Download Business Intelligence for SUPRA® WHITE PAPER

Business Intelligence
for SUPRA®
A Technical Overview
WHITE PAPER
Cincom In-depth Analysis and Review
S I M P L I F I C AT I O N T H R O U G H I N N O VAT I O N ®
Business Intelligence
for SUPRA®
A Technical Overview
Table of Contents
Complete Business Intelligence Solution . . . . . . . . . . . . . 1
Major Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Data Access for SUPRA . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Application Program Interfaces . . . . . . . . . . . . . . . . . . . 3
Reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
WHITE PAPER
Cincom In-depth Analysis and Review
Data Warehouse for SUPRA . . . . . . . . . . . . . . . . . . . . . . . . 4
ETL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Extract Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
ETL Steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
ETL Jobs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Warehouse Database . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Client Applications and Reports . . . . . . . . . . . . . . . . . . 6
Analytics for SUPRA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Dimensional Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Developing Multi-dimensional Applications . . . . . . . . 9
Application Programming Interfaces . . . . . . . . . . . . . . . 9
Visual OLAP Components . . . . . . . . . . . . . . . . . . . . . . 10
Multi-dimensional Reports . . . . . . . . . . . . . . . . . . . . . . 11
Microsoft Excel Support . . . . . . . . . . . . . . . . . . . . . . . . 11
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1
Complete Business Intelligence
Solution
Business Intelligence for SUPRA helps you to derive
maximum value from your corporate data with high
productivity and low risk. It provides an end-to-end
solution for direct access to SUPRA data, warehousing of
SUPRA data and multi-dimensional analysis of SUPRA
data. It allows you to quickly transform volumes of data
from your transactional systems into decision-support and
analytic information.
Business Intelligence for SUPRA is implemented in layers
providing you with the flexibility to solve a number of
business intelligence problems. You can directly access
your host database using SQL-based tools and
applications. You can reorganize your transactional data
into a data warehouse to integrate with other data and
provide historical information. You can also use multidimensional analysis to reveal product performance, profit
trends or productivity comparisons. This is shown in the
following image.
Business Intelligence for
SUPRA
Analytics for
SUPRA
Multidimensional
Analysis
Data Warehouse for
SUPRA
Historical &
cleansed
data
Data Access for
SUPRA
Direct SQL
Access
SUPRA
Major Advantages
Business Intelligence for SUPRA provides you with the
following advantages.
Optimized for SUPRA PDM
Accessing SUPRA PDM data for decision support can be
complex as SUPRA implements specialized structures for
navigation and performance optimization. Also, decisionsupport applications and tools are typically implemented
on Windows, UNIX or Linux, and the data types used by
the host PDM must be properly converted. Business
Intelligence for SUPRA understands the SUPRA PDM data
structures and how to optimize the extraction of host data.
It understands the platform-specific data types and how to
convert them to forms that are suitable for analysis.
Cost-Effective
Business Intelligence for SUPRA is a cost-effective analytics
solution for SUPRA PDM. In comparison, third-party
business intelligence products can be very expensive.
They do not provide a solution that integrates with
SUPRA, so you also have the added cost of developing
custom data access or data extraction procedures.
Low Risk
Business Intelligence for SUPRA gives you the flexibility to
try solutions and demonstrate the benefits without a large
software investment. For example, a “data-mart” solution
could be implemented where the analysis focuses on a
single subject area such as customers, inventory or
shopping baskets. The layered implementation is
integrated using standard query languages, standard
programming interfaces and standard protocols. This
allows you to integrate with your existing tool sets and
decision support software.
2
Data Access for SUPRA
Data Access for SUPRA gives you direct access to your
SUPRA operational data through the SQL language. Direct
data access provides you with the following advantages:
• You can access current information without the need to
migrate data.
• Changes are not required to operational data or existing
applications.
• Transactional access is available if needed.
• Third-party tools can access SUPRA data using standard
SQL and SQL-based interfaces.
Data Access for SUPRA provides a wide range of options
for data access. Support for JDBC is provided enabling
support for J2EE applications and tools. ODBC and
OLEDB drivers are provided enabling support for .Net
applications and tools. A reporting framework is provided
to allow you to design and deploy reports without the
need to program. This is shown in the following image.
