Pocket data mining - big data on small devices

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

Institutional framing of CSR and social entrepreneurship interactions in South Africa

Objectives. This paper considers the intersection of Corporate Social Responsibility (CSR) and social entrepreneurship in South Africa through the lens of institutional theories and draws upon a number of illustrative case study examples. In particular it: (1) charts the historically evolving relationship between CSR and social entrepreneurship in South Africa, and how this relationship has been informed by institutional changes since the end of apartheid, particularly over the last few years; (2) identifies different interactional relationship forms between social enterprises and corporates engaging in CSR, with an emphasis on new innovative multi-stakeholder partnerships; and (3) considers internal engagements with social responsibility by SME social enterprises in South Africa. Prior Work. Reflecting South Africa’s history of division, the controversial role of business during apartheid, and the ongoing legacies of that period, the South African government has been particularly pro-active in encouraging companies to contribute to development and societal transformation through CSR and Black Economic Empowerment (BEE). Accordingly a substantial body of work now exists examining and critically reflecting upon CSR and BEE across a range of sectors. In response to perceived problems with BEE, efforts have recently been made to foster broader-based economic empowerment. However the implications of these transitions for the relationship between CSR and social entrepreneurship in South Africa have received scant academic attention. Approach. Analysis is undertaken of legislative and policy changes in South Africa with a bearing on CSR and social entrepreneurship. Data collected during fieldwork in South Africa working with 6 social enterprise case studies is utilised including qualitative data from key informant interviews, focus groups with stakeholders and observational research. Results. The paper considers the historically evolving relationship between CSR and social entrepreneurship in South Africa informed by institutional change. Five different relationship forms are identified and illustrated with reference to case examples. Finally internal engagement with social responsibility concerns by small and medium social enterprises are critically discussed. Implications. This paper sheds light on some of the innovative partnerships emerging between corporates and social enterprises in South Africa. It reflects on some of the strengths and weaknesses of South Africa’s policy and legislative approaches. Value. The paper provides insights useful for academic and practitioner audiences. It also has policy relevance, in particularly for other African countries potentially looking to follow South Africa’s example, in the development of legislative and policy frameworks to promote corporate responsibility, empowerment and transformation.

The perceived impact of the National Health Service on personalised nutrition service delivery among the UK public

Personalised nutrition (PN) has the potential to reduce disease risk and optimise health and performance. Although previous research has shown good acceptance of the concept of PN in the UK, preferences regarding the delivery of a PN service (e.g. online v. face-to-face) are not fully understood. It is anticipated that the presence of a free at point of delivery healthcare system, the National Health Service (NHS), in the UK may have an impact on end-user preferences for deliverances. To determine this, supplementary analysis of qualitative data obtained from focus group discussions on PN service delivery, collected as part of the Food4Me project in the UK and Ireland, was undertaken. Irish data provided comparative analysis of a healthcare system that is not provided free of charge at the point of delivery to the entire population. Analyses were conducted using the 'framework approach' described by Rabiee (Focus-group interview and data analysis. Proc Nutr Soc 63, 655-660). There was a preference for services to be led by the government and delivered face-to-face, which was perceived to increase trust and transparency, and add value. Both countries associated paying for nutritional advice with increased commitment and motivation to follow guidelines. Contrary to Ireland, however, and despite the perceived benefit of paying, UK discussants still expected PN services to be delivered free of charge by the NHS. Consideration of this unique challenge of free healthcare that is embedded in the NHS culture will be crucial when introducing PN to the UK.

Missing steps in a staircase: a qualitative study of the perspectives of key stakeholders on the use of adaptive designs in confirmatory trials

Background Despite the promising benefits of adaptive designs (ADs), their routine use, especially in confirmatory trials, is lagging behind the prominence given to them in the statistical literature. Much of the previous research to understand barriers and potential facilitators to the use of ADs has been driven from a pharmaceutical drug development perspective, with little focus on trials in the public sector. In this paper, we explore key stakeholders’ experiences, perceptions and views on barriers and facilitators to the use of ADs in publicly funded confirmatory trials. Methods Semi-structured, in-depth interviews of key stakeholders in clinical trials research (CTU directors, funding board and panel members, statisticians, regulators, chief investigators, data monitoring committee members and health economists) were conducted through telephone or face-to-face sessions, predominantly in the UK. We purposively selected participants sequentially to optimise maximum variation in views and experiences. We employed the framework approach to analyse the qualitative data. Results We interviewed 27 participants. We found some of the perceived barriers to be: lack of knowledge and experience coupled with paucity of case studies, lack of applied training, degree of reluctance to use ADs, lack of bridge funding and time to support design work, lack of statistical expertise, some anxiety about the impact of early trial stopping on researchers’ employment contracts, lack of understanding of acceptable scope of ADs and when ADs are appropriate, and statistical and practical complexities. Reluctance to use ADs seemed to be influenced by: therapeutic area, unfamiliarity, concerns about their robustness in decision-making and acceptability of findings to change practice, perceived complexities and proposed type of AD, among others. Conclusions There are still considerable multifaceted, individual and organisational obstacles to be addressed to improve uptake, and successful implementation of ADs when appropriate. Nevertheless, inferred positive change in attitudes and receptiveness towards the appropriate use of ADs by public funders are supportive and are a stepping stone for the future utilisation of ADs by researchers.

