Knowledge Discovery in Data Warehouses

As the size of data warehouses increase to several hundreds of gigabytes or terabytes, the need for methods and tools that will automate the process of knowledge extraction, or guide the user to subsets of the dataset that are of particular interest, is becoming prominent. In this survey paper we explore the problem of identifying and extracting interesting knowledge from large collections of data residing in data warehouses, by using data mining techniques. Such techniques have the ability to identify patterns and build succinct models to describe the data. These models can also be used to achieve summarization and approximation. We review the associated work in the OLAP, data mining, and approximate query answering literature. We discuss the need for the traditional data mining techniques to adapt, and accommodate the specific characteristics of OLAP systems. We also examine the notion of interestingness of data, as a tool to guide the analysis process. We describe methods that have been proposed in the literature for determining what is interesting to the user and what is not and how this approaches can be incorporated in the data mining algorithms.