Data Access for SUPRA provides SQL support for SUPRA
PDM. This allows you to directly access SUPRA PDM using
standard relational APIs and access SUPRA data using
relational tools. In Data Access for SUPRA, the metadata
for the fields in a PDM file are mapped into an entity
called a foreign table. Support is provided so that this
foreign table can be treated as a standard SQL table by
relational applications and tools. Data Access for SUPRA
optimizes the access and navigation of PDM files based
on the information retrieved from the PDM directory
(linkpaths, indices and control keys).
Data Access for SUPRA allows applications to access
SUPRA PDM files as standard SQL tables. To enable this,
you define SUPRA PDM files as foreign tables. An example
is shown in the following figure.
Data Access for SUPRA
Employee
Foreign
Table
Empl-Dept
Foreign
Table
Department
Foreign
Table
Jasper Reports
J2EE Applications
JDBC
ODBC
EMPL
EMDE
DEPT
.Net Applications
OLEDB
Data Access Server
Data Access for SUPRA
SUPRA
SUPRA PDM
Foreign table definitions are somewhat like SQL view
definitions. You define the fields to access in a PDM file,
similar to the way you would define the columns to access
in a table using an SQL view.
In this example, you have an employee primary file called
EMPL, a department primary file called DEPT and a
related file called EMDE that relates multiple employees
to a department. To access these, you would define a
foreign table for each PDM file to Data Access for SUPRA.
In the foreign table definitions, you relate column names
with field names like the following.
3
Employee
EMPL
EMPLROOT
employee-name……………….
EMPLNAME
employee-numb……………….
EMPLNUMB
Empl-Dept
EMDE
emde-empl-numb……………..
EMDEEMPL
emde-dept-numb……………...
EMDEDEPT
DEPTLKEM
Department
DEPT
DEPTROOT
department-name………..……
DEPTNAME
department-numb…………..…
DEPTNUMB
DEPTLKEM
You could then access these using SQL as in the following example:
SELECT employee-name, employee-numb FROM Employee
Application Program Interfaces
Data Access for SUPRA provides JDBC (Java Database
Connectivity) to enable support for J2EE applications and
tools. ODBC (Open Database Connectivity) and OLEDB
(Object Linking and Embedding Database) are provided
enabling support for .Net applications and tools.
JDBC is a standard Java interface for connecting to
relational databases from J2EE applications. The supplied
JDBC driver complies with the SQL92 Entry Level
standard.
ODBC is a standard interface for connecting to relational
databases from .Net applications. OLEDB is a set of
Component Object Model (COM) interfaces providing
applications with uniform access to data stored in diverse
information sources. Like ODBC, OLEDB can be used for
connecting to relational databases from .Net applications.
The ODBC driver and OLEDB Provider supplied with Data
Access for SUPRA comply with the SQL92 Entry Level
standard.
Reports
To design and produce reports from query results, Data
Access for SUPRA supplies JasperReports (a reporting
framework) to help you design and deploy reports without
programming. Reports can be delivered in a variety of
formats such as PDF, HTML, XLS, CSV and XML files.
JasperReports formats data retrieved from a relational
database through JDBC according to the report design
defined in an XML file. The report design format provides
a number of options such as:
• Using input parameters to drive report output. For
example, input parameters can be used to change the
columns used in an MDX query.
• Defining the layout of the report using sections and
frames.
• Defining how data is grouped.
• Presenting data in charts such as bar charts, pie charts
and line graphs.
To create a report, you can develop the report design XML
file or you can use iReport to visually develop the report.
Using iReport, you can easily define data sources to use
for relational data, how to format extracted fields, how to
structure the report and how to present data in charts.
As an example, in an inventory database, information may
be kept for part costs, and various parts might be used by
different projects. A report could be developed to
organize part cost by project to evaluate project cost. The
report could show:
• The parts used by each project
• For each part, the planned cost and current cost
Using iReport, you can design a JasperReport that
provides the summary of each part cost for a given project
on a separate page. You could produce this in a PDF
format as in the following example.
4
Data Warehouse for SUPRA
Data Warehouse for SUPRA provides a data warehousing
solution optimized to SUPRA PDM. Building a data
warehouse allows you to structure the data toward your
business intelligence objectives. This provides a number of
advantages over direct access to data.
