Oracle Discoverer™
Explore New Dimensions
1. Introduction
The information revolution is underway. Organizations are
generating and storing unprecedented amounts of data about their
day to day operations. A new paradigm must be uncovered to reign
in and harness these vast amounts of data. Furthermore, it is not
enough to simply collect and collate large amounts of data
without putting it to good use. With the advent of data
warehousing, organizations have begun to discover new ways of
turning data into a valuable resource. As the leader in providing
information technology, Oracle is at the fore with an array of
technologies to assist in gaining the benefit from data
warehouses. Decision makers at all levels of the organization
must be able to access and manipulate data in such a way that it
can be transformed into an organization’s most valuable
asset: information. Oracle Discoverer™ is the tool that
allows business users to turn data into information.
2. Data Warehousing
Recent surveys indicate that over 90% of mid to large sized
organizations will set up a data warehouse this year. According
to IDC, approximately 80% of organizations that have already
invested in data warehousing, view them as a major success. Why?
A data warehouse provides a distinct centralized repository from
OLTP systems that contains extracts of vital business data from a
variety of corporate databases. This data is analyzed and used as
a strategic competitive weapon. Fast, accurate analysis of
business issues affect long term survival. For example, analyzing
trends in geographic demand patterns allowed one organization to
regulate supplies accordingly and increase sales by over $2
Million.
Unlike those of operational systems, data structures in a data
warehouse are optimized for rapid retrieval and analysis. The
data is historical and is updated at some regular interval.
There are 3 general steps to defining, creating and using a data
warehouse:
1) Model end user business needs. The designers of the
warehouse must obtain information needs from the various business
users. The designers translate these information needs into a
warehouse model. Designers must take a rigorous and disciplined
approach to ensure completeness of the model.
2) Model meta-data. In conjunction with modeling the end
users’ needs, warehouse designers must also model meta-data
(data about the data). This information defines the data going
into the data warehouse and the rules associated with this data.
Since the data warehouse is subject oriented, this modeling of
meta data might cross functional business areas. The meta data
falls into two categories: loading and user.
a) Loading view: This describes all the data sources and all the rules for extracting, scrubbing, and transporting data to the warehouse.
b) End user view: Here, the model matches the business uses of the data. This is the floor plan of the warehouse that end users use to access and explore their information.
3) Evaluate, determine, and implement extraction,
transformation, and access tools. Once designers have
established a model of the end user needs and accompanying meta
data in the repository, tools must be chosen for stocking the
data warehouse. The final decision entails selecting the tools
that end users will work with to access the information stored in
the warehouse.
3. The Oracle Warehouse
The Oracle Warehouse consists of a suite of products and services
that span the entire process of defining, designing, and
implementing a data warehouse. The figure below shows the
components in the Oracle Warehouse:
Figure 1 - The Oracle Warehouse
Any Source. The data collected in the Oracle
Warehouse can come from a variety of sources, including both
operational (internal) and external. Traditionally, most of the
data in a warehouse has come from internal operational systems
such as order entry, inventory, or human resource data. However,
external sources (demographic, economic, internet) are becoming
more and more prevalent and will soon be providing more content
to the data warehouse than the internal sources. Both sources
must be harnessed and melded into a single storage container to
provide end users with seamless access to both kinds of data.
Any Data. Because of the breadth of users now
involved with accessing the data warehouse, system designers are
faced with a diverse set of requirements. Access to the data must
be fast, straightforward, and intuitive. The mass of users
require straightforward query and drill capabilities, while
others require more sophisticated analytical capabilities. The
data source must be able to handle new formats of data such as
audio, video, text and spatial. Furthermore, vast historical
requirements from an increasing number of users frequently leads
to very large databases (VLDBs). To satisfy these requirements,
Oracle provides both a relational (Oracle7) and a
multidimensional (Express Server) solution.
