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Time-variant: Data are stored to provide information from an historic perspective
(e.g., the past 5–10 years). Every key structure in the data warehouse contains, either
implicitly or explicitly, a time element.
Nonvolatile: A data warehouse is always a physically separate store of data trans-
formed from the application data found in the operational environment. Due to
this separation, a data warehouse does not require transaction processing, recovery,
and concurrency control mechanisms. It usually requires only two operations in data
accessing: initial loading of data and access of data.

In sum, a data warehouse is a semantically consistent data store that serves as a
physical implementation of a decision support data model. It stores the information
an enterprise needs to make strategic decisions. A data warehouse is also often viewed
as an architecture, constructed by integrating data from multiple heterogeneous sources
to support structured and/or ad hoc queries, analytical reporting, and decision making.
Based on this information, we view data warehousing as the process of construct-
ing and using data warehouses. The construction of a data warehouse requires data
cleaning, data integration, and data consolidation. The utilization of a data warehouse
often necessitates a collection of decision support technologies. This allows “knowledge
workers” (e.g., managers, analysts, and executives) to use the warehouse to quickly and
conveniently obtain an overview of the data, and to make sound decisions based on
information in the warehouse. Some authors use the term data warehousing to refer
only to the process of data warehouse construction, while the term warehouse DBMS is
used to refer to the management and utilization of data warehouses. We will not make
this distinction here.
“How are organizations using the information from data warehouses?” Many orga-
nizations use this information to support business decision-making activities, includ-
ing (1) increasing customer focus, which includes the analysis of customer buying
patterns (such as buying preference, buying time, budget cycles, and appetites for
spending); (2) repositioning products and managing product portfolios by compar-
ing the performance of sales by quarter, by year, and by geographic regions in order
to fine-tune production strategies; (3) analyzing operations and looking for sources of
profit; and (4) managing customer relationships, making environmental corrections,
and managing the cost of corporate assets.
Data warehousing is also very useful from the point of view of heterogeneous database
integration. Organizations typically collect diverse kinds of data and maintain large
databases from multiple, heterogeneous, autonomous, and distributed information
sources. It is highly desirable, yet challenging, to integrate such data and provide easy
and efficient access to it. Much effort has been spent in the database industry and
research community toward achieving this goal.
The traditional database approach to heterogeneous database integration is to build
wrappers and integrators (or mediators) on top of multiple, heterogeneous databases.
When a query is posed to a client site, a metadata dictionary is used to translate the
query into queries appropriate for the individual heterogeneous sites involved. These