Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

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.

Historic environments and tourism: Improving access for disabled people

Historic environments and buildings are valued and valuable features of the UK tourism sector, as visitor attractions and as holiday accommodation. Keeping historic environments in economic use is crucial to their conservation, but they date from eras when access for disabled people was not a consideration. Part III of the Disability Discrimination Act 1995 (the DDA) took effect on 1 October 2004 and requires service providers to make reasonable building adjustments to remove physical barriers to disabled access. This independent scoping study by the College of Estate Management, sponsored by Marsh Limited and The Mercers' Company, explores progress in making historic environments accessible to disabled people through an examination of UK policy, literature and case studies in South Oxfordshire and London. The report findings are relevant for property and built environment professionals, business managers and all those involved with historic environments that are used for tourism.

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.

Urban economic openness and IPO underpricing

We examine the empirical impact of trade openness on the short-run underpricing of initial public offerings (IPOs) using city-level real estate data. This paper represents a first attempt to employ a macroeconomic approach to explain IPO performance. We investigate an openness effect in which urban economic openness (UEO) has a significant impact on the productivity and on the prices of both direct and indirect real estate due to productivity gains of companies in more open areas. This in turn positively affects the firm’s profitability, enhancing the confidence in the local real estate market and the future company performance and decreasing the uncertainty of the IPO valuation. And as a result, we find that issuers have less incentive to underprice the IPO shares. China provides a suitable experimental ground to study the immense underpricing in developing markets, which cannot solely be accounted for by firm specific effects. First, Chinese real estate companies show strong geographic patterns focusing their businesses locally – usually at a city level. Second, we observe a degree of openness which is significantly heterogeneous across Chinese cities. Controlling for company-specific variables, location and state ownership, we find the evidence that companies whose businesses are in economically more open areas experience less IPO underpricing. Our results show high explanatory power and are robust to diverse specifications.

The career competencies of self-initiated and assigned expatriates: assessing the development of career capital over time

Building on a modern careers approach, we assess the effects of working abroad on individuals’ career capital. Given the dearth of longitudinal studies, we return to a sample of economics graduates in Finland eight years later. We measure changes in three dimensions of career capital; ‘knowing how’, ‘knowing whom’, ‘knowing why’ and find that company assigned expatriates learn more than self-initiated expatriates. All three career capital areas benefit from international experience and all are increasingly valued over time. Based on our findings we conclude that a dynamic notion of career capital acquisition and use is needed. Managerial implications include the need for a wider view of talent management for international businesses.

The Wheal Jane wetlands model for bioremediation of acid mine drainage

Acid mine drainage (AMD) is a widespread environmental problem associated with both working and abandoned mining operations. As part of an overall strategy to determine a long-term treatment option for AMD, a pilot passive treatment plant was constructed in 1994 at Wheal Jane Mine in Cornwall, UK. The plant consists of three separate systems, each containing aerobic reed beds, anaerobic cell and rock filters, and represents the largest European experimental facility of its kind. The systems only differ by the type of pretreatment utilised to increase the pH of the influent minewater (pH <4): lime dosed (LD), anoxic limestone drain (ALD) and lime free (LF), which receives no form of pretreatment. Historical data (1994-1997) indicate median Fe reduction between 55% and 92%, sulphate removal in the range of 3-38% and removal of target metals (cadmium, copper and zinc) below detection limits, depending on pretreatment and flow rates through the system. A new model to simulate the processes and dynamics of the wetlands systems is described, as well as the application of the model to experimental data collected at the pilot plant. The model is process based, and utilises reaction kinetic approaches based on experimental microbial techniques rather than an equilibrium approach to metal precipitation. The model is dynamic and utilises numerical integration routines to solve a set of differential equations that describe the behaviour of 20 variables over the 17 pilot plant cells on a daily basis. The model outputs at each cell boundary are evaluated and compared with the measured data, and the model is demonstrated to provide a good representation of the complex behaviour of the wetland system for a wide range of variables. (C) 2004 Elsevier B.V/ All rights reserved.

Bioremediation of acid mine drainage: an introduction to the Wheal Jane wetlands project

Acid mine drainage (AMD) is a widespread environmental problem associated with both working and abandoned mining operations. As part of an overall strategy to determine a long-term treatment option for AMD, a pilot passive treatment plant was constructed in 1994 at Wheat Jane Mine in Cornwall, UK. The plant consists of three separate systems; each containing aerobic reed beds, anaerobic cell and rock filters, and represents the largest European experimental facility of its kind. The systems only differ by the type of pre-treatment utilised to increase the pH of the influent minewater (pH<4): lime-dosed (LD), anoxic limestone drain (ALD) and lime free (LF), which receives no form of pre-treatment. The Wheal Jane pilot plant offered a unique facility and a major research project was established to evaluate the pilot plant and study in detail the biological mechanisms and the geochemical and physical processes that control passive treatment systems. The project has led to data, knowledge, models and design criteria for the future design, planning and sustainable management of passive treatment systems. A multidisciplinary team of scientists and managers from the U.K. universities, the Environment Agency and the Mining Industry has been put together to obtain the maximum advantage from the excellent facilities facility at Wheal Jane. (C) 2004 Elseaier B.V All rights reserved.

