Enhancing DWH models with the utilisation of multiple hierarchical schemata

Data Warehouse (DWH) Models are based on static dimensions having single hierarchies. With the evolution of World Wide Web, including external knowledge can enrich those models and provide better results of data analysis. Therefore, it would be useful to have intelligent transformation utilities that can mine various data structures and extract useful knowledge participating in the construction of flexible DWH models. This paper proposes a new approach towards intelligent transformation utilities that will allow the utilisation of multiple hierarchical schemata when defining the dimensions of data-warehouses. By allowing a particular dimension to have multiple but semantically close definitions, we allow same users to query same data with the aid of different semantics. To put it differently users are allowed to change or refine the axis of analysis with respect to a particular query, in their effort to achieve a more meaningful answer. We make use of Intuitionistic Fuzzy Logic to soften the rules of calculating the similarity between different hierarchies which in turn is used to decide if the hierarchies can be included in the definition of data warehouse dimension. Data transformations are used to transform the data from one hierarchy to another. With the aid of external data, some sort of estimation is used to estimate values of new hierarchy levels and then based on the user´s request the desired hierarchy is used to view an OLAP cube.