Page 41 -

HAN 08-ch01-001-038-9780123814791

4 Chapter 1 Introduction 2011/6/1 3:12 Page 4 #4

Since the 1960s, database and information technology has evolved systematically
from primitive file processing systems to sophisticated and powerful database systems.
The research and development in database systems since the 1970s progressed from
early hierarchical and network database systems to relational database systems (where
data are stored in relational table structures; see Section 1.3.1), data modeling tools,
and indexing and accessing methods. In addition, users gained convenient and flexible
data access through query languages, user interfaces, query optimization, and transac-
tion management. Efficient methods for online transaction processing (OLTP), where a
query is viewed as a read-only transaction, contributed substantially to the evolution and
wide acceptance of relational technology as a major tool for efficient storage, retrieval,
and management of large amounts of data.
After the establishment of database management systems, database technology
moved toward the development of advanced database systems, data warehousing, and
data mining for advanced data analysis and web-based databases. Advanced database
systems, for example, resulted from an upsurge of research from the mid-1980s onward.
These systems incorporate new and powerful data models such as extended-relational,
object-oriented, object-relational, and deductive models. Application-oriented database
systems have flourished, including spatial, temporal, multimedia, active, stream and
sensor, scientific and engineering databases, knowledge bases, and office information
bases. Issues related to the distribution, diversification, and sharing of data have been
studied extensively.
Advanced data analysis sprang up from the late 1980s onward. The steady and
dazzling progress of computer hardware technology in the past three decades led to
large supplies of powerful and affordable computers, data collection equipment, and
storage media. This technology provides a great boost to the database and information
industry, and it enables a huge number of databases and information repositories to be
available for transaction management, information retrieval, and data analysis. Data
can now be stored in many different kinds of databases and information repositories.
One emerging data repository architecture is the data warehouse (Section 1.3.2).
This is a repository of multiple heterogeneous data sources organized under a uni-
fied schema at a single site to facilitate management decision making. Data warehouse
technology includes data cleaning, data integration, and online analytical processing
(OLAP)—that is, analysis techniques with functionalities such as summarization, con-
solidation, and aggregation, as well as the ability to view information from different
angles. Although OLAP tools support multidimensional analysis and decision making,
additional data analysis tools are required for in-depth analysis—for example, data min-
ing tools that provide data classification, clustering, outlier/anomaly detection, and the
characterization of changes in data over time.
Huge volumes of data have been accumulated beyond databases and data ware-
houses. During the 1990s, the World Wide Web and web-based databases (e.g., XML
databases) began to appear. Internet-based global information bases, such as the WWW
and various kinds of interconnected, heterogeneous databases, have emerged and play
a vital role in the information industry. The effective and efficient analysis of data from
such different forms of data by integration of information retrieval, data mining, and
information network analysis technologies is a challenging task.