Data warehouses are designed to facilitate the process of multi-dimensional OLAP (On-Line Analytical Processing), thereby enabling efficient support for a vast array of business decision-making applications. Data warehousing technology is introduced as a central repository of information that is continually being assembled from the operational databases of an organization, as well as from external sources. Conversely, to enable end-user access to a logically centralized and consistent view of detailed data, as well as summary information that is generally structured to specifically support enterprise-wide decision-making. Simultaneously, data warehouses are utilized as an excellent source of data for the business planning and forecasting that utilizes the Data Mining technology in order to produce its deliverables as required. An important aspect of the Knowledge Discovery In Databases (KDD) process module is inherently introduced as an application supporting module. Data Mining (DM) technology is typically utilized for the identification and modeling of hidden patterns, structures, associations, rules, etc. in large data sets in order to support problem domain decision making. The purpose of data mining is to extract and present knowledge, as consecutively identified patterns produced, into DM results being understood and verified by users, as the DM process unfolds. Data warehouse (DW) technology is introduced as a central repository of integrated data being collected and utilized in support of business process decision-making throughout an extended enterprise.
Data mining consists of more than collecting data and sorting through the data to identify different patterns. The data can be located in various properties and forms. Data mining includes the process of warehousing data and disbursing and accessing the information. However, to allow for data mining, the process must be automated and focused to solve specific problems. Data mining is associated with many other terms such as KDD, knowledge extraction, information discovery, and many more. Among these terms, KDD is the term that is most closely associated with data mining. KDD describes the overall process of converting raw data into useful information, while data mining describes the application of specific algorithms for pattern discovery. Data mining is the process of discovering implicit, previously unknown, and potentially useful knowledge from large collections of data. It is the process of finding and determining, in one step, simple, useful, and new patterns in very large data sets. Its goal is to answer the unknown, however posed, to provide assistance and direction in solving problems. Data mining is a multidisciplinary field which combines statistics, machine learning, artificial intelligence, and database technology. Data mining techniques are used to explore, classify, and extract patterns in large databases. These patterns are then transformed into knowledge.