• Data structures optimized for operational systems can
be complex. A data warehouse allows you to restructure
data into a schema that is easier for analysts to
understand.
• Data can be combined from several sources into a
unified relational schema.
• Data can be cleansed to meet the needs of analysis or
changing standards.
• The warehouse data can be built incrementally to
provide a historical basis for analysis.
• There may be less resource usage on operational
systems as data is migrated to the data warehouse.
Data Warehouse for SUPRA provides the ExtractTransform-Load components to help you develop and
automate the construction and updating of your
warehouse data. Data can be directly extracted using Data
Access for SUPRA or it can be extracted using SUPRA
utilities. Data Warehouse for SUPRA supplies a relational
database with support for developing J2EE and .Net
applications. A reporting framework is also provided to
allow you to design and deploy reports without the need
to program. This is shown in the following image.
J2EE and
.Net
Apps
Jasper
Reports
Data Warehouse for SUPRA
The Extract Transform Load (ETL) process provides the
data basis for building your warehouse solution. The
process extracts data from operational host systems and
conforms data from disparate sources into a shape that is
suitable for analysis.
Data Warehouse for SUPRA provides an ETL tool to help
you develop and automate the construction and updating
of your warehouse data. Facilities are provided to:
• Reshape your host data into the physical schema you
design for the warehouse database.
• Conform heterogeneous data from multiple sources.
• Cleanse data to enforce your business rules.
• Handle large-volume initial loads and incremental
updates.
The ETL tool supports your design of the warehouse
physical schema by providing the transformation steps
commonly needed to reshape host data. In a data
warehouse, the physical schema is optimized to provide
efficient analysis. That is, the data is structured to allow
fast analytic queries where the emphasis is on grouping
and aggregating data.
The host data used in your data warehouse solution may
come from multiple, disparate data sources, which may
not conform to the same business rules. And the
extraction techniques may vary from one data source to
another. Your data warehouse may be required to provide
a cohesive data model that unifies the disparate data
sources in your enterprise. Data warehouse designs refer
to such an integrated model as having “conformed”
dimensions and “conformed” measures. The ETL tool
supports the integration of data by the following:
• Processes are provided to extract data directly from
relational sources or from text files. For text files, a
variety of format options are available.
• Data conforming processes are available to support your
business rules. For example, look-up tables can be used
to substitute values to conform to a standard.
Data
Warehouse
• Data joining processes are provided to help match and
join together different data sources.
ETL
Host data may not have the accuracy needed for analysis.
That is, some data sources may be incorrect, ambiguous
or inconsistent. The design of your data warehouse may
require the ETL process to cleanse the data by removing
or correcting data that does not meet certain rules. The
ETL tool provides processes that allow you to check
various attributes of your data and filter out data or
conform the data to a standard.
Data
Access for
SUPRA
SUPRA
ETL
Host
Data
5
As the volume of data rises, the scalability of your ETL tool
must meet your performance objectives. The Data
Warehouse for SUPRA ETL tool provides an architecture
that scales with increasing volumes. This applies not only
to the initial loading of your data warehouse but also to
the ongoing incremental updates.
Extracting Data with SUPRA Utilities
Mainframe
Data
Warehouse
Extract Options
Text files
A variety of options are available to extract host data for
the warehouse.
The Data Warehouse for the SUPRA ETL component
integrates with Data Access for SUPRA to allow you to
directly extract data, as shown in the following figure.
Direct SUPRA Extract
ETL Tool
Data
Warehouse
SUPRA
Data
Access
for
SUPRA
SUPRA
Data Access for SUPRA allows the ETL tool to access
SUPRA PDM files as standard SQL tables. The ability to
use the SQL language when extracting data from a host
system can be a powerful advantage in the development
of a data warehouse. During the extraction, data can be
joined together using the navigation strategies of SUPRA.
Also, platform-specific data types are converted to ASCII
text, which is suitable for loading a data warehouse. Also,
data can be further converted and “re-shaped” using SQL
in the extraction process. This can greatly simplify and
speed up the ETL process.
Data Warehouse for SUPRA also provides a mainframe
extract utility to unload SUPRA PDM data into flat files.
