Data Warehouses are increasingly used by commercial organizations to extract, from a huge amount of transactional data, concise information useful for supporting decision processes. However, the task of designing a data warehouse and evaluating its effectiveness is not trivial, especially in the case of large databases and in presence of redundant information. The meaning and the quality of selected attributes heavily influence the data warehouse’s effectiveness and the quality of derived decisions. Our research is focused on interactive methodologies and techniques targeted at supporting the data warehouse design and evaluation by taking into account the quality of initial data. In this chapter we propose an approach for supporting the data warehouses development and refinement, providing practical examples and demonstrating the effectiveness of our solution. Our approach is mainly based on two phases: the first one is targeted at interactively guiding the attributes selection by providing quantitative information measuring different statistical and syntactical aspects of data, while the second phase, based on a set of 3D visualizations, gives the opportunity of run-time refining taken design choices according to data examination and analysis. For experimenting proposed solutions on real data, we have developed a tool, called ELDA (EvaLuation DAta warehouse quality), that has been used for supporting the data warehouse design and evaluation.

Interactive Quality-Oriented Data Warehouse Development

PIGHIN, Maurizio;
2009

Abstract

Data Warehouses are increasingly used by commercial organizations to extract, from a huge amount of transactional data, concise information useful for supporting decision processes. However, the task of designing a data warehouse and evaluating its effectiveness is not trivial, especially in the case of large databases and in presence of redundant information. The meaning and the quality of selected attributes heavily influence the data warehouse’s effectiveness and the quality of derived decisions. Our research is focused on interactive methodologies and techniques targeted at supporting the data warehouse design and evaluation by taking into account the quality of initial data. In this chapter we propose an approach for supporting the data warehouses development and refinement, providing practical examples and demonstrating the effectiveness of our solution. Our approach is mainly based on two phases: the first one is targeted at interactively guiding the attributes selection by providing quantitative information measuring different statistical and syntactical aspects of data, while the second phase, based on a set of 3D visualizations, gives the opportunity of run-time refining taken design choices according to data examination and analysis. For experimenting proposed solutions on real data, we have developed a tool, called ELDA (EvaLuation DAta warehouse quality), that has been used for supporting the data warehouse design and evaluation.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11390/859714
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