Any Access. Oracle offers a comprehensive suite
of tools that allows all types of users to access the data held
in the warehouse, including: ad hoc querying and reporting,
drill-down and pivot, and full analysis (modeling, forecasting,
what-if analysis, etc.). The majority of users need a
straightforward, intuitive tool that allows them to easily access
the data to make common business decisions. A separate set of
analytical users need to do more sophisticated, lengthy analysis
in support of business strategies. Taken together, the need to
access the information spans an entire organization. The data
warehouse of today has moved from the executive and analyst
domain to include this broad category termed knowledge workers.
This evolution is a direct result of pushing authority down the
command chain; flattening the organization. Given the more
prominent role of the knowledge worker, the decision on which
tools to implement becomes much more critical.
To address this diverse set of knowledge worker needs, Oracle
provides a unique and complete set of business end user access
tools.
Oracle Discoverer™ is the end user query, reporting,
drill/pivot, and web publishing tool that allows users to gain
rapid access to the relational data warehouse allowing them to
make more informed business decisions.
Express Analyzer™ is the end user Analysis and web access
tool that provides sophisticated analysis capabilities such as
forecasting, modeling and what-if analysis.
Express Objects™ is an OLAP development tool, tightly
integrated with Express Analyzer.
4. Oracle Discoverer™ - Product
Architecture Overview
Figure 2 - Oracle Discoverer™ Architecture
Oracle Discoverer™ is comprised of 3 main elements:
1) User Edition
2) Administration Edition
3) End User Layer™
4.1 Oracle Discoverer™ - User
Edition
The User Edition enables users to query the warehouse, graph
results, create reports, perform drill and pivot analysis and
publish results to the World Wide Web. Given such a fundamental
role, this module must meet a number of end user requirements.
During design and development of the End User component, Oracle
focused in 3 key areas:
1) Ease of Use
2) Performance
3) Flexible Warehouse Exploration
4.1.1 Ease of Use
For non-technical, business-oriented end users, usability of
access tools is the single most important consideration. Oracle
Discoverer™ provides a unique interface, where you interact
directly with the data you are familiar with. There is no
requirement to go to a separate conceptual dialogue to pivot, or
a separate "drill mode" to drill.
4.1.1.1 User Interface Development
A Unique Approach.
Traditionally, engineering focus has been primarily on the
technical integrity of the product being built. As an integral
part of development of Oracle Discoverer™, the software
engineers were exposed to customer and user feedback in a variety
of ways. Such exposure introduced a newfound understanding of the
manner in which end users would like to interact with their data
warehouse access tools. Based upon this understanding, an
entirely new approach to development was taken with Oracle
Discoverer™.
Prototyping Design Possibilities.
Early in the development process for Oracle Discoverer™,
paper prototypes were used extensively by a specialized group of
User Interface design professionals. Paper prototypes of the user
interface were shown to testers, marketers, product managers and
potential customers in an effort to solicit input on areas such
as ease of use and intuitiveness. Based on this early feedback,
the proposals evolved until a basic prototype design was settled
upon.
Focus Groups and StoryBoards.
Once the paper design phase for Oracle Discoverer™ was
completed, the User Interface (UI) design group used software to
build ‘mock’ screens or StoryBoards in order to solicit
more advanced feedback about how users interact with the tool.
The purpose of the Focus Group was to gain a better understanding
of a user’s thought process when faced with performing a
specific task. For example, ‘How do I extract information
about last years sales figures for the East and West Region and
produce a meaningful report with summary totals?’ The user
interface continued to be tailored according to this feedback.
Once the members of the UI design team were satisfied with the
interface, the software engineers started coding.
Usability Laboratories.
The usability focus of Oracle Discoverer™ did not stop with
Focus Groups. Throughout the development of the project, the UI
design team has run several usability tests with a broad range of
users from various technical backgrounds. This ensured the
development team received constant iterative feedback and could
keep focus on the usability of the product. Usability testing
allows developers to further refine the interface by providing
real-world insight into issues and concerns that affect end
users. Current and future development of Oracle Discoverer™
will continue to leverage the benefits of extensive usability
testing.