This can be useful for very large extracts where direct
access to SUPRA PDM across the network may not be
practical. This is shown in the following figure:
ETL Tool
(Transform
and load)
SUPRA
Utilities
(Extract)
This provides some of the same advantages of extracting
data with Data Access for SUPRA, including the conversion
of platform-specific data types to ASCII text and a limited
join capability. However, the full power of the SQL
language is not available for complex joins and data
conversions.
The ETL component also allows you to extract from other
host sources and integrate the data with your SUPRA data.
You can:
• directly extract data from databases supporting JDBC,
• extract data from flat files allowing you to use data from
any source that can unload data to comma-separated
value files (CSV), and
• extract data from Excel spreadsheets and XML files.
6
ETL Steps
ETL Jobs
A graphical tool is provided to help you construct and test
transformation graphs. These graphs consist of connected
steps categorized as “input,” “transformation” or
“output” steps.
A graphical tool is also available to help you construct and
test ETL jobs. An ETL job is a set of connected job steps
used to run scripts and transformation graphs, test for
conditions and send e-mail alerts.
For example, the following graph is used to show the
loading of a client dimension table from two data sources.
For example, the following ETL job might be used to run
the previously described transformation graph for loading
the client dimension.
In the example, the client dimension consists of a
hierarchy of client and sales region information. The ETL
process needs to join these two sources together and
correct some problems in the process.
1. Client information is read from a client database table,
and sales region information is read from a text file.
2. The client information is changed to conform to a
different naming standard used for sales regions based
on a corrections look-up file.
3. Sales region information is checked for the existence of
a sales representative. Any error rows are sent to an
error file.
4.The client information and sales region information is
joined together based on the sales region name.
5. The joined information is written to the client dimension
table in the warehouse database.
Input steps are available to read text files, database tables,
XML files or Excel files. A number of transformation steps
are available for joining, filtering, grouping, merging,
sorting, etc. Transformation steps are also available to help
with common data warehouse needs such as surrogate
keys and slowly changing dimensions.
The sales region file is transferred from another system
using FTP before running the transformation to load the
client dimension. Also, a test is made to see if the
transformation produced an error file. If an error file was
produced, or if any of the previous steps fail, an e-mail
alert is sent.
Job steps are provided to execute SQL statements and
run scripts, transformation graphs, FTP files, etc. You can
also test for various conditions.
Warehouse Database
Data Warehouse for SUPRA embeds PostgreSQL as the
warehouse database. PostgreSQL is an open source
database with a strong reputation for reliability, data
integrity and performance. PostgreSQL’s ease of
administration and deployment make it an ideal choice for
an embedded database server. However, Data Warehouse
for SUPRA does not require PostgreSQL and allows you
the flexibility of using other relational databases.
Client Applications and Reports
As with Data Access for SUPRA, you can use the
JasperReports framework to produce reports from your
warehouse data without programming. You can also use
JDBC to enable support for J2EE applications and tools,
and ODBC and OLEDB to enable support for .Net
applications and tools.
7
Dimensional Data
Analytics for SUPRA
Analytics for SUPRA enables multi-dimensional analysis of
information from your operational SUPRA databases. This
information is expressed in business measures that can
reveal product performance, profit trends or productivity
comparisons. Multi-dimensional analysis not only allows
you to quickly reveal business performance and trends, it
also allows you to explore new analysis areas. Ad hoc
analysis can let you reveal trends and performance
measures that would remain hidden if traditional querying
and reporting were used.
Analytics for SUPRA builds on advantages provided by
Data Warehouse for SUPRA and Data Access for SUPRA.
An analytic server is provided allowing you to organize
data using the measures and dimensions that are
important to business intelligence objectives.
Components are provided to allow the easy development
of analytic applications. A reporting framework is provided
to allow you to design and produce reports using analytic
data. Also, tools are provided to help you develop analytic
queries and to perform ad hoc analysis of data. This is
shown in the following image.
Analytic Reports
Analytic Applications
Analytic Tools
In multi-dimensional analysis, the data items to be
examined are referred to as measures. The measures are
described or categorized by dimensions. These are usually
organized into a particular domain of inquiry as a “sales
performance” or “client purchases.” The basic unit of
organizing and storing the dimensions and the measures
they contain is an OLAP cube.
