Abstract
The Data Warehouse is a data storage medium with the purpose to produce accurate and useful information to support business stakeholders to conduct data analysis that helps with performing decision making processes and improving information resources. The data warehouse provides a single and detailed view of the organization, and it is intended to be exploited by means of OLAP (On-line Analytical Processing) tools. These tools facilitate information analysis and navigation through the business data based on the multidimensional paradigm. A crucial decision for designing multidimensional models concerns the grain of facts, determined by fact–dimension relationships. This means, that the accuracy of the information can depend on how the data model is structured to support multi-value dimensions and avoid double-counting’s. The paper presents a technique used to overcome these constraints enabling designers to abstract complexity at a conceptual level without taking into account of more complex schema structures (like bridge table) to deal with non-strict fact–dimension relationships at different granularities. The technique is demonstrated using the Pentaho tool and lessons learned from our case study, an information system to monitor the execution of public works contracts.
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Pestana, G., Catelas, P., Rosa, I. (2015). A Multi-driven Approach to Improve Data Analytics for Multi-value Dimensions. In: Rocha, A., Correia, A., Costanzo, S., Reis, L. (eds) New Contributions in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 354. Springer, Cham. https://doi.org/10.1007/978-3-319-16528-8_12
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DOI: https://doi.org/10.1007/978-3-319-16528-8_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16527-1
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