4.1.1.2 Usability
The net result of this innovative approach to development is an
interface that provides unsurpassed usability with an intuitive
look and feel.
Figure 3. Oracle Discoverer™ User Interface
Wizards, Cue Cards and Quick Tour (CBT)
Oracle Discoverer™ uses a modern wizard-based approach to
lead you through each step of the query building process. Wizards
greatly simplify common tasks. Additionally, task-oriented
assistance in the form of cue cards and computer based training
ensure a minimal training curve for new users. Via the newly
designed interface, you produce results immediately with no
supervision.
Single User Interface for Queries, Reports, and Exploration.
Unlike many products, Oracle Discoverer™ provides one
consistent interface for querying, reporting and drill down/pivot
functionality. Oracle Discoverer™ blurs the distinction
between these approaches, enabling an inexperienced user to
retrieve and analyze information without understanding the
conceptual differences and terminology of these technologies.
Windows95 Features.
Oracle Discoverer™ incorporates the advanced ease of use
features of Windows95: drag and drop information into a query,
using the right mouse button for shortcuts and saving results
using long filenames and extensions, to name a few.
The key aspect of the user interface is that you interact
directly on the data they see before them - there is no separate
conceptual dialogue to use to either "slide-and-dice"
or drill. The net result is superior overall usability which
frees you to become immediately more productive.
4.1.1.3 End User Layer
The End User Layer is a server based, low-maintenance, powerful
mechanism for providing end users with a business-oriented view
of the data warehouse.
The End User Layer :
1) Abstracts the complexity of the underlying database structures.
2) Defines drill down and other related analysis information.
3) Automatically creates and maintains summary tables.
4) Enables automatic query redirection to summary tables.
Hiding Complexity
Without the concept of an End User Layer, you are forced to
access their relational data directly using SQL. You need to
understand underlying relational database structures such as
tables, columns, joins, etc. For a small subset of users who are
technically experienced, this is perfectly acceptable. However,
the knowledge workers of today span all functional areas and
technical abilities. Clearly, we need a way to mask the
underlying complexity of the database to provide end users with
an interface they can easily understand. The End User Layer of
Oracle Discoverer™ provides just such a mechanism.
Figure 4. The End User Layer
Via the End User Layer, Oracle Discoverer™ is able to
logically group together related sets of information in the form
of Business Areas. A Business Area may contain, for example,
personal data, depar&tradeental data (Sales, Marketing,
Finance), data relating to a specific topic (Customer
information), and so forth. While the underlying technical
definition of a Business Area may contain as many references to
database objects as desired, users interact with terminology and
business groupings that they understand.
Subsequent levels of detail are grouped in a hierarchical manner.
A Business Area comprises a number of Folders (roughly analogous
to database tables or views) which in turn contain a number of
Items (roughly analogous to database columns). Relationships
between folders, (corresponding to joins between tables) are
created automatically in the End User Layer based on referential
integrity constraints in the RDBMS, or on matching column names.
A Folder can also group columns from different tables into a
single logical unit known as a Complex Folder. This flexibility
allows the meta data in the End User Layer to be set up in the
manner that best suits the business-oriented end users.
Drill Down and Other Analysis Information
More importantly, the End User Layer allows the administrator to
define informational structures that do not exist in the online
database data dictionary. These structures direct users to easily
do further analysis of the data, such as drilling and
slice-and-dicing. Examples of these additional informational
structures include hierarchies, computed items, additional joins
and alternative sort orders.
Hierarchies
The RDBMS data dictionary holds a small amount of meta
information such as column length, precision etc., but lacks
information on how items are related to one another. Areas of
business data are naturally related to one another in some
hierarchical way. For example, Regions can be broken down into
States, and States into Cities, and so on. Similarly, employees
have managers, managers have supervisors. When exploring data, it
is extremely useful to traverse up or down these hierarchies
looking at increasing or decreasing levels of detail.