As an example, you might be interested in an inventory
analysis – measuring inventory quantity by location. This
can be pictured as a two-dimensional cube as shown in
the following:
Chicago, Bldg 1 New York, Bldg 3 Phoenix, Bldg 2
Rudder
20
0
Wing flap
0
300
4
0
Engine mount
24
0
50
Prop
1
8
5
Wing support
0
222
0
The cells of the cube contain the Quantity measure while
the rows and columns represent the part and location
dimensions.
You could continue to define dimensions for the cube. For
example, you may want to measure inventory levels over
time and define a time dimension. This can be pictured as
a three-dimensional cube:
Time
Chicago, Bldg 1 New York, Bldg 3 Phoenix, Bldg 2
Analytics for SUPRA
Analysis
Server
Data Warehouse for
SUPRA
Data Access for
SUPRA
SUPRA
Rudder
20
0
Wing flap
0
300
4
0
Engine mount
24
0
50
Prop
1
8
5
Wing support
0
222
0
8
You define a cube to the analytic server by using a cube
schema definition. This schema defines the measures and
dimensions and how they are stored in the warehouse
database. To create and maintain these definitions, a
Cube Schema Builder tool is provided. For example, the
Cube Schema Builder might display the definition of the
above “inventory” cube as the following:
The cube schema definitions allow the analytic server to
map dimensions and measures in the cube to tables and
columns in the warehouse database. The cube schema
definitions also provide a cache organization for holding
computed aggregations in memory so subsequent queries
can access values without going to the warehouse
database.
In an analytic solution, the performance of grouping and
aggregating data is critical to success. For very large
volumes of data, the aggregates should be pre-computed
when the warehouse database is loaded. Special
“aggregate tables” can be constructed so that aggregates
are stored and do not need to be computed by the
analytic server. For aggregates that must be computed by
the analytic server, the cache is used to hold results.
The analytic server has a number of features for
developing your logical data model:
• Multiple hierarchies can be defined for dimensions. For
example, the Time dimension could be defined as a
calendar hierarchy: year, month and week. It can also be
defined as a fiscal calendar: fiscal year or fiscal quarter.
The Inventory cube schema contains the Quantity measure
and the dimensions: Time, Part and Location. The
definition of the Quantity measure is shown. The Quantity
measure is aggregated over dimensions as a sum. The
measure is stored in a fact table in the column
“qty_on_hand.” Also, formatting options are available to
format the measure in analytic query results.
The analytic server uses the cube schema definition to
provide an organization for storing data in a memory
cache. This is shown in the following diagram:
Application
Server
cache
Warehouse
Analytic
Server
WH
Database
Cube
schema
• Cubes can be mapped to warehouse databases with a
“star” schema or a “snowflake” schema.
• Measures can be created with user-defined formulas.
• Aggregate tables in the warehouse database schema
can be used to improve performance.
• Security can be defined for users to control access to
analytic data.
9
Developing Multi-dimensional Applications
Application Programming Interfaces
Multi-dimensional applications allow analysis of multidimensional data where information is presented in a form
that can immediately be understood by users. This is
usually in a tabular or graphical form where more detailed
information can be obtained by drill-down or breakdown
lists. Applications are typically time-oriented to reveal past
trends and patterns. These applications are often referred
to as Online Analytical Processing (OLAP) applications.
Analytics for SUPRA provides several application
programming interfaces to communicate requests to the
analytic server.
OLAP applications use the Multi-dimensional Expression
language (MDX) to manipulate multi-dimensional data.
MDX is oriented to analysis queries as it allows multiple
dimensions, hierarchies of dimensions and aggregation of
measures. Using this language, queries can be requested
from an analytic server to provide measures that are
organized and summarized by dimensions.
You can develop analytic web applications using
application programming interfaces to communicate MDX
requests to the analytic server. You can also use GUI
components that accept MDX queries and render visual
components such as charts and pivot tables.
Applications can communicate MDX requests to an
analytic server using XML for Analysis. XML for Analysis
(XMLA) is a standard that allows clients to talk to analytic
servers using Web Services. This allows opens access from
a variety of platforms and languages to multi-dimensional
data servers. Client requestor components are also
supplied so that client applications can use XMLA without
a detailed understanding of the Web Service protocol and
technologies. This is shown in the following diagram:
Application
Server
Userwritten
XMLA
Requestor
soap
XMLA
Responder
Analytic
Server
OLAP
Application
For producing reports from MDX query results, you can
use reporting frameworks to design and produce OLAP
reports.
