An overview of interactive visual data mining techniques for knowledge discovery

n the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real-time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image-processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry.

The world at one's fingertips: interactive interpretation of environmental data

This chapter introduces the latest practices and technologies in the interactive interpretation of environmental data. With environmental data becoming ever larger, more diverse and more complex, there is a need for a new generation of tools that provides new capabilities over and above those of the standard workhorses of science. These new tools aid the scientist in discovering interesting new features (and also problems) in large datasets by allowing the data to be explored interactively using simple, intuitive graphical tools. In this way, new discoveries are made that are commonly missed by automated batch data processing. This chapter discusses the characteristics of environmental science data, common current practice in data analysis and the supporting tools and infrastructure. New approaches are introduced and illustrated from the points of view of both the end user and the underlying technology. We conclude by speculating as to future developments in the field and what must be achieved to fulfil this vision.

How Twitter is studied in the medical professions: a classification of Twitter papers indexed in PubMed

Background: Since their inception, Twitter and related microblogging systems have provided a rich source of information for researchers and have attracted interest in their affordances and use. Since 2009 PubMed has included 123 journal articles on medicine and Twitter, but no overview exists as to how the field uses Twitter in research. // Objective: This paper aims to identify published work relating to Twitter indexed by PubMed, and then to classify it. This classification will provide a framework in which future researchers will be able to position their work, and to provide an understanding of the current reach of research using Twitter in medical disciplines. Limiting the study to papers indexed by PubMed ensures the work provides a reproducible benchmark. // Methods: Papers, indexed by PubMed, on Twitter and related topics were identified and reviewed. The papers were then qualitatively classified based on the paper’s title and abstract to determine their focus. The work that was Twitter focused was studied in detail to determine what data, if any, it was based on, and from this a categorization of the data set size used in the studies was developed. Using open coded content analysis additional important categories were also identified, relating to the primary methodology, domain and aspect. // Results: As of 2012, PubMed comprises more than 21 million citations from biomedical literature, and from these a corpus of 134 potentially Twitter related papers were identified, eleven of which were subsequently found not to be relevant. There were no papers prior to 2009 relating to microblogging, a term first used in 2006. Of the remaining 123 papers which mentioned Twitter, thirty were focussed on Twitter (the others referring to it tangentially). The early Twitter focussed papers introduced the topic and highlighted the potential, not carrying out any form of data analysis. The majority of published papers used analytic techniques to sort through thousands, if not millions, of individual tweets, often depending on automated tools to do so. Our analysis demonstrates that researchers are starting to use knowledge discovery methods and data mining techniques to understand vast quantities of tweets: the study of Twitter is becoming quantitative research. // Conclusions: This work is to the best of our knowledge the first overview study of medical related research based on Twitter and related microblogging. We have used five dimensions to categorise published medical related research on Twitter. This classification provides a framework within which researchers studying development and use of Twitter within medical related research, and those undertaking comparative studies of research relating to Twitter in the area of medicine and beyond, can position and ground their work.

Developing a knowledge management system using an ontological approach in global organisation

This paper introduces an ontology-based knowledge model for knowledge management. This model can facilitate knowledge discovery that provides users with insight for decision making. The users requiring the insight normally play different roles with different requirements in an organisation. To meet the requirements, insights are created by purposely aggregated transnational data. This involves a semantic data integration process. In this paper, we present a knowledge management system which is capable of representing knowledge requirements in a domain context and enabling the semantic data integration through ontology modeling. The knowledge domain context of United Bible Societies is used to illustrate the features of the knowledge management capabilities.

Performing knowledge requirements analysis for public organisations in a Virtual Learning Environment: a social network analysis approach

This paper describes an application of Social Network Analysis methods for identification of knowledge demands in public organisations. Affiliation networks established in a postgraduate programme were analysed. The course was executed in a distance education mode and its students worked on public agencies. Relations established among course participants were mediated through a virtual learning environment using Moodle. Data available in Moodle may be extracted using knowledge discovery in databases techniques. Potential degrees of closeness existing among different organisations and among researched subjects were assessed. This suggests how organisations could cooperate for knowledge management and also how to identify their common interests. The study points out that closeness among organisations and research topics may be assessed through affiliation networks. This opens up opportunities for applying knowledge management between organisations and creating communities of practice. Concepts of knowledge management and social network analysis provide the theoretical and methodological basis.

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions: The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.

