Oracle Application Server Tips by Burleson
The component allows for the fast definition
and deployment of web pages. Like the non-Oracle tools such as
DreamWeaver and MS FrontPage, Oracle Portal allows the developer to
create and deploy web content. The important difference is that
Oracle Portal allows the developer to include content dynamically
created, personalized web pages from multiple data sources using
Portlets. The Oracle Portal product provides the following
Portal page creation, management and
The assembly of web content from multiple
sources using Portlets.
Web page content that contains data
retrieved from a database.
Portal content publishing facilities using
Advanced features such as text searching
(via Oracle Text) and wireless support via XML and HTML
These components fit-together into an
architecture that allows developers to quickly create and deploy web
page content (Figure 1.8).
Figure 8: The Oracle9iAS Portal Architecture
In Figure 1.8 we see that Portal
administrator defining the Portlet content and the content for the
basic web pages. At run-time, Portal users access these definitions
to create dynamic publishing content, using the Portlet definitions,
the web page definitions, and data from inside the Oracle database.
It is beyond the scope of this book to examine all of the content
deliver features of Oracle Portal. For complete information of
using Oracle Portal see the Oracle Press book Oracle9i Application
Server Portal Handbook by Vandiver & Cox.
This component allows for the easy end-user
query implementation. In essence, Oracle Discoverer is an ad-hoc
query, reporting, analysis, and Web publishing tool. Like Crystal
Reports and Business Objects, Oracle Discoverer provides a GUI
metaphor for the specification of Oracle database content and
In addition Oracle Discoverer is a business
analysis intelligence tools, with interfaces with Oracle Clickstream
and the Oracle database. When using Oracle Discoverer, the end-user
develops workbooks. At a high level, a workbook is a bundle of
metadata that includes the following components:
The tables that participate in the query
The report formatting for the result set
The calculations to perform on the data
Once defined, these workbooks allow
inexperienced end-users to easily create ad-hoc reports against the
Oracle database using the Discoverer End-User Layer (EUL) GUI
interface. In addition, Oracle Discoverer allows for the end user
to view summary data at several levels, and includes components that
allow the end-user to drill-down into more detail or roll-up into
summary level (Figure 1.9).
Figure 9: Oracle9iAS Discoverer Architecture
As we see in Figure 1.9, there are two main
phases in Discoverer usage. The first step is the Discoverer
Administrator creating the workbooks by specifying the tables,
formatting and computation rules for any given report. The second
phase is the run-time phase, where the end-user accesses the EUL and
created customized reports using the Discoverer Wizards.
The core of administration for Oracle
Discoverer is the development and maintenance of the workbooks and
metadata objects. For example, each time and end-user runs a
report, Discoverer refers to eul_qpp_statistics metadata table in
the infrastructure to produce a time estimate for the report. For
more details on the administration and use of Oracle Discoverer, see
the Oracle Press book, Oracle Discoverer Handbook, Armstrong-Smith.
Oracle Form Server
This component is used to format and deploy
and render end-user presentation pages. An evolution of the Oracle
SQL*Forms application development tool, the Oracle Forms Server was
originally used to render screen display from Oracle content.
Enhanced to provide support for HTML, Oracle Forms Server is now
used within Oracle9iAS to render web pages that include Oracle
Because the Oracle Forms Server is the main
engine for rendering web pages, the tuning and administration of the
Forms Server is a critical aspect of Oracle9iAS administration. We
will be discussing Oracle Forms Server administration and tuning in
more details in Chapter 10, Performance Tuning with Oracle9iAS.
Analyzing page viewing behavior and creating
custom web page content on a busy eCommerce site is a formidable
computing challenge. To address these issues, Oracle has developed
the Oracle9iAS personalization and the Oracle data mining suite.
Oracle personalization is extremely sophisticated and relies on
internal data about end-users web-page visits, web-page clicks, and
referrer statistics. Even more powerful, Oracle personalization
allows for the incorporation of external metadata such as customer
demographics. It is worthwhile to note that Oracle has several
competitors in the web personalization market, notably Blue Martini,
Vignette and Personify.
The goal of Oracle9iAS personalization is to
accurately identify classes of end-users and correlate their
behavior with the behavior of other known groups of end-users.
Using sophisticated multivariate correlation techniques; web-page
contact can be customized according to predictions about each
end-users preference for web page content. The nature of this
analysis is very resource intensive, and almost all large Oracle9iAS
shops devote large servers exclusively for developing these
IT marketing professionals know that it is
critical of get the right products onto a custom web page. To be
successful, Oracle9iAS must be able to accurately predict your
propensity to buy a product, based on prior buying and browsing
patterns, and buying patterns of like-minded customers (customer
profiling). The challenge in developing these predictive models is
accurately placing visitors into consumer groups. A consumer group
is a group of customers with similar demographics and buying
Figure 1.10 shows the process of analyzing
demographic information to place visitors into consumer groups. A
visitor can be placed into a consumer group in two ways:
Figure 10: Architecture of Oracle
Once we have defined consumer groups in
Oracle Personalization, we next start a data mining procedure to
correlate the patterns of each consumer group to specific products.
The customized HTML personalization is based on data from three
Known consumer group data ? These groups
consist of predetermined summaries of consumer group
Weighted rankings of pages viewed ? This
is a measure of the popularity of product pages according to each
Historical data ? This is historical sales
data, correlated by consumer group.
Oracle Personalization uses these
sophisticated consumer group and data mining component mechanisms to
create the web content (Figure 1.11). The administration of Oracle
Personalization is simplified by using the Oracle Personalization
GUI, and the Oracle documentation has an excellent discussion of
Oracle9iAS Personalization administration.
Figure 11: The Oracle9iAS Personalization Engine at runtime
This is an excerpt from "Oracle
10g Application Server Administration Handbook" by Don Burleson
and John Garmany.