You can also use multi-dimensional query tools to explore
dimensions and measures and to design and test MDX
queries.
Multi-dimensional Applications and Tools
Analytic Reports
Analytic Web
Applications
Analytic Tools
MDX
Analytic
Queries
Java programs can also use the Java for OLAP (JOLAP)
programming interface. JOLAP is a J2EE objectedoriented interface to analytic servers. Data Warehouse for
SUPRA provides an implementation of the interfaces. For
example, you might use JOLAP to implement an OLAP
servlet application as shown in the following diagram.
Application
Server
Application
Server
Analytic
Server
Web
Browser
http
Servlet
App
JOLAP
Analytic
Server
10
Visual OLAP Components
Components are also provided to visually present analytic
query results in the development of Java Server Pages
applications. These include a:
• Navigator component to explore the dimensions and
measures defined in a cube.
• Pivot table component to display analytic query results
in a tabular form. The component allows slicing, dicing,
drilling down and rolling up.
• Chart component to display analytic query results in a
variety of formats. These include bar charts, line graphs,
pie charts, etc.
A Java Server Pages Tag library is provided to help you
easily construct server pages to render visual components
and to change options for components.
As an example, your data warehouse may provide an
inventory analysis cube. The cube could provide
measurements for inventory quantities and inventory costs.
The dimensions that are important for the analysis could
include part names, part locations and year time periods.
To understand the use of part storage space at different
locations, you could design a query that shows:
• Part quantity measurement where the aggregation is the
maximum
• Part quantity broken down by the location dimension
• Part quantity that is further broken down by time periods
You could use the visual components to construct a Java
Server Page to request your query and present a bar chart
and pivot table as in the following image.
For users who are familiar with OLAP technology, a
navigator component can be used to change measures
and add or remove dimensions. Also, other visual
components can be presented to change the properties
of charts and pivot tables. Toolbars can be presented to
choose visual components. For example, you could use
the visual components to construct a Java Server Page to
present a navigator component and other options as a
toolbar. The navigator can be used to add or remove
dimensions to the inventory space analysis query and
present a bar chart and pivot table as in the following
image.
11
Multi-dimensional Reports
To design and produce reports from MDX query results,
you can use JasperReports. This allows you to use the
dimensions and measures from analytic queries to
produce reports in a variety of formats. You can also use
iReport to help you visually design JasperReports. Using
iReport, you can easily define data sources to use for
analytic data, how to format extracted fields, how to
structure the report and how to present data in charts.
Microsoft Excel Support
Microsoft Excel is an established tool for business analysis
and reporting. You can use Microsoft Excel with Analytics
for SUPRA through third-party integration software. Excel
allows you to dynamically create Excel Pivot Tables and
charts using drag-and-drop operations. This allows you to
explore and perform ad hoc analysis without knowledge of
multi-dimensional query languages. Drill-down and
summary operations are provided to further explore and
understand trends.
For example, in an inventory analysis, you might be
interested in comparing the current and planned part
costs for a project. You would also like to see the cost
comparisons over time. With Excel, you can design a
clustered column chart dragging measures and
dimensions from a field list to the chart. The chart shows
summary costs for past years and monthly costs for the
current year.
12
Glossary
Aggregate
For example, when viewing sales data for North America,
a drill-down operation in the Region dimension would
then display Canada, the eastern United States and the
western United States. A further drill-down on Canada
might display Toronto, Vancouver, Montreal, etc.
See Dimension Hierarchy.
Cube
A cube is an array of data cells arranged by dimensions.
The data cells contain the measures or summary of the
measures. For example, a spreadsheet is a twodimensional array with the data cells arranged by rows and
columns where the rows and columns represent
dimensions.
Dimension
A dimension is a structural attribute of a cube that is a list
of members, all of which are of a similar type in the user's
perception of the data. For example, all months, quarters,
years, etc., make up a time dimension; likewise all cities,
regions, countries, etc., make up a geography dimension.
A dimension acts as an index for identifying values within
a multi-dimensional array. If one member of the dimension
is selected, then the remaining dimensions in which a
range of members (or all members) are selected defines a
sub-cube. If all but two dimensions have a single member
selected, the remaining two dimensions define a
spreadsheet (or a "slice" or a "page"). If all dimensions
have a single member selected, then a single cell is
defined. Dimensions offer a very concise, intuitive way of
organizing and selecting data for retrieval, exploration and
analysis.
