Ad-hoc association-rule mining within the data warehouse

Abstract

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. Association-rule mining, which captures co-occurrence 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 information-rich 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 data-mining 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 ad-hoc 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 association-rule mining from transactionlevel data to aggregated data as well.

Granule mining oriented data warehousing model for representations of multidimensional association rules

“…To support multidimensional association rules, Goil and Choudhary (1999) presented a scalable parallel system with the techniques of OLAP and data mining to calculate support and confidence. Moreover, Nestorov and Jukic (2003) proposed an approach that can keep all query-processing within the data warehouse and extend association rules using the non-item dimension to obtain more detailed rules.…”

Combining the Data Warehouse and Operational Data Store

“…MTI has detailed historical data for the period from Jan-1999 till Apr-2004. Metadata (Nestorov and Jukic, 2003) and data marts technologies are available for all varieties of departmental and activities of business that covers all analytical analysis for marketing, sales, and customer support purposes. Figure 1 shows MTI multidimensional databases model.…”

Enhanced mining of association rules from data cubes

“…Furthermore, it lacks a formal description that enables a concrete generalization to other application domains. Extended association rules were proposed in [14] by Nestorov and Jukić. It consists in mining associations from data warehouses by utilizing the SQL processing power of the data warehouse itself without using a separate data mining tool.…”