Decision support based on genomics:integration of data-and knowledge-driven reasoning

The breadth and depth of available clinico-genomic information, present an enormous opportunity for improving our ability to study disease mechanisms and meet the individualised medicine needs. A difficulty occurs when the results are to be transferred 'from bench to bedside'. Diversity of methods is one of the causes, but the most critical one relates to our inability to share and jointly exploit data and tools. This paper presents a perspective on current state-of-the-art in the analysis of clinico-genomic data and its relevance to medical decision support. It is an attempt to investigate the issues related to data and knowledge integration. Copyright © 2010 Inderscience Enterprises Ltd.

Visual data mining using principled projection algorithms and information visualization techniques

We introduce a flexible visual data mining framework which combines advanced projection algorithms from the machine learning domain and visual techniques developed in the information visualization domain. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection algorithms, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates and billboarding, to provide a visual data mining framework. Results on a real-life chemoinformatics dataset using GTM are promising and have been analytically compared with the results from the traditional projection methods. It is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework. Copyright 2006 ACM.

Using knowledge management to give context to analytics and big data and reduce strategic risk

At the moment, the phrases “big data” and “analytics” are often being used as if they were magic incantations that will solve all an organization’s problems at a stroke. The reality is that data on its own, even with the application of analytics, will not solve any problems. The resources that analytics and big data can consume represent a significant strategic risk if applied ineffectively. Any analysis of data needs to be guided, and to lead to action. So while analytics may lead to knowledge and intelligence (in the military sense of that term), it also needs the input of knowledge and intelligence (in the human sense of that term). And somebody then has to do something new or different as a result of the new insights, or it won’t have been done to any purpose. Using an analytics example concerning accounts payable in the public sector in Canada, this paper reviews thinking from the domains of analytics, risk management and knowledge management, to show some of the pitfalls, and to present a holistic picture of how knowledge management might help tackle the challenges of big data and analytics.

Using a simulation model for knowledge elicitation and knowledge management

The work reported in this paper is part of a project simulating maintenance operations in an automotive engine production facility. The decisions made by the people in charge of these operations form a crucial element of this simulation. Eliciting this knowledge is problematic. One approach is to use the simulation model as part of the knowledge elicitation process. This paper reports on the experience so far with using a simulation model to support knowledge management in this way. Issues are discussed regarding the data available, the use of the model, and the elicitation process itself. © 2004 Elsevier B.V. All rights reserved.

Cues and knowledge structures used by mental-health professionals when making risk assessments

Background: Research into mental-health risks has tended to focus on epidemiological approaches and to consider pieces of evidence in isolation. Less is known about the particular factors and their patterns of occurrence that influence clinicians’ risk judgements in practice. Aims: To identify the cues used by clinicians to make risk judgements and to explore how these combine within clinicians’ psychological representations of suicide, self-harm, self-neglect, and harm to others. Method: Content analysis was applied to semi-structured interviews conducted with 46 practitioners from various mental-health disciplines, using mind maps to represent the hierarchical relationships of data and concepts. Results: Strong consensus between experts meant their knowledge could be integrated into a single hierarchical structure for each risk. This revealed contrasting emphases between data and concepts underpinning risks, including: reflection and forethought for suicide; motivation for self-harm; situation and context for harm to others; and current presentation for self-neglect. Conclusions: Analysis of experts’ risk-assessment knowledge identified influential cues and their relationships to risks. It can inform development of valid risk-screening decision support systems that combine actuarial evidence with clinical expertise.