Dimension Hierarchy
Dimensions can be organized with parent-child
relationships. The parent represents an aggregation of its
children. For example, a time dimension might be
organized in a hierarchy of year and month. The data for
year might be an aggregation of its children (months). This
aggregation is typically a sum but can be more complex
such as an average. The aggregation is sometimes
referred to as a “roll-up” of data from children.
Drill Down/Up
Drilling down or up is a specific analytical technique
whereby the user navigates among levels of data ranging
from the most summarized (up) to the most detailed
(down). The drilling paths may be defined by the
hierarchies within dimensions or other relationships that
may be dynamic within or between dimensions.
Fact
See Measure.
Measure
Measures, also referred to as facts, are data to be
analyzed or examined. Measures are numeric and are
usually additive.
Multi-dimensional Array
See Cube.
ODBC
ODBC, short for Open Database Connectivity, is a
database access method for SQL databases.
OLAP
Online Analytical Processing designates a category of
applications and technologies that allow the management
of multi-dimensional data, with the goal of analysis. This is
often used in sales and marketing analysis to study the
volume of sales by products, location, time, etc. It is also
used in decision support to forecast changes in income,
expense and profit and in quality of service analysis.
OLEDB
OLEDB (Object Linking and Embedding Database) is an
API designed by Microsoft for accessing different types of
data stores in a uniform manner. It is a set of interfaces
implemented using the Component Object Model (COM);
it is otherwise unrelated to OLE. It was designed as a
higher-level replacement for, and successor to, ODBC,
extending its feature set to support a wider variety of nonrelational databases, such as object databases and
spreadsheets that do not necessarily implement SQL.
OLTP
OLTP (online transaction processing) is a classification of
programs that manage transactions. Data entry and data
retrieval applications are examples.
13
Pivot Table
Star Schema
A pivot table is a tool that allows you to visualize and
explore the results of an analytic query. The results are
presented as a spreadsheet of measures organized by
rows and columns that represent dimensions. Pivot tables
allow slicing and dicing, rotating and roll-up operations.
A star schema is an organization of tables in a relational
database optimized for OLAP. In the center is a fact table,
whose columns contain the multi-dimensional measures.
The branches of the star consist of dimension tables where
each table contains the hierarchy information for a
dimension. The dimension tables are linked to the fact
table through foreign key relationships.
Roll Up
See Dimension Hierarchy.
Rotate
To change the dimensional orientation of a report or page
display. For example, rotating may consist of swapping the
rows and columns, moving one of the row dimensions into
the column dimension, or swapping an off-spreadsheet
dimension with one of the dimensions in the page display
(either to become one of the new rows or columns), etc. A
specific example of the first case would be taking a report
that has Time across (the columns) and Products down
(the rows) and rotating it into a report that has Product
across and Time down.
An example of the second case would be to change a
report that has Measures and Products down and Time
across into a report with Measures down and Time over
Products across. An example of the third case would be
taking a report that has Time across and Product down
and changing it into a report that has Time across and
Geography down.
Slice and Dice
The user-initiated process of navigating by calling for page
displays interactively, through the specification of slices via
rotations and drill down/up.
Slowly Changing Dimensions
Slowly changing dimensions are dimensions that change
over time. A variety of techniques can be used to update
dimensions in a warehouse database and to record
versioning information for changes.
Snowflake Schema
A snowflake schema is a variation of a star schema where
the information for a dimension is normalized into multiple
tables. This is usually used for very large dimensions.
Surrogate Keys
Surrogate keys are primary keys that are substituted for
the natural key of a table. A surrogate key is usually an
integer and can help in the performance and updating of
a warehouse database.
Cincom, the Quadrant Logo, SUPRA, and Simplification Through Innovation are
registered trademarks of Cincom Systems, Inc. All other trademarks belong to
their respective companies.
© 2007 Cincom Systems, Inc.
FORM DB060822-9 3/07
Printed in U.S.A.
All Rights Reserved
World Headquarters • Cincinnati, OH USA • US 1-800-2CINCOM
Fax 1-513-612-2000 • International 1-513-612-2769
E-mail [email protected] • http://www.cincom.com