The End User Layer allows us to define these hierarchies. There
are three basic types of hierarchies:
- Item hierarchies - define relationships among columns (Regions, States, Cities)
- Value hierarchies - define relationships among data values (Employee, Manager)
- Date hierarchies - define relationships among date formats (Years, Quarters, Months)
Computed Items
In general, data that is useful to users may not exist explicitly
as a column in the database. Frequently this data must be derived
from existing database columns. A simple example is a data item
such as Profit which may be defined as ‘Revenue -
Cost’. Computed items are expressions pre-defined in the End
User Layer or created at run time based on existing columns in
the database. The complexity of the calculation is hidden from
end users.
Alternative Sort Keys
In business practice, there is a common need to sort data by
non-conventional methods. Alternative sort keys enable you to
define the sort order for your information. One example of this
is in the financial world, where months may be defined as M1 to
M12. The alternative sort key provides an automatic mechanism for
ensuring these dates are sorted correctly in every circumstance.
Summary Tables
Summary tables greatly enhance the performance of information
retrieval in a large volume data warehousing environment. In a
relational database, users often summarize detail data "on
the fly" to find aggregate values. This results in
time-consuming and resource intensive queries that dramatically
affect performance of the system. Factor in that the typical
system supports dozens, hundreds, or thousands of users
performing similar types of queries and it becomes clear that a
better solution is required. Using Oracle Discoverer™, you
can create and maintain pre-summarized data, and the automatic
summary redirection capability reduces the amount of data to be
searched while also reducing (or eliminating) the need for
extensive calculations. The implementation of summary tables are
just one of the many features of Oracle Discoverer™ geared
towards improving performance.
4.1.2 Performance
One clear result from the usability tests and focus group
feedback is that performance is ultimately a usability issue. If
an end user access tool does not provide rapid response times, it
will be summarily rejected by users. Oracle Discoverer™ is
a breakthrough development which resolves the performance issues
common with many end user access tools in the market today.
4.1.2.1 A Solution to Long Running Queries
Data warehouses are typified by large amounts of data and an ever
growing number of users which both contribute to increasingly
long running queries. Oracle Discoverer™ provides two
mechanisms for reducing the negative impact of long running
queries.
Reactive Query Governor
A reactive query governor allows end users or administrators to
set an upper threshold on query execution time. If a query is
still running when the time threshold is reached, the query is
automatically terminated and valuable system resources are
released. Clearly, such a safety device is imperative, yet it
provides only part of the solution for dealing with long running
queries. The shortcoming of this mechanism is that the query
runs, tying up system resources until the threshold is hit, at
which time the query is terminated. The requesting end user is
forced to wait and is ultimately left without the query results.
To effectively deal with this situation, Oracle Discoverer™
also employs an altogether different and unique type of query
governor.
Predictive Query Governor
The predictive query governor in Oracle Discoverer™
provides an estimate of the retrieval time before a query is run.
If the query is predicted to take longer than a user-defined time
threshold, Oracle Discoverer™ warns you and allows you to
determine if the query should be run. This feedback enables you
to make sensible decisions on whether to run a query immediately
or refer it to a batch process for later calculation. Valuable
system resources are conserved, overall load is reduced, and end
users wait less.
4.1.2.2 Summary Tables and Automatic Summary Redirection
Summary tables are required to ensure performance against large
volume data warehouses. Issues on summary table creation and
maintenance are discussed later, under section 4.2.3 Summary
Management.
Assuming that summarized tables are available, the question then
becomes how to gain the maximum benefit from them.
Oracle Discoverer™ provides a major breakthrough in this
area, providing the first true automatic summary redirection
capability. As users issue requests for summarized data, Oracle
Discoverer™ checks to see if the requested data has already
been pre-summarized. If so, the query engine generates and issues
the appropriate SQL and the query is automatically redirected to
run against the summary table containing the pre-summarized data.
If a table doesn’t exist, Oracle Discoverer™
ascertains the closest summarized match down the hierarchy, and
aggregates from that level. This unique capability provides rapid
response times and lightens load on the server, without any
effort from either the user or administrator.