Internationalization of small and medium sized UK manufacturing enterprises:technology and knowledge transfer perspective

This dissertation examines internationalisation of small and medium sized enterprises. There has been a journey to achieve this. The research has started as an action research as Teaching Company Scheme Associate. This has been done in two research cycles, which investigated factors for successful internationalisation of a small and medium sized UK manufacturing enterprise. This has revealed that successful internationalisation requires good technology and knowledge transfer to the new operations. The action research is followed by a survey that has been conducted within UK manufacturing companies. The data collected was analysed under three models: entry mode selection, role of factory and level of internationalisation. The first two models explain two major aspects of internationalisation decision. The last is showing what makes successful internationalising small and medium sized companies. These models provided several important results. The small and medium sized enterprise internationalisation is harder to achieve because most of these organisations do not have experience in technology and knowledge transfer. The success of internationalisation depends on the success of the transfer. This is achieved through employee ownership of the new knowledge. There are many factors affecting this result such as the network relationships such as trust, control and commitment and cognitive distance between two organisations. The last is a product of the difference between prior knowledge and the required level of knowledge. The entry mode and role of factory are decided through these factors while the level of internationalisation can only be explained by absorptive capacity of the recipient organisation and the technology transfer ability of the host organisation.

Knowledge elicitation and knowledge representation in a large domain with multiple experts

This paper describes the knowledge elicitation and knowledge representation aspects of a system being developed to help with the design and maintenance of relational data bases. The size algorithmic components. In addition, the domain contains multiple experts, but any given expert's knowledge of this large domain is only partial. The paper discusses the methods and techniques used for knowledge elicitation, which was based on a "broad and shallow" approach at first, moving to a "narrow and deep" one later, and describes the models used for knowledge representation, which were based on a layered "generic and variants" approach. © 1995.

Climate change and knowledge politics

This paper addresses the paradox that although the Intergovernmental Panel on Climate Change has reached a broad consensus, various governments pursue different, if not opposing policies. This puzzle not only challenges the traditional belief that scientific knowledge is objective and can be more or less directly translated into political action, but also calls for a better understanding of the relation between science and public policy in modern society. Based on the conceptual framework of knowledge politics the use of expert knowledge in public discourse and in political decisions will be analysed. This will be carried out through a country comparison between the United States and Germany. The main finding is that the press in both countries relies on different sources of scientific expertise when reporting on global warming. In a similar way, governments in both countries use these different sources for legitimising their contrasting policies.

The National Literacy Strategy and knowledge about language in the English school curriculum

In “The English Patient: English Grammar and teaching in the Twentieth Century”, Hudson and Walmsley (2005) contend that the decline of grammar in schools was linked to a similar decline in English universities, where no serious research or teaching on English grammar took place. This article argues that such a decline was due not only to a lack of research, but also because it suited educational policies of the time. It applies Bernstein’s theory of pedagogic discourse (1990 & 1996) to the case study of the debate surrounding the introduction of a national curriculum in English in England in the late 1980s and the National Literacy Strategy in the 1990s, to demonstrate the links between academic theory and educational policy.

Social control and knowledge in democratic societies

In this article we introduce the notions of knowledge policy and the politics of knowledge. These have to be distinguished from the older, well-known terms of research policy, or science and technology policy. While the latter aim to foster the development of innovations in knowledge and its applications, the former is aware of side effects of new knowledge and tries to address them. While research policy takes the aims of innovations as largely unproblematic (insofar as they help improving national competitiveness), knowledge policy tries to govern (regulate, control, restrict, or even forbid) the production of knowledge.

Reliability, normative data, and the effect of age-related macular disease on the Eger Macular Stressometer photostress recovery time

Purpose: To assess repeatability and reproducibility, to determine normative data, and to investigate the effect of age-related macular disease, compared with normals, on photostress recovery time measured using the Eger Macular Stressometer (EMS). Method: The study population comprised 49 healthy eyes of 49 participants. Four EMS measurements were taken in two sessions separated by 1 h by two practitioners, with reversal of order in the second session. EMS readings were also taken from 17 age-related maculopathy (ARM), and 12 age-related macular degeneration (AMD), affected eyes. Results: EMS readings are repeatable to within ± 7 s. There is a statistically significant difference between controls and ARM affected eyes (t = 2.169, p = 0.045), and AMD affected eyes (t = 2.817, p = 0.016). The EMS is highly specific, and demonstrates sensitivity of 29% for ARM, and 50% for AMD. Conclusions: The EMS may be a useful screening test for ARM, however, direct illumination of the macula of greater intensity and longer duration may yield less variable results. © 2004 The College of Optometrists.

A concise guide to market research:the process, data, and methods using IBM SPSS Statistics

This accessible, practice-oriented and compact text provides a hands-on introduction to the principles of market research. Using the market research process as a framework, the authors explain how to collect and describe the necessary data and present the most important and frequently used quantitative analysis techniques, such as ANOVA, regression analysis, factor analysis, and cluster analysis. An explanation is provided of the theoretical choices a market researcher has to make with regard to each technique, as well as how these are translated into actions in IBM SPSS Statistics. This includes a discussion of what the outputs mean and how they should be interpreted from a market research perspective. Each chapter concludes with a case study that illustrates the process based on real-world data. A comprehensive web appendix includes additional analysis techniques, datasets, video files and case studies. Several mobile tags in the text allow readers to quickly browse related web content using a mobile device.

Knowledge:politics and knowledge

Knowledge is of crucial, and growing importance in social, political and economic relations in modern society. The range and variety of available knowledge dramatically enlarges the available options of social action. This five volume collection brings together a broad array of contributions from a variety of disciplines. Featuring essays from philosophers who have investigated the foundations of knowledge, and addressing different forms of knowledge in society such as common sense and practical knowledge, this collection also discusses the role of knowledge in economic process and gives attention to the role of expert knowledge in political decision making. Including a collection of articles from the sociology of knowledge and science, the set also provides a new introduction by the editors, making it a unique and invaluable research resource for both student and scholar.

Non-normally distributed data and non-parametric statistics

Different types of numerical data can be collected in a scientific investigation and the choice of statistical analysis will often depend on the distribution of the data. A basic distinction between variables is whether they are ‘parametric’ or ‘non-parametric’. When a variable is parametric, the data come from a symmetrically shaped distribution known as the ‘Gaussian’ or ‘normal distribution’ whereas non-parametric variables may have a distribution which deviates markedly in shape from normal. This article describes several aspects of the problem of non-normality including: (1) how to test for two common types of deviation from a normal distribution, viz., ‘skew’ and ‘kurtosis’, (2) how to fit the normal distribution to a sample of data, (3) the transformation of non-normally distributed data and scores, and (4) commonly used ‘non-parametric’ statistics which can be used in a variety of circumstances.

Business processes and knowledge management

Knowledge has been a subject of interest and inquiry for thousands of years since at least the time of the ancient Greeks, and no doubt even before that. “What is knowledge” continues to be an important topic of discussion in philosophy. More recently, interest in managing knowledge has grown in step with the perception that increasingly we live in a knowledge-based economy. Drucker (1969) is usually credited as being the first to popularize the knowledge-based economy concept by linking the importance of knowledge with rapid technological change in Drucker (1969). Karl Wiig coined the term knowledge management (hereafter KM) for a NATO seminar in 1986, and its popularity took off following the publication of Nonaka and Takeuchi’s book “The Knowledge Creating Company” (Nonaka & Takeuchi, 1995). Knowledge creation is in fact just one of many activities involved in KM. Others include sharing, retaining, refining, and using knowledge. There are many such lists of activities (Holsapple & Joshi, 2000; Probst, Raub, & Romhardt, 1999; Skyrme, 1999; Wiig, De Hoog, & Van der Spek, 1997). Both academic and practical interest in KM has continued to increase throughout the last decade. In this article, first the different types of knowledge are outlined, then comes a discussion of various routes by which knowledge management can be implemented, advocating a process-based route. An explanation follows of how people, processes, and technology need to fit together for effective KM, and some examples of this route in use are given. Finally, there is a look towards the future.