K-Surfer: a KNIME extension for the management and analysis of Human brain MRI FreeSurfer/FSL data

Human brain imaging techniques, such as Magnetic Resonance Imaging (MRI) or Diffusion Tensor Imaging (DTI), have been established as scientific and diagnostic tools and their adoption is growing in popularity. Statistical methods, machine learning and data mining algorithms have successfully been adopted to extract predictive and descriptive models from neuroimage data. However, the knowledge discovery process typically requires also the adoption of pre-processing, post-processing and visualisation techniques in complex data workflows. Currently, a main problem for the integrated preprocessing and mining of MRI data is the lack of comprehensive platforms able to avoid the manual invocation of preprocessing and mining tools, that yields to an error-prone and inefficient process. In this work we present K-Surfer, a novel plug-in of the Konstanz Information Miner (KNIME) workbench, that automatizes the preprocessing of brain images and leverages the mining capabilities of KNIME in an integrated way. K-Surfer supports the importing, filtering, merging and pre-processing of neuroimage data from FreeSurfer, a tool for human brain MRI feature extraction and interpretation. K-Surfer automatizes the steps for importing FreeSurfer data, reducing time costs, eliminating human errors and enabling the design of complex analytics workflow for neuroimage data by leveraging the rich functionalities available in the KNIME workbench.

Investment in preventing and preparing for biological emergencies and disasters: social and economic costs of disasters versus costs of surveillance and response preparedness

Biological emergencies such as the appearance of an exotic transboundary or emerging disease can become disasters. The question that faces Veterinary Services in developing countries is how to balance resources dedicated to active insurance measures, such as border control, surveillance, working with the governments of developing countries, and investing in improving veterinary knowledge and tools, with passive measures, such as contingency funds and vaccine banks. There is strong evidence that the animal health situation in developed countries has improved and is relatively stable. In addition, through trade with other countries, developing countries are becoming part of the international animal health system, the status of which is improving, though with occasional setbacks. However, despite these improvements, the risk of a possible biological disaster still remains, and has increased in recent times because of the threat of bioterrorism. This paper suggests that a model that combines decision tree analysis with epidemiology is required to identify critical points in food chains that should be strengthened to reduce the risk of emergencies and prevent emergencies from becoming disasters.