Skill Rating by Bayesian Inference

Systems Engineering often involves computer modelling the behaviour of proposed systems and their components. Where a component is human, fallibility must be modelled by a stochastic agent. The identification of a model of decision-making over quantifiable options is investigated using the game-domain of Chess. Bayesian methods are used to infer the distribution of players’ skill levels from the moves they play rather than from their competitive results. The approach is used on large sets of games by players across a broad FIDE Elo range, and is in principle applicable to any scenario where high-value decisions are being made under pressure.

‘Inherited commitments’: do changes in ownership affect corporate social responsibility (CSR) at African gold mines?

This paper critically examines the issue of ‘inherited corporate social responsibility’ in the gold mining industry, focusing specifically on the case of sub-Saharan Africa, a region plagued with excessive corruption, rampant poverty and weak governance. Whilst there appears to be little incentive to proactively engage with communities and implement cutting-edge environmental policies in the region, mine managers argue otherwise, highlighting a number of reasons for embracing corporate social responsibility (CSR). After briefly reviewing the philosophical underpinnings of CSR, the paper provides an in-depth analysis of these arguments, in the process, underscoring how tenuous the case for CSR in the extractive industries, and gold mining more specifically, is in the context of sub-Saharan Africa. Following a change in ownership, new management faces few pressures to embrace CSR in its entirety and therefore, more often than not, finds itself in a position to implement programs and policies of its choice. More research is needed that further popularizes the issue of ‘inherited CSR’ in the gold mining sector and extractive industries more generally.

PMCRI: a parallel modular classification rule induction framework

In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction of Decision Trees (TDIDT) algorithm is a very widely used technology to predict the classification of newly recorded data. However alternative technologies have been derived that often produce better rules but do not scale well on large datasets. Such an alternative to TDIDT is the PrismTCS algorithm. PrismTCS performs particularly well on noisy data but does not scale well on large datasets. In this paper we introduce Prism and investigate its scaling behaviour. We describe how we improved the scalability of the serial version of Prism and investigate its limitations. We then describe our work to overcome these limitations by developing a framework to parallelise algorithms of the Prism family and similar algorithms. We also present the scale up results of a first prototype implementation.

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.

Typography papers, 9

This latest issue of the series of Typography papers opens with a beautifully illustrated article by the type designer Gerard Unger on ‘Romanesque’ letters. A further installment of Eric Kindel’s pathbreaking history of stencil letters is published in contributions by him, Fred Smeijers, and James Mosley. Maurice Göldner writes the first history of an early twentieth-century German typefounder, Brüder Butter. William Berkson and Peter Enneson recover the notion of ‘readability’ through a history of the collaboration between Matthew Luckiesh and the Linotype Company. Paul Luna discusses the role of pictures in dictionaries. Titus Nemeth describes a new form of Arabic type for metal composition. The whole gathering shows the remarkable variety and vitality of typography now.

Relationships Among Murine CD11chigh Dendritic Cell Subsets as Revealed by Baseline Gene Expression Patterns

The functional relationships and properties of different subtypes of dendritic cells (DC) remain largely undefined. To better characterize these cells, we used global gene analysis to determine gene expression patterns among murine CD11c(high) DC subsets. CD4(+), CD8alpha(+), and CD8alpha(-) CD4(-) (double negative (DN)) DC were purified from spleens of normal C57/BL6 mice and analyzed using Affymetrix microarrays. The CD4(+) and CD8alpha(+) DC subsets showed distinct basal expression profiles differing by >200 individual genes. These included known DC subset markers as well as previously unrecognized, differentially expressed CD Ags such as CD1d, CD5, CD22, and CD72. Flow cytometric analysis confirmed differential expression in nine of nine cases, thereby validating the microarray analysis. Interestingly, the microarray expression profiles for DN cells strongly resembled those of CD4(+) DC, differing from them by <25 genes. This suggests that CD4(+) and DN DC are closely related phylogenetically, whereas CD8alpha(+) DC represent a more distant lineage, supporting the historical distinction between CD8alpha(+) and CD8alpha(-) DC. However, staining patterns revealed that in contrast to CD4(+) DC, the DN subset is heterogeneous and comprises at least two subpopulations. Gene Ontology and literature mining analyses of genes expressed differentially among DC subsets indicated strong associations with immune response parameters as well as cell differentiation and signaling. Such associations offer clues to possible unique functions of the CD11c(high) DC subsets that to date have been difficult to define as rigid distinctions.