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128 Chapter 4 Data Warehousing and Online Analytical Processing 3:17 Page 128 #4

queries are then mapped and sent to local query processors. The results returned from
the different sites are integrated into a global answer set. This query-driven approach
requires complex information filtering and integration processes, and competes with
local sites for processing resources. It is inefficient and potentially expensive for frequent
queries, especially queries requiring aggregations.
Data warehousing provides an interesting alternative to this traditional approach.
Rather than using a query-driven approach, data warehousing employs an update-
driven approach in which information from multiple, heterogeneous sources is inte-
grated in advance and stored in a warehouse for direct querying and analysis. Unlike
online transaction processing databases, data warehouses do not contain the most cur-
rent information. However, a data warehouse brings high performance to the integrated
heterogeneous database system because data are copied, preprocessed, integrated, anno-
tated, summarized, and restructured into one semantic data store. Furthermore, query
processing in data warehouses does not interfere with the processing at local sources.
Moreover, data warehouses can store and integrate historic information and support
complex multidimensional queries. As a result, data warehousing has become popular
in industry.

4.1.2 Differences between Operational Database Systems
and Data Warehouses
Because most people are familiar with commercial relational database systems, it is easy
to understand what a data warehouse is by comparing these two kinds of systems.
The major task of online operational database systems is to perform online trans-
action and query processing. These systems are called online transaction processing
(OLTP) systems. They cover most of the day-to-day operations of an organization such
as purchasing, inventory, manufacturing, banking, payroll, registration, and account-
ing. Data warehouse systems, on the other hand, serve users or knowledge workers in
the role of data analysis and decision making. Such systems can organize and present
data in various formats in order to accommodate the diverse needs of different users.
These systems are known as online analytical processing (OLAP) systems.
The major distinguishing features of OLTP and OLAP are summarized as follows:

Users and system orientation: An OLTP system is customer-oriented and is used
for transaction and query processing by clerks, clients, and information technology
professionals. An OLAP system is market-oriented and is used for data analysis by
knowledge workers, including managers, executives, and analysts.
Data contents: An OLTP system manages current data that, typically, are too detailed
to be easily used for decision making. An OLAP system manages large amounts of
historic data, provides facilities for summarization and aggregation, and stores and
manages information at different levels of granularity. These features make the data
easier to use for informed decision making.