Explanatory analytics in OLAP

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In this paper the authors describe a method to integrate explanatory business analytics in OLAP information systems. This method supports the discovery of exceptional values in OLAP data and the explanation of such values by giving their 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 applications with more powerful analysis and reporting functions. The authors 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.
Original languageEnglish
Pages (from-to)67-82
JournalInternational Journal of Business Intelligence Research
Volume4
Issue number3
Publication statusPublished - 2013

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abstract = "In this paper the authors describe a method to integrate explanatory business analytics in OLAP information systems. This method supports the discovery of exceptional values in OLAP data and the explanation of such values by giving their 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 applications with more powerful analysis and reporting functions. The authors 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 hierarchicalstructure. The method is demonstrated in a case study on financial data.",
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Explanatory analytics in OLAP. / Caron, E.A.M.; Daniëls, H.A.M.

In: International Journal of Business Intelligence Research, Vol. 4, No. 3, 2013, p. 67-82.

Research output: Contribution to journalArticleScientificpeer-review

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