Many organizations often underutilize their already constructed data warehouses. In this paper, we suggest a novel way of acquiring more information from corporate data warehouses without the complications and drawbacks of deploying additional software systems. Associationrule mining, which captures cooccurrence patterns within data, has attracted considerable efforts from data warehousing researchers and practitioners alike. Unfortunately, most data mining tools are loosely coupled, at best, with the data warehouse repository. Furthermore, these tools can often find association rules only within the main fact table of the data warehouse (thus ignoring the informationrich dimensions of the star schema) and are not easily applied on non transaction level data often found in data warehouses. In this paper, we present a new datamining framework that is tightly integrated with the data warehousing technology. Our framework has several advantages over the use of separate data mining tools. First, the data stays at the data warehouse, and thus the management of security and privacy issues is greatly reduced. Second, we utilize the query processing power of a data warehouse itself, without using a separate data mining tool. In addition, this framework allows adhoc data mining queries over the whole data warehouse, not just over a transformed portion of the data that is required when a standard datamining tool is used. Finally, this framework also expands the domain of associationrule mining from transactionlevel data to aggregated data as well.
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