The LH5 model for data mining

In the age of E-Business many companies faced with massive data sets that must be analysed for gaining a competitive edge. these data sets are in many instances incomplete and quite often not of very high quality. Although statistical analysis can be used to pre-process these data sets, this technique has its own limitations. In this paper we are presenting a system - and its underlying model - that can be used to test the integrity of existing data and pre-process the data into clearer data sets to be mined. LH5 is a rule-based system, capable of self-learning and is illustrated using a medical data set.

Forecasting overseas visitors into the United Kingdom using continuous time and autoregressive fractional integrated moving average models with discrete data

This paper applies Gaussian estimation methods to continuous time models for modelling overseas visitors into the UK. The use of continuous time modelling is widely used in economics and finance but not in tourism forecasting. Using monthly data for 1986–2010, various continuous time models are estimated and compared to autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models. Dynamic forecasts are obtained over different periods. The empirical results show that the ARIMA model performs very well, but that the constant elasticity of variance (CEV) continuous time model has the lowest root mean squared error (RMSE) over a short period.

Reflections of professional practice: using electronic discourse analysis networks (EDANs) to examine embedded business emails

Emails have become a central genre in business communication, reflecting both how people communicate and how they go about their professional practices. This chapter examines embedded business emails as reflections of the professional practices of the regulatory and policy department of a multinational based in London, UK. It argues that the nature of online communication in international organisations, with its high levels of intertextuality and interdiscursivity, requires multidimensional analytical approaches that are capable of capturing its complexity and dynamics. To this end, the chapter introduces electronic discourse analysis networks (EDANs) as one example of such approaches. It begins with a brief review of the literature that has informed the study reported on here before it discusses EDANs as its analytical framework. Using a group of embedded emails and a number of networked data sets, the chapter shows how EDANs can be used to further our understanding of professional online communication.

Discipline-specific writing for business students: Research, practice and pedagogy

In the past few years strong arguments have been made for locating academic writing in higher education within the students’ disciplinary contexts in the belief that a full understanding of the role and dynamic of writing can only be achieved if it is examined as a social practice in its context of production. This chapter reports on a study that examined the conceptualisations of writing for business by a group of undergraduate and postgraduate lecturers and students at the business school of a British university. Based on a critical analysis of the literature reviewed for the study, and the data collected, the chapter contributes to existing writing pedagogy with a number of research-informed transformative pedagogical applications for teaching discipline-specific writing for business. Such applications which combine context-oriented practices (e.g. raising awareness of the role of disciplinary values in shaping writing) and text-oriented activities (e.g. discipline-specific referencing) aim at influencing the pedagogic agenda for teaching writing in higher education. The chapter concludes with questions for reflection and discussion that provide an opportunity for readers to reflect upon their own teaching environment.