measures the patient perspective and the impact on the quality of compassionate care that they receive would provide further value in the evaluation of MOOCs in this area. Implications for Practice The overall findings from the research project will be used to inform educators, healthcare leaders and practitioners of the usefulness of a MOOC to learn about compassionate care. However, the researcher identified a significant area for development in practice which would allow the vital messages of self-compassion and compassion to be shared. This could be done through the utilisation of the MOOC learning and the creation of a new champion role within healthcare, the “Compassion Champion”

Knowledge Decay in a Normalised Knowledge Base

Knowledge `decay' is a measure of the degradation of knowledge integrity. In a unified knowledge representation, data, information and knowledge are all represented in a single formalism as "items". A unified knowledge representation is extended here to include two measures of knowledge integrity. A rule of inference is defined on the unified knowledge representation that preserves the validity of these two measures. This rule of inference is used to define a normalised knowledge base. The use of a unified knowledge representation and the application of knowledge base normalisation simplifies the estimation of knowledge decay

Knowledge Decay in a Normalised Knowledge Base

Abstract. Knowledge ‘decay ’ is a measure of the degradation of knowledge integrity. In a unified knowledge representation, data, information and knowledge are all represented in a single formalism as “items”. A unified knowledge representation is extended here to include two measures of knowledge integrity. A rule of inference is defined on the unified knowledge representation that preserves the validity of these two measures. This rule of inference is used to define a normalised knowledge base. The use of a unified knowledge representation and the application of knowledge base normalisation simplifies the estimation of knowledge decay. 1