ABSTRACT

This chapter demonstrates the feasibility of the concept for a knowledge-based cause-effect analysis from Benjamins. The concept is manually applied to an example data set to support a fictitious decision scenario by generating a multilayered decision support system (DSS) model. Formulas of DSS models provide structured knowledge about quantified relationships between data. OLAP cubes implicate potential cause-effect relationships within their dimensional structure and even ETL processes from a data warehouse (DW) implicitly contain cause-effect knowledge within their transformations. Knowledge about cause-effect relationships is extracted from the OLTP and DW data model, transferred into a unified structure, and thus, a knowledge base is created. This is done by representing a database table as an ontology class and important table columns as annotation to a class. The knowledge reasoning is manually applied to the created knowledge base for the fictitious scenario, thus showing the procedure of the reasoning phases: initialization, exploration, and evaluation.