Abstract
The two mature disciplines, namely Data Mining and Data Warehousing have broadly the same set of objectives. Yet, they have developed largely separate from each other resulting in different techniques being used in each discipline. It has been recognized that mining techniques developed for pattern recognition such as Clustering and Visualization can assist in designing data warehouse schema. However, a suitable methodology is required for the seamless integration of mining methods in the design of warehouse schema. In previous work, we presented a methodology that employs hierarchical clustering to derive a tree structure that can be used by a data warehouse designer to build a schema. We believe that, in order to strengthen the decision making process, there is a strong need for a method that automatically extracts knowledge present at different levels of abstraction from a warehouse. We demonstrate with examples how mining at different levels of a hierarchical warehouse schema can give new insights about the underlying data cluster which not only helps in building more meaningful dimensions and facts for data warehouse design but can also improve the decision making process.
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Usman, M., Pears, R. (2011). Multi Level Mining of Warehouse Schema. In: Fong, S. (eds) Networked Digital Technologies. NDT 2011. Communications in Computer and Information Science, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22185-9_34
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DOI: https://doi.org/10.1007/978-3-642-22185-9_34
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