Explanatory Business Analytics in OLAP

Explanatory Business Analytics in OLAP

Emiel Caron (Centre for Science and Technology Studies, Leiden University, Leiden, The Netherlands) and Hennie Daniels (Center for Economic Research, Tilburg University, Tilburg, The Netherlands, & Rotterdam School of Management, Erasmus University, Rotterdam, The Netherlands)
Copyright: © 2013 |Pages: 16
DOI: 10.4018/ijbir.2013070105
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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 hierarchical structure. The method is demonstrated in a case study on financial data.
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Olap Database Systems

OLAP databases are capable of capturing the structure of business data in the form of multi-dimensional tables which are known as data cubes that form an essential part of information systems, like DSS, MIS, and ERP systems. Manipulation and presentation of such information through interactive multi-dimensional tables and graphical displays provide important support for the business analyst.

The highly normalized form of the relational data model for OLTP databases is inappropriate in an OLAP database for performance reasons (Kimball, 1996). Therefore, OLAP database implementations typically employ a star model, which stores data de-normalized in a central fact table and associated dimension tables. This type of data model allows for fast query access because the number of table joins is heavily reduced compared to the relational model.

In a star scheme, data is organized into measures and dimensions. Measures are the basic numerical units of interest for analysis and textual dimensions correspond to different perspectives for viewing measures. Dimensions are usually organized as dimension hierarchies, which offer the possibility to inspect measures on different dimension hierarchy levels. Aggregating measures up to a certain dimension level with aggregation functions like SUM, COUNT, and AVERAGE, creates a multi-dimensional view of the data, also known as the data or OLAP cube.

Drill-down equations are formed by the application of a specific aggregation function f on a measure y(C), somewhere in the lattice L (see Appendix A for details). The aggregation we consider here is the common SUM function. The measure y is an additive drill-down measure if for every cell where C is a cube in the lattice L, we have

(1)

The latter equation is a used for expanding a dimension that is of interest. Equations in OLAP are simple SUM, COUNT, MAX, or MIN equations, in general however we can have arbitrary equations of the form:

(2) where and y and are measures on the same cube C.

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