Extending the OLAP framework for automated explanatory tasks
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 â€œbestâ€ 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
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