Forensic flavour

Databases often receive an uninspired and uninterested response. The curriculum content of a database module generally involves the design of entity-relationship models, SQL programming, application development and advanced database applications such as data warehousing and data mining. These are often taught within the tired and relatively worn case studies of purchase order systems, retail or health care systems. However the current trend for crime scene investigation drama and the frequent stories in the news of personal tragedies involving incorrect data, missing data or data mix-up capture the attention of many. The truth is that crimes require data investigation and expert database witnesses to provide evidence and this requires database knowledge and skill. This project involved the introduction of a ‘forensic flavour’ to the teaching of databases as part of an undergraduate Computing Degree to students. The ‘forensic flavour’ involved introducing investigative and enquiry based learning techniques as well as selecting case studies based around real-life crimes and crime data. The learning objectives remained unchanged for the modules as did the curriculum content. The initial findings are that the students engaged on average 40% better and enjoyed the experience more

Application of Business Intelligence Techniques using SAS on Open Data: Analysing Health Inequality in English Regions

Health inequality is a widely reported problem. There is an existing body of work that links health inequality and geographical location. This means that one might be more disadvantage health-wise if one was born in one region compared to another. Existing health inequality related work in various developed and developing countries rely on population census or survey data. Effective conclusions drawn require large scale data with multiple parameters. There is a new phenomenon in countries (e.g. the UK), where governments are opening up citizen-centric data for transparency purposes and to facilitate data-informed policy making. There are many health organisations, including NHS and sister organisations (e.g. HSCIC), which participate in this drive to open up data. These health-related datasets can be exploited health inequality analytics. This work presents a novel approach of analysing health inequality in English regions solely based on open data. A methodological and systematic approach grounded in CRISP-DM methodology is adhered to for the analyses of the datasets. The analysis utilises a well-cited work on health inequality in children and the corresponding parameters such as Preterm birth, Low birth weight, Infant mortality, Excessive weight in children, Breastfeeding prevalence and Children in poverty. An authority in health datasets, called Public Health Outcomes(PHO) Framework, is chosen as a data source that contains data with these parameters. The analysis is carried out using various SAS data mining techniques such as clustering, and time series analysis. The results show the presence of health inequality in English regions. The work clearly identifies the English regions on the right and wrong side of the divide. The policy and future work recommendations based on these findings are articulated in this research. This work presented in this paper is novel as it applies SAS based BI techniques to analyse health inequality for children in the UK solely based on open data

The effect of funding changes on public sector nonprofit organizations: the case of Bushcare NSW

Research into nonprofit organizations abounds, but not much is known about public sector nonprofit organizations. Recent funding incentives in Australia have led to significant changes in the market environment for such organizations. This study describes these market changes and explores the reactions of one environmental public sector nonprofit organization, Bushcare NSW, to these changes. This paper contends that, within this institutional environment, nonprofit organizations more successful in attracting large amounts of external funding have better administrative structures in place, whereas those less successful find themselves confronted with burdensome administrative duties. Neo-institutional theory provides a theoretical basis for this empirical investigation. Funding changes have had a major impact on Bushcare organizations, those more successful in attracting grants reporting significantly fewer recent administrative changes

Biclustering: overcoming data dimensionality problems in market segmentation

Data-driven market segmentation is a popular and widely used segmentation method in tourism. It aims to identify market segments among tourists who are similar to each other, thus allowing a targeted marketing mix to be developed. Typically data used to segment tourists are characterized by small numbers of respondents and large numbers of survey questions. Small samples and numerous questions cause serious methodological problems that have typically been addressed by using factorcluster analysis to reduce the dimensionality of data. Recently, factor-cluster analysis has been shown as an unacceptable solution to the problem of high data dimensionality in segmentation. In this article, the authors introduce biclustering, a novel approach to address the problem of high dimensionality in tourism segmentation studies. We discuss the circumstances in which biclustering should be used rather than parametric or nonparametric grouping techniques. An illustrative example of how biclustering is computed is also provided