Research agenda for the built environment in Latin America

This paper disseminates the outcomes of a series of interdisciplinary and multi-sector research seminars that focused on current development problems in a region of fast urban growth. Qualitative data was collected during round table discussions and workshops involving practitioners and government officials from some of the largest economies in Latin America. The authors then grouped these discussions into coherent themes and framed them into current scholarly debates. After assessing the suitability of theory to respond to practice, the paper concludes with four key areas for further research, with the final aim to encourage more scholarly analysis that can better inform development policy in emerging economies.

Promoting behaviour change through personalized energy feedback in offices

A body of research suggests that the provision of energy feedback information to building users can elicit significant energy reductions through behaviour change. However, most studies have focused on energy use in homes and the assessment of interventions and technologies, to the neglect of the non-domestic context and broader issues arising from the introduction of feedback technologies. To address this gap, a non-domestic case study explores the delivery of personalized energy feedback to office workers through a novel system utilizing wireless technologies. The research demonstrates advantages of monitoring occupancy and quantifying energy use from specific behaviours as a basis for effective energy feedback; this is particularly important where there are highly disaggregated forms of energy use and a range of locations for that activity to take place. Quantitative and qualitative data show that personalized feedback can help individuals identify energy reduction opportunities. However, the analysis also highlights important contextual barriers and issues that need to be addressed when utilizing feedback technologies in the workplace. If neglected, these issues may limit the effective take-up of feedback interventions.

Exploring the role the brand plays in the choice of charity by UK volunteers

Charities need to understand why volunteers choose one brand rather than another in order to attract more volunteers to their organisation. There has been considerable academic interest in understanding why people volunteer generally. However, this research explores the more specific question of why a volunteer chooses one charity brand rather than another. It builds on previous conceptualisations of volunteering as a consumption decision. Seen through the lens of the individual volunteer, it considers the under-researched area of the decision-making process. The research adopts an interpretivist epistemology and subjectivist ontology. Qualitative data was collected through depth interviews and analysed using both Means-End Chain (MEC) and Framework Analysis methodology. The primary contribution of the research is to theory: understanding the role of brand in the volunteer decision-making process. It identifies two roles for brand. The first is as a specific reason for choice, an ‘attribute’ of the decision. Through MEC, volunteering for a well-known brand connects directly through to a sense of self, both self-respect but also social recognition by others. All four components of the symbolic consumption construct are found in the data: volunteers choose a well-known brand to say something about themselves. The brand brings credibility and reassurance, it reduces the risk and enables the volunteer to meet their need to make a difference and achieve a sense of accomplishment. The second closely related role for brand is within the process of making the volunteering decision. Volunteers built up knowledge about the charity brands from a variety of brand touchpoints, over time. At the point of decision-making that brand knowledge and engagement becomes relevant, enabling some to make an automatic choice despite the significant level of commitment being made. The research identifies four types of decision-making behaviour. The research also makes secondary contributions to MEC methodology and to the non-profit context. It concludes within practical implications for management practice and a rich agenda for future research.

Contraceptive methods with male participation: a perspective of Brazilian couples

Objective: To assess the perspectives of couples who requested vasectomy in a public health service on the use of male participation contraceptive methods available in Brazil: male condoms, natural family planning/calendar, coitus interruptus and vasectomy. Methods: A qualitative study with semi-structured interviews was held with 20 couples who had requested vasectomy at the Human Reproduction Unit of the Universidade Estadual de Campinas, Brazil. Data analysis was carried out through thematic content analysis. Findings: The couples did not, in general, know any effective contraceptive options for use by men and/or participating in their use, except for vasectomy. The few methods with male participation that they knew of were perceived to interfere in spontaneity and in pleasure of intercourse. Men accepted that condom use in extra-conjugal relations offered them protection from sexually transmitted diseases; that their wives might also participate in extra-marital relationships was not considered. Discussion: The few contraceptive options with male participation lead to difficulty in sharing responsibilities between men and women. On the basis of perceived gender roles, women took the responsibility for contraception until the moment when the situation became untenable, and they faced the unavoidable necessity of sterilization. Conclusion: Specific actions are necessary for men to achieve integral participation in relation to reproductive sexual health. These include education and discussions on gender roles, leading to greater awareness in men of the realities of sexual and reproductive health.

PCA Tomography: how to extract information from data cubes

Astronomy has evolved almost exclusively by the use of spectroscopic and imaging techniques, operated separately. With the development of modern technologies, it is possible to obtain data cubes in which one combines both techniques simultaneously, producing images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We present a method of analysis of data cube (data from single field observations, containing two spatial and one spectral dimension) that uses Principal Component Analysis (PCA) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates ordered by principal components of decreasing variance. The new coordinates are referred to as eigenvectors, and the projections of the data on to these coordinates produce images we will call tomograms. The association of the tomograms (images) to eigenvectors (spectra) is important for the interpretation of both. The eigenvectors are mutually orthogonal, and this information is fundamental for their handling and interpretation. When the data cube shows objects that present uncorrelated physical phenomena, the eigenvector`s orthogonality may be instrumental in separating and identifying them. By handling eigenvectors and tomograms, one can enhance features, extract noise, compress data, extract spectra, etc. We applied the method, for illustration purpose only, to the central region of the low ionization nuclear emission region (LINER) galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore, we show that it is displaced from the centre of its stellar bulge.

Investigation of a new GRASP-based clustering algorithm applied to biological data

A large amount of biological data has been produced in the last years. Important knowledge can be extracted from these data by the use of data analysis techniques. Clustering plays an important role in data analysis, by organizing similar objects from a dataset into meaningful groups. Several clustering algorithms have been proposed in the literature. However, each algorithm has its bias, being more adequate for particular datasets. This paper presents a mathematical formulation to support the creation of consistent clusters for biological data. Moreover. it shows a clustering algorithm to solve this formulation that uses GRASP (Greedy Randomized Adaptive Search Procedure). We compared the proposed algorithm with three known other algorithms. The proposed algorithm presented the best clustering results confirmed statistically. (C) 2009 Elsevier Ltd. All rights reserved.

The log-exponentiated Weibull regression model for interval-censored data

In interval-censored survival data, the event of interest is not observed exactly but is only known to occur within some time interval. Such data appear very frequently. In this paper, we are concerned only with parametric forms, and so a location-scale regression model based on the exponentiated Weibull distribution is proposed for modeling interval-censored data. We show that the proposed log-exponentiated Weibull regression model for interval-censored data represents a parametric family of models that include other regression models that are broadly used in lifetime data analysis. Assuming the use of interval-censored data, we employ a frequentist analysis, a jackknife estimator, a parametric bootstrap and a Bayesian analysis for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Furthermore, for different parameter settings, sample sizes and censoring percentages, various simulations are performed; in addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to a modified deviance residual in log-exponentiated Weibull regression models for interval-censored data. (C) 2009 Elsevier B.V. All rights reserved.

Statistical analysis of proficiency testing results under elliptical distributions

The use of inter-laboratory test comparisons to determine the performance of individual laboratories for specific tests (or for calibration) [ISO/IEC Guide 43-1, 1997. Proficiency testing by interlaboratory comparisons - Part 1: Development and operation of proficiency testing schemes] is called Proficiency Testing (PT). In this paper we propose the use of the generalized likelihood ratio test to compare the performance of the group of laboratories for specific tests relative to the assigned value and illustrate the procedure considering an actual data from the PT program in the area of volume. The proposed test extends the test criteria in use allowing to test for the consistency of the group of laboratories. Moreover, the class of elliptical distributions are considered for the obtained measurements. (C) 2008 Elsevier B.V. All rights reserved.

Log-Burr XII regression models with censored data

In survival analysis applications, the failure rate function may frequently present a unimodal shape. In such case, the log-normal or log-logistic distributions are used. In this paper, we shall be concerned only with parametric forms, so a location-scale regression model based on the Burr XII distribution is proposed for modeling data with a unimodal failure rate function as an alternative to the log-logistic regression model. Assuming censored data, we consider a classic analysis, a Bayesian analysis and a jackknife estimator for the parameters of the proposed model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the log-logistic and log-Burr XII regression models. Besides, we use sensitivity analysis to detect influential or outlying observations, and residual analysis is used to check the assumptions in the model. Finally, we analyze a real data set under log-Buff XII regression models. (C) 2008 Published by Elsevier B.V.

Gamma transition intensity determination using multidetector coincidence data

This work describes two similar methods for calculating gamma transition intensities from multidetector coincidence measurements. In the first one, applicable to experiments where the angular correlation function is explicitly fitted, the normalization parameter from this fit is used to determine the gamma transition intensities. In the second, that can be used both in angular correlation or DCO measurements, the spectra obtained for all the detector pairs are summed up, in order to get the best detection statistics possible, and the analysis of the resulting bidimensional spectrum is used to calculate the transition intensities; in this method, the summation of data corresponding to different angles minimizes the influence of the angular correlation coefficient. Both methods are then tested in the calculation of intensities for well-known transitions from a (152)Eu standard source, as well as in the calculation of intensities obtained in beta-decay experiments with (193)Os and (155)Sm sources, yielding excellent results in all these cases. (C) 2009 Elsevier B.V. All rights reserved.

Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.