∙ Subject oriented. Data are organized by detailed subject, such as sales, products, or customers, containing only information relevant for decision support.
∙ Integrated. Integration is closely related to subject orientation. Data warehouses must place data from different sources into a consistent format. To do so, they must deal with naming conflicts and discrepancies among units of measure. A data warehouse is presumed to be totally integrated.
∙ Time variant (time series). A warehouse maintains historical data. The data do not necessarily provide current status (except in real-time systems). They detect trends, deviations, and long-term relationships for forecasting and comparisons, leading to decision making. Every data warehouse has a temporal quality. Time is the one important dimension that all data warehouses must support. Data for analysis from multiple sources contains multiple time points (e.g., daily, weekly, monthly views).
∙ Nonvolatile. After data are entered into a data warehouse, users cannot change or update the data. Obsolete data are discarded, and changes are recorded as new data.
∙ Web based. Data warehouses are typically designed to provide an efficient computing environment for Web-based applications.
∙ Relational/multidimensional. A data warehouse uses either a relational structure or a multidimensional structure. A recent survey on multidimensional structures can be found in Romero and Abelló (2009).
∙ Client/server. A data warehouse uses the client/server architecture to provide easy access for end users.
∙ Real time. Newer data warehouses provide real-time, or active, data-access and analysis capabilities (see Basu, 2003; and Bonde and Kuckuk, 2004).
∙ Include metadata. A data warehouse contains metadata (data about data) about how the data are organized and how to effectively use them.