4.1.2.3 Client Side Cubic Cache
Oracle Discoverer™ employs a sophisticated client-side
cubic cache that enables rapid analysis without re-querying the
database. Using the benefits of the client/server architecture,
the results of a query are compressed and indexed in a memory
efficient cubic cache, completely transparent to both end user
and administrator. The local storage (caching) allows end users
to ask subsequent questions about the data without having to
re-execute the query on the server. While a user rotates or
reformats the data, all processing is handled locally to provide
exceptional performance. As additional data is requested by the
user, Oracle Discoverer™ fetches only the newly requested
data and incorporate it into the existing cache. Support for
multiple SQL statements enables the results of additional queries
or measures to be dynamically added to the cache without
re-querying existing data. As more data is retrieved, unwanted
data is seamlessly removed on a least-recently-used basis to
ensure the cache does not grow endlessly. The cache supports any
number of dimensions or measures and users may specify it’s
physical size and location.
4.1.2.4 Leveraging Oracle7, Release 7.3
Data warehouse applications require different processing
techniques than OLTP applications due to the complex, ad hoc
queries running against large amounts of data. To address these
special requirements, Oracle Discoverer™ can directly
leverage the rich variety of query processing techniques (e.g.,
bi&tradeapped indexes, hash joins), sophisticated query
optimizations (e.g., support for "star" and
"snowflake" schema’s), and scaleable architecture
of Oracle7, Release 7.3.
4.1.3 Flexible Warehouse Exploration
Many types of data analysis involve exploring deeper
levels of detail in a given area of information. This operation
is commonly known as a "drill." The drill paradigm in
Odysseus is unique in that it employs a "Just-in-time"
strategy analogous to browsing the World Wide Web. Warehouse
administrators are not forced to predict which areas of
information end users will need to work with, in the same way as
an individual browsing the web is not forced to stay within some
pre-defined set of web pages. The only data queried from the
server and brought over to each client machine is the data
explicitly requested by the user - nothing more, nothing less.
Large amounts of unnecessary overhead for both administrator and
system are avoided altogether. The "surfing" metaphor
of the World Wide Web precisely describes the information
retrieval paradigm in Oracle Discoverer™.
4.1.3.1 Conditional Drills
To further increase the power and flexibility of the drilling
mechanism, Oracle Discoverer™ includes support for a number
of different styles of drill. Oracle Discoverer™ supports
conditional drills, enabling you to drill from one level of
detail to another according to some subset of the information,
greatly reducing the amount of data received. For example, rather
than simply drilling from region (North, South, East, and West)
to detailed information for all regions, a user may only need to
drill to detailed information for the North region. Conditional
drills also allow you to drill to any level of detail, bypassing
intervening levels. Consider an example with Country, Region,
State, City. You may want to drill from Country level data
directly to city level data without first having to fetch data
pertaining to region and state. Oracle Discoverer™ frees
you to explore the information you need, when you need it.
4.1.3.2 HyperDrill (Drill to Detail)
A complex issue in data warehouse access tools today is the
requirement to drill through different levels of summarized data
down to detailed rows (e.g., drill down a variance report in your
general ledger to find the detail invoices that cause anomalies).
Oracle Discoverer™ resolves this issue by providing a
unique Hyper-Drill capability to rapidly drill from summary
information to detail rows in the relational database.
4.1.3.3 HyperDrill Plug-In (Drill Out)
In an extension of the Hyper-Drill capability, Oracle
Discoverer™ also supports drill out to external
applications via the HyperDrill Plug-In. For example, users may
drill out to a multimedia application, a Microsoft Word or Excel
application, or to their favorite World Wide Web browser.
4.1.4 Web Enabling Oracle Discoverer™
Disseminating information quickly and easily throughout the
enterprise is critical to the success of any business, and the
World Wide Web (WWW) is rapidly becoming recognized as the most
powerful and cost-effective mechanism to achieve this. Extending
the multidimensional analysis model of Oracle Discoverer™
to the World Wide Web (WWW) has a number of advantages:
- Fast and easy distribution to any business user with a WWW browser. The user does not have to be a Oracle Discoverer™ user.
