Mining the data for deriving the knowledge is an important area of current research. Repository of data plays an important role in the mining process. Data can be stored in flat files in the form of transactions, which could be relational or object-oriented. This paper aims at discussing the various aspects of mining of data stored in data warehouses. The advantages of a data warehouse in the mining process compared to other repositories are analyzed to a great extent. Various issues like data summarization, multi-objective mining and multi-level mining are also focused upon. At the end, the challenges involved in this domain are also highlighted.

Data mining has become a very popular branch for developing applications and performing research in the area of knowledge discovery for the past few decades. After gathering a large amount of data by Online Transaction Processing (OLTP), the next step for any organization is to analyze the data and then derive (mine) some useful results from it. There are several methods available for data mining including association rule mining, classification, clustering, etc. One can choose any one of them or any combination according to the requirement. The major factor which is to be considered in data mining is the platform or repository of the database. Much research has been done for databases such as transaction, relational, text and web databases.

One other repository of databases is data warehouse where multidimensional data is present. Online Analytical Processing (OLAP) is to perform various operations on data of data warehouse to represent the data in various summarized forms. The same data which is present in data warehouse is the best candidate input for various data mining tasks. The integration of OLAP operations with data mining methods is called Online Analytical Mining (OLAM). The advantages of OLAM include: (i) Knowledge can be generated from different cubes of data warehouse, (ii) High quality of data which is required by any data mining method available in a data warehouse, and (iii) More than one data mining method can be applied in sequence on data.

Systems Management Journal, Online Transaction Processing, OLTP, Online Analytical Processing, OLAP, Online Analytical Mining, OLAM, Knowledge Discovery in Databases, KDD, Network Management, Risk Analysis, Brand Loyalty, Graphical User Interface, Data Modeling, Data Warehouse, Data Base Management System, DBMS, Multidimensional Data Model.