Minable Data Warehouse

Data warehouses have been widely used in various capacities such as large corporations or public institutions. These systems contain large and rich datasets that are often used by several data mining techniques to discover interesting patterns. However, before data mining techniques can be applied to data warehouses, arduous and convoluted preprocessing techniques must be completed. Thus, we propose a minable data warehouse that integrates the preprocessing stage in a data mining technique within the cleansing and transformation process in a data warehouse. This framework will allow data mining techniques to be computed without any additional preprocessing steps. We present our proposed framework using a synthetically generated dataset and a classical data mining technique called Apriori to discover association rules within instant messaging datasets.

• Type: Conference
• Year: 2009
• Where: ICEIS
• Authors: David Morgan, Jai W. Kang, James M. Kang
• Keywords: Convoluted Preprocessing Techniques, Data Mining Technique, Data Warehouse, ICEIS 2009, Information Systems