- Lightweight and readily available from virtually any model of PC. By eliminating or postponing hardware upgrades, organizations can save money and re-use existing computer equipment.
- Portability to all client platforms
4.1.4.1 Web Publishing
Web Publishing is the ability to publish an existing workbook or
report seamlessly to the world wide web. Reports from Oracle
Discoverer™ are exported to the file system as H&tradeL
(Hyper Text Markup Language). Any user who has access to a web
server can use a supported web browser to access this
information.
Web publishing is the simplest way to enable data-access via the
WWW. It boasts a number of advantages:
- Simplicity - It is very easy to generate H&tradeL all classes of users, from novice to experts, can accomplish this.
- Portability - the generated pages can be read on any client - Macintosh, Motif, Windows.
- Lightweight - the file sizes are small which does not cause a huge overhead on the system.
4.2 Oracle Discoverer™ - Business
Administrator
The Administration Edition of Oracle Discoverer™ is used
for the initial setup and ongoing maintenance of all aspects of
the End User Layer. The administrator performs a variety of tasks
including maintenance of business areas, folders, summary table
creation, and end user access. Clearly, the implementation of the
administration tools are a critical success factor when setting
up the end user environment.
During design and development of the Administration Edition,
Oracle focused in 3 key areas:
- Ease of Use
- Server Based Administration
- Summary Management
4.2.1 Ease of Use
The unique approach to UI development of the User Edition is
carried across to the Administration Edition. With the same User
Interface design engineers concentrating on the interface, the
Administration Edition features similar usability features that
are found with the End User component. An administrator is free
to concentrate on the tasks at hand rather than uncovering the
intricacies of the tool. Most common administration tasks can be
accomplished either by default via the Wizard-based, Windows95
style interface.
Figure 6 - Oracle Discoverer™ Administration Edition User Interface
4.2.2 Server Based Administration
The administrator’s primary task is the creation and
maintenance of the End User Layer. The implementation of the meta
layer in Oracle Discoverer™ has key advantages for
administrators:
4.2.2.1 Central Database Repository
The data and data structures that comprise the End User Layer are
all stored in the database on the server. A server based meta
layer provides administrators with one central repository for
maintenance, rather than worry about the distribution
nigh&tradeares of a client/file based meta layer.
4.2.2.2 Easy to Create, Maintain, and Access
The emphasis on the user interface and usability of the tool has
ensured the creation and maintenance process is the simplest
possible. Accessing the single, server-based End User Layer is as
easy as connecting to the database.
4.2.2.3 Scalable
Since the End User Layer is comprised of database schema objects
stored in the relational database, the model scales precisely as
well as the database scales. This is an important consideration
as data warehouses grow rapidly in both size and amount of users.
4.2.2.4 Leverage Security of the RDBMS
Relational databases have matured over the years and now provide
rich security mechanisms. Administrators of the End User Layer
can directly leverage the security features of the underlying
database such as end user access to the database, tables, and so
on. No separate security model is required, along with the
learning and maintenance headaches associated.
4.2.2.5 Support Any Schema Design
Administrators can build up the End User Layer regardless of how
the data structures in the database are set up. Users are
presented with a uniform view of the data even as underlying
database structures change.
4.2.3 Summary Management Strategy
Oracle Discoverer™ uses summarization management techniques
that generate and maintain the required combinations of
summaries, and populate meta-data with information describing
what summaries are available. Sparse matrices are handled
automatically, further reducing space requirements. When a query
such as
‘Show total no of staff in Department 10 over the last 3 years’
is asked, the summarization meta-data causes SQL generation to
be directed towards the pre-summarized data automatically, and
the query is satisfied by a simple indexed search instead of a
large aggregation. The larger the database, the bigger the
benefit of such an approach, and the effect is to make the
response times of queries consistently quick, with operations
like drill down and pivot being performed transparently against
the appropriate summary.
