Towards a parallel computationally efficient approach to scaling up data stream classification

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks.

An investigation of the role of business analysts in IS development

Business analysis has developed since the early 1990s as an IS discipline that is concerned with understanding business problems, defining requirements and evaluating relevant solutions. However, this discipline has had limited recognition within the academic community and little research has been conducted into the practices and standards employed by business analysts. This paper reports on a study into business analysis that considered the activities conducted and the outcomes experienced on IS projects. Senior business analysts were interviewed in order to gain insights into the business analyst role and the techniques and approaches applied when conducting this work. The Context, Content, Process, Outcomes framework was adopted as a basis for developing the interview questions. The data collected was analysed using the template analysis technique and the template was based upon this framework. Additional themes concerning aspects of business analysis that may contribute to IS success emerged during data analysis. These included the key business analysis activities and the skills business analysts require to perform these activities. The organisational attitude was also identified as a key factor in enabling the use and contribution of business analysis.

Assessing macro uncertainty in real-time when data are subject to revision

Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box-Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested, and used to generate more accurate prediction intervals for 25 macroeconomic variables, in line with the theory. A simulation study based on an empirically-estimated model of data revisions for US output growth is used to investigate small-sample properties.

Follow the leaders? An analysis of convergence and innovation of faculty recruiting practices in US business schools

The debate associated with the qualifications of business school faculty has raged since the 1959 release of the Gordon–Howell and Pierson reports, which encouraged business schools in the USA to enhance their legitimacy by increasing their faculties’ doctoral qualifications and scholarly rigor. Today, the legitimacy of specific faculty qualifications remains one of the most discussed topics in management education, attracting the interest of administrators, faculty, and accreditation agencies. Based on new institutional theory and the institutional logics perspective, this paper examines convergence and innovation in business schools through an analysis of faculty hiring criteria. The qualifications examined are academic degree, scholarly publications, teaching experience, and professional experience. Three groups of schools are examined based on type of university, position within a media ranking system, and accreditation by the Association to Advance Collegiate Schools of Business. Data are gathered using a content analysis of 441 faculty postings from business schools based in the USA over two time periods. Contrary to claims of global convergence, we find most qualifications still vary by group, even in the mature US market. Moreover, innovative hiring is more likely to be found in non-elite schools.

Predicting early data revisions to US GDP and the effects of releases on equity markets

The effects of data uncertainty on real-time decision-making can be reduced by predicting early revisions to US GDP growth. We show that survey forecasts efficiently anticipate the first-revised estimate of GDP, but that forecasting models incorporating monthly economic indicators and daily equity returns provide superior forecasts of the second-revised estimate. We consider the implications of these findings for analyses of the impact of surprises in GDP revision announcements on equity markets, and for analyses of the impact of anticipated future revisions on announcement-day returns.

The determinants of inward foreign direct investment in business services across European regions

The determinants of inward foreign direct investment in business services across European regions, Regional Studies. The role of forward linkages with manufacturing sectors and other service sectors as attractors of business services foreign direct investment (FDI) is studied at the regional level. Using data on 146 NUTS-2 regions, it is found that regions specialized in those (manufacturing) sectors that are high potential users of business services attract more FDI in the business services than other regions. Results are robust to the inclusion of the traditional determinants of foreign investments at the regional level as well as to controls for spatial dependence. The results suggest that regional policies aimed at attracting foreign investors in the business service industry might prove ineffective in the absence of a pre-existing local intermediate demand

Big data: a normal accident waiting to happen?

Widespread commercial use of the internet has significantly increased the volume and scope of data being collected by organisations. ‘Big data’ has emerged as a term to encapsulate both the technical and commercial aspects of this growing data collection activity. To date, much of the discussion of big data has centred upon its transformational potential for innovation and efficiency, yet there has been less reflection on its wider implications beyond commercial value creation. This paper builds upon normal accident theory (NAT) to analyse the broader ethical implications of big data. It argues that the strategies behind big data require organisational systems that leave them vulnerable to normal accidents, that is to say some form of accident or disaster that is both unanticipated and inevitable. Whilst NAT has previously focused on the consequences of physical accidents, this paper suggests a new form of system accident that we label data accidents. These have distinct, less tangible and more complex characteristics and raise significant questions over the role of individual privacy in a ‘data society’. The paper concludes by considering the ways in which the risks of such data accidents might be managed or mitigated.

Business life cycle and capital structure: evidence from Chinese manufacturing firms

This paper uses a panel data-fixed effect approach and data collected from Chinese public manufacturing firms between 1999 and 2011 to investigate the impacts of business life cycle stages on capital structure. We find that cash flow patterns capture more information on business life cycle stages than firm age and have a stronger impact on capital structure decision-making. We also find that the adjustment speed of capital structure varies significantly across life cycle stages and that non-sequential transitions over life cycle stages play an important role in the determination of capital structure. Our study indicates that it is important for policy-makers to ensure that products and financial markets are well-balanced.