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Unformatted text preview: Business Intelligence and Tools Glossary Glossary
Append: The append process unconditionally adds the incoming data,
preserving the existing data in the target table. When an incoming record is
a duplicate of an existing record, you can define the process, either to allow
or reject the incoming record.
Attributes: Attributes describe the characteristics of properties of the
Bottom-Up Approach: The objective of bottom-up approach is to deliver
business value by deploying the dimensional data marts at the earliest.
Unlike the top-down approach, the data marts in this approach contain all
the data (both atomic and summary) that users may want. The data is
modeled in a star schema design to optimize usability and query
Business Analyzing phase: This is one of the phases in the phased
Enterprise data modeling. This provides a means for further defining of the
concepts provided in the information planning phase. This phase is
described in business terms to make business people understand the data
details without any special training. The purpose of the phase is to gather
and arrange the business requirements and define the business terms.
Business Area Analysis (BAA): This is one of the tiers in the phased
enterprise data modeling as proposed by IBM in Worldwide Solution Design
and Delivery Method.
Business Dimensional Lifecycle: This is a methodology adopted for
planning, designing, implementing and maintaining the BI system. Sikkim Manipal University Page No. 243 Business Intelligence and Tools Glossary Business Intelligence (BI): A generic term used to describe leveraging the
organizational internal and external data, information for making the best
possible business decisions.
Business Objects: This is a popular suite that has a set of business
Business System Design (BSD): This is one of the tiers in the phased
enterprise data modeling as proposed by IBM in Worldwide Solution Design
and Delivery Method.
Business System Implementation (BSI): This is one of the tiers in the
phased enterprise data modeling as proposed by IBM in Worldwide Solution
Design and Delivery Method.
Business System Maintenance (BSM): This is one of the tiers in the
phased enterprise data modeling as proposed by IBM in Worldwide Solution
Design and Delivery Method.
By-Product Method: This is a method employed to determine the
information needs of the senior executives in an organization. Under this
method, various informational by-products of the current operations of the
organization are summarized and aggregated through use of the traditional
TPSs and other MISs that are being used in the organization.
Capital costs: These are the costs that are associated with acquisition of a
Capture based on Date and Time Stamp: In this method, every source
record created or updated is marked with a stamp that shows the date and
time. The data capture occurs at a later time after the creation or updation of
a source record and the time stamp provides the basis for selecting the
records for data extraction. Sikkim Manipal University Page No. 244 Business Intelligence and Tools Glossary Capture by Comparing Files: According to this method, you capture the
changes to your product data by comparing the current source data with the
previous captured data.
Capture in Source Applications: In this method, the source application is
made to assist in the data capture for the data ware house. Here, you need
to modify the relevant application programs that write to the source files and
Capture through Database Triggers: The database triggers are specially
stored procedures or programs stored on the database and are fired when a
pre-defined event occurs.
Capture through Transaction Logs: In this method, the transaction logs of
the DBMSs are used to capture the data. Whenever there is an updation in
the database table, the DBMS writes entries on the log file.
Cardinality: It represents the maximum number of instances of one entity
that are related to a single instance in another table and vice versa. Thus
the possible cardinalities include one-to-one (1:1), one-to-many (1:M), and
many-to-many (M:M). In a detailed normalized ER model, any M:M
relationship is not shown because it is resolved to an associative entity.
Classification: In this approach, the data mining processes are intended to
discover rules that define whether an event belongs to a particular subset or
class of data. This category of techniques is most applicable to different
types of business problems and the technique involves two sub-processes;
predicting classifications and building a model.
Cluster Analysis: Clustering methods can be used to create partitions so
that all members of each set are similar according to a set of metrics. A
cluster is simply a set of objects grouped together by virtue of their similarity
to each other. Sikkim Manipal University Page No. 245 Business Intelligence and Tools Glossary Cognos: Cognos is a rich set of tools for development of data mines, data
marts and data warehouses
Constructive Merge: If the primary key of an incoming record matches with
the key of an existing record, it leaves the existing record, adds the
incoming record and marks it as superceding the old record.
Consultant: An individual who has experience and expertise in applying
tools and techniques to resolve process problems and who can advise and
facilitate an organization's improvement efforts.
Conversion: This is a data transformation task that includes a large variety
of rudimentary conversions of single fields. This task is done for two
reasons; to standardize the data among the data extractions from disparate
source systems, to make the fields usable and understandable to the users.
Critical Success Factors (CSF) Method: This is a method employed to
determine the information needs of the senior executives in an organization.
Under this approach, the critical success factors of an organization are
identified. The CSFs are those things that must be done right if the
organization wants to be successful. Similar to the key indicators method,
this method requires gathering of the information on the identified CSFs and
the information is supplied to the top executives.
Current Value: The current value is the stored value of an attribute at that
moment of time. These values are transient and change as and when
business transactions happen.
Dashboard: This is a reporting tool that consolidates aggregates and
arranges measurements, metrics (measurements compared to a goal) on a
single screen so that information can be monitored at a glance.
Data integration: This includes combining of all relevant operational data
into coherent data structures so as to make them ready for loading into data
Sikkim Manipal University Page No. 246 Business Intelligence and Tools Glossary warehouse. It standardizes the names and data representations and
resolves the discrepancies.
Data Loading: After the creation of load images, the next set of activities is
to take the prepared data, apply it to the data warehouse, and store it in the
data warehouse database. Here, the data warehouse will be offline during
Data Management: This is the process of controlling, protecting, and
facilitating access to data in order to provide the end users with timely
access to the data they need.
Data mart: It is a physical and logical subset of an Enterprise data
warehouse and is also termed as a department-specific data warehouse.
Generally, data marts are organized around a single business process.
Data mining (DM): This is the set of activities used to find new, hidden, or
unexpected patterns in data. It is the process of analyzing data from
different perspectives and summarizing it into the useful information.
Data Mining (or Data Surfing): This is a technique geared for the user who
typically does not know exactly what he is searching for, but is looking for
particular patterns or trends. Data mining is the process of sifting through
large amounts of data to produce data content relationships. It can predict
future trends