However, this raises several issues for administrators:
1. How do I know which summary tables are required?
2. How do I build and maintain these summary tables?
3. How do I ensure these are always utilized?
The task of creating summary tables is also daunting. For the
general case, given N items (or columns) on an axis of a
cross-tabular report there are 2(N)-1 possible ways of combining
the items. The number of aggregate rows required depends on the
number of valid combinations of item values, and the situation is
complicated further when the items are in multi-level
hierarchies, with Month rolling up to Quarter and Year. However,
there are pruning techniques that can be employed: for example by
specifying which combinations of dimensions or levels do not make
business sense to combine, and by not aggregating at all levels,
allowing some minimal aggregation from a lower level, where
required. It is also true that single dimension combinations
result in the fewest number of extra rows being added, and also
give the biggest gain in terms of performance by avoiding
aggregation.
Although there is a trade-off in terms of database space used to
hold summaries, this can be made configurable, allowing an
administrator to create pre-aggregated summaries where they are
most effective. The administration utility in Oracle
Discoverer™ makes this process more manageable.
Overview of Summary Table Creation
As end users issue requests to the warehouse, on ongoing audit is
gathering statistics about those queries (tables hit, number of
rows, etc.). An internal algorithm monitors these statistics,
determines inefficient access paths to the warehouse tables and
makes suggestions to the administrator regarding appropriate
summary tables. Along with recommendations for summary tables,
the administrator is also presented with a space usage estimate
of each recommendation. Administrators then make informed
decisions regarding the space trade-off of such summaries. The
summary tables the administrator chooses to accept are then
created and populated automatically at a time specified by the
administrator. The only thing left for the administrator to do is
specify the interval upon which the summary tables should be
refreshed (nightly, weekly, etc.). Oracle Discoverer™
becomes immediately aware of the newly created summary tables,
with subsequent user requests for that data being automatically
redirected to the summarized data.
This summary management also provides the ability to skip levels
of summarization within a hierarchy, to reduce space usage. The
automatic summary redirection capability then instantly
aggregates data from the next summary table available, providing
an unbeatable combination of local aggregation and server-based
summary data to gain the maximum benefit for both performance and
space management.
The normally daunting task of summary table administration is
made automatic for administrators with Oracle Discoverer™.
5. Summary
5.1 Superior Ease of Use
The focus placed on usability during the development process of
Oracle Discoverer™ has produced an end user tool that frees
end users and administrators to focus on the task at hand by
reducing the learning curve with an easy to use, intuitive
interface.
5.2 Optimal Productivity
The usability of the interface coupled with unique performance
features automatically gives users greater productivity. For end
users the faster response times, an easy to user interface, and a
business-oriented view of the data warehouse all contribute to an
increased ability to make better informed decisions faster. For
administrators, the same easy to use interface along with
powerful administration and summarization strategies reduce
administration overhead and gives greater flexibility to the
warehouse environment.
5.3 Leverage Existing Investment
Oracle Discoverer™ is a key component of the Oracle
Warehouse. It’s technology directly builds upon and extends
existing Oracle technology. Tight product integration exists
between Oracle Discoverer™ and Designer/2000 to load and
maintain the End User Layer; between Oracle Discoverer™ and
the reports component of Developer/2000 to extend reporting
types, and with Oracle Applications to remove metalayer setup and
maintenance. Existing in-house technology and expertise with
operational systems can be leveraged with emerging data warehouse
projects. Key technologies of Oracle Discoverer™, such as
the End User Layer and summarization techniques, will become
standard in the Oracle Warehouse. Within the Oracle Warehouse,
Oracle Discoverer™ extends existing technologies and will
serve as a cornerstone as we look ahead.
Availability
Oracle Discoverer™ will enter beta in the Fall of 1996 and
will be production by the end of calendar year 1996 on Windows 95
and Windows NT. A Windows 3.1 version and full-featured web
version will be available shortly after.