New scientific paradigms for probiotics and prebiotics

The inaugural meeting of the International Scientific Association for Probiotics and Prebiotics (ISAPP) was held May 3 to May 5 2002 in London, Ontario, Canada. A group of 63 academic and industrial scientists from around the world convened to discuss current issues in the science of probiotics and prebiotics. ISAPP is a non-profit organization comprised of international scientists whose intent is to strongly support and improve the levels of scientific integrity and due diligence associated with the study, use, and application of probiotics and prebiotics. In addition, ISAPP values its role in facilitating communication with the public and healthcare providers and among scientists in related fields on all topics pertinent to probiotics and prebiotics. It is anticipated that such efforts will lead to development of approaches and products that are optimally designed for the improvement of human and animal health and well being. This article is a summary of the discussions, conclusions, and recommendations made by 8 working groups convened during the first ISAPP workshop focusing on the topics of: definitions, intestinal flora, extra-intestinal sites, immune function, intestinal disease, cancer, genetics and genomics, and second generation prebiotics. Humans have evolved in symbiosis with an estimated 1014 resident microorganisms. However, as medicine has widely defined and explored the perpetrators of disease, including those of microbial origin, it has paid relatively little attention to the microbial cells that constitute the most abundant life forms associated with our body. Microbial metabolism in humans and animals constitutes an intense biochemical activity in the body, with profound repercussions for health and disease. As understanding of the human genome constantly expands, an important opportunity will arise to better determine the relationship between microbial populations within the body and host factors (including gender, genetic background, and nutrition) and the concomitant implications for health and improved quality of life. Combined human and microbial genetic studies will determine how such interactions can affect human health and longevity, which communication systems are used, and how they can be influenced to benefit the host. Probiotics are defined as live microorganisms which, when administered in adequate amounts confer a health benefit on the host.1 The probiotic concept dates back over 100 years, but only in recent times have the scientific knowledge and tools become available to properly evaluate their effects on normal health and well being, and their potential in preventing and treating disease. A similar situation exists for prebiotics, defined by this group as non-digestible substances that provide a beneficial physiological effect on the host by selectively stimulating the favorable growth or activity of a limited number of indigenous bacteria. Prebiotics function complementary to, and possibly synergistically with, probiotics. Numerous studies are providing insights into the growth and metabolic influence of these microbial nutrients on health. Today, the science behind the function of probiotics and prebiotics still requires more stringent deciphering both scientifically and mechanistically. The explosion of publications and interest in probiotics and prebiotics has resulted in a body of collective research that points toward great promise. However, this research is spread among such a diversity of organisms, delivery vehicles (foods, pills, and supplements), and potential health targets such that general conclusions cannot easily be made. Nevertheless, this situation is rapidly changing on a number of important fronts. With progress over the past decade on the genetics of lactic acid bacteria and the recent, 2,3 and pending, 4 release of complete genome sequences for major probiotic species, the field is now armed with detailed information and sophisticated microbiological and bioinformatic tools. Similarly, advances in biotechnology could yield new probiotics and prebiotics designed for enhanced or expanded functionality. The incorporation of genetic tools within a multidisciplinary scientific platform is expected to reveal the contributions of commensals, probiotics, and prebiotics to general health and well being and explicitly identify the mechanisms and corresponding host responses that provide the basis for their positive roles and associated claims. In terms of human suffering, the need for effective new approaches to prevent and treat disease is paramount. The need exists not only to alleviate the significant mortality and morbidity caused by intestinal diseases worldwide (especially diarrheal diseases in children), but also for infections at non-intestinal sites. This is especially worthy of pursuit in developing nations where mortality is too often the outcome of food and water borne infection. Inasmuch as probiotics and prebiotics are able to influence the populations or activities of commensal microflora, there is evidence that they can also play a role in mitigating some diseases. 5,6 Preliminary support that probiotics and prebiotics may be useful as intervention in conditions including inflammatory bowel disease, irritable bowel syndrome, allergy, cancer (especially colorectal cancer of which 75% are associated with diet), vaginal and urinary tract infections in women, kidney stone disease, mineral absorption, and infections caused by Helicobacter pylori is emerging. Some metabolites of microbes in the gut may also impact systemic conditions ranging from coronary heart disease to cognitive function, suggesting the possibility that exogenously applied microbes in the form of probiotics, or alteration of gut microecology with prebiotics, may be useful interventions even in these apparently disparate conditions. Beyond these direct intervention targets, probiotic cultures can also serve in expanded roles as live vehicles to deliver biologic agents (vaccines, enzymes, and proteins) to targeted locations within the body. The economic impact of these disease conditions in terms of diagnosis, treatment, doctor and hospital visits, and time off work exceeds several hundred billion dollars. The quality of life impact is also of major concern. Probiotics and prebiotics offer plausible opportunities to reduce the morbidity associated with these conditions. The following addresses issues that emerged from 8 workshops (Definitions, Intestinal Flora, Extra-Intestinal Sites, Immune Function, Intestinal Disease, Cancer, Genomics, and Second Generation Prebiotics), reflecting the current scientific state of probiotics and prebiotics. This is not a comprehensive review, however the study emphasizes pivotal knowledge gaps, and recommendations are made as to the underlying scientific and multidisciplinary studies that will be required to advance our understanding of the roles and impact of prebiotics, probiotics, and the commensal microflora upon health and disease management.

Neural and wavelet network models for financial distress classification

This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single "mother wavelet" function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997-2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models.

Ratio selection for classification models

This paper is concerned with the selection of inputs for classification models based on ratios of measured quantities. For this purpose, all possible ratios are built from the quantities involved and variable selection techniques are used to choose a convenient subset of ratios. In this context, two selection techniques are proposed: one based on a pre-selection procedure and another based on a genetic algorithm. In an example involving the financial distress prediction of companies, the models obtained from ratios selected by the proposed techniques compare favorably to a model using ratios usually found in the financial distress literature.

Subjectively construed identities and discourse: towards a research agenda for construction management

Identity issues are under-explored in construction management. We provide a brief introduction to the organization studies literature on subjectively construed identities, focusing on discourse, agency, relations of power and identity work. The construction management literature is investigated in order to examine identity concerns as they relate to construction managers centred on (1) professionalism; (2) ethics; (3) relational aspects of self-identity; (4) competence, knowledge and tools; and (5) national culture. Identity, we argue, is a key performance issue, and needs to be accounted for in explanations of the success and failure of projects. Our overriding concern is to raise identity issues in order to demonstrate their importance to researchers in construction management and to spark debate. The purpose of this work is not to provide answers or to propose prescriptive models, but to explore ideas, raise awareness and to generate questions for further programmatic research. To this end, we promote empirical work and theorizing by outlining elements of a research agenda which argues that 'identity' is a potentially generative theme for scholars in construction management.

Adaptive modelling, estimation and fusion from data: a neurofuzzy approach

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input. This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency. Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.