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

General Model for Automated Diagnosis of Business Performance

In this paper, we describe an extension of the methodology for explanation generation in financial knowledge-based systems, offering the possibility to automatically generate explanations and diagnostics to support business decision tasks. The central goal is the identification of specific knowledge structures and reasoning methods required to construct computerized explanations from financial data and business models. A multi-step look-ahead algorithm is proposed that deals with so-called calling-out effects, which are a common phenomenon in financial data sets. The extended methodology was tested on a case-study conducted for Statistics Netherlands involving the comparison of financial figures of firms in the Dutch retail branch. The analyses are performed with a diagnostic software application which implements our theory of explanation. Comparison of results of the classic explanation methodology with the results of the extended methodology shows significant improvements in the analyses when cancelling-out effects are present in the data

Diagnosis in the Olap Context

The purpose of OLAP (On-Line Analytical Processing) systems is to provide a framework for the analysis of multidimensional data. Many tasks related to analysing multidimensional data and making business decisions are still carried out manually by analysts (e.g. financial analysts, accountants, or business managers). An important and common task in multidimensional analysis is business diagnosis. Diagnosis is defined as finding the “best” explanation of observed symptoms. Today’s OLAP systems offer little support for automated business diagnosis. This functionality can be provided by extending the conventional OLAP system with an explanation formalism, which mimics the work of business decision makers in diagnostic processes. The central goal of this paper is the identification of specific knowledge structures and reasoning methods required to construct computerized explanations from multidimensional data and business models. We propose an algorithm that generates explanations for symptoms in multidimensional business data. The algorithm was tested on a fictitious case study involving the comparison of financial results of a firm’s business units

Explanation of Exceptional Values in Multi-dimensional Business Databases

“How can the functionality of multi-dimensional business databases be extended with diagnostic capabilities to support managerial decision-making?” This question states the main research problem addressed in this thesis. Before giving an answer, the question first requires clarification and delineation. In this chapter, the research question is placed briefly into context, both regarding academic and business relevance. This leads to the formulation of three specific research questions. Subsequently, a section is dedicated to each specific research question. An outline of this thesis concludes the chapter

Association of respiratory symptoms and lung function with occupation in the multinational Burden of Obstructive Lung Disease (BOLD) study

Background Chronic obstructive pulmonary disease has been associated with exposures in the workplace. We aimed to assess the association of respiratory symptoms and lung function with occupation in the Burden of Obstructive Lung Disease study. Methods We analysed cross-sectional data from 28 823 adults (≥40 years) in 34 countries. We considered 11 occupations and grouped them by likelihood of exposure to organic dusts, inorganic dusts and fumes. The association of chronic cough, chronic phlegm, wheeze, dyspnoea, forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1)/FVC with occupation was assessed, per study site, using multivariable regression. These estimates were then meta-analysed. Sensitivity analyses explored differences between sexes and gross national income. Results Overall, working in settings with potentially high exposure to dusts or fumes was associated with respiratory symptoms but not lung function differences. The most common occupation was farming. Compared to people not working in any of the 11 considered occupations, those who were farmers for ≥20 years were more likely to have chronic cough (OR 1.52, 95% CI 1.19–1.94), wheeze (OR 1.37, 95% CI 1.16–1.63) and dyspnoea (OR 1.83, 95% CI 1.53–2.20), but not lower FVC (β=0.02 L, 95% CI −0.02–0.06 L) or lower FEV1/FVC (β=0.04%, 95% CI −0.49–0.58%). Some findings differed by sex and gross national income. Conclusion At a population level, the occupational exposures considered in this study do not appear to be major determinants of differences in lung function, although they are associated with more respiratory symptoms. Because not all work settings were included in this study, respiratory surveillance should still be encouraged among high-risk dusty and fume job workers, especially in low- and middle-income countries.publishedVersio