EXPLANATORY ANALYSIS IN BUSINESS INTELLIGENCE SYSTEMS

In this paper we describe a method for the discovery of exceptional values in business intelligence (BI) systems, in particular OLAP information systems. We also show how exceptional values can be explained by underlying causes. OLAP applications offer a support tool for business analysts and accountants in analyzing financial data because of the availability of different views and managerial reporting facilities. The purpose of the methods and algorithms presented here, is to extend OLAP based systems with more powerful analysis and reporting functions. We describe how exceptional values at any level in the data, can be automatically detected by statistical models. Secondly a generic model for diagnosis of atypical values is realized in the OLAP context. By applying it, a full explanation tree of causes at successive levels can be generated. If the tree is too large, the analyst can use appropriate filtering measures to prune the tree to a manageable size. This methodology has a wide range of applications such as interfirm comparison, analysis of sales data and the analysis of any other data that possess a multi-dimensional hierarchical structure. The method is demonstrated in a case study on financial data

Structured computer-based training in the interpretation of neuroradiological images

Computer-based systems may be able to address a recognised need throughout the medical profession for a more structured approach to training. We describe a combined training system for neuroradiology, the MR Tutor that differs from previous approaches to computer-assisted training in radiology in that it provides case-based tuition whereby the system and user communicate in terms of a well-founded Image Description Language. The system implements a novel method of visualisation and interaction with a library of fully described cases utilising statistical models of similarity, typicality and disease categorisation of cases. We describe the rationale, knowledge representation and design of the system, and provide a formative evaluation of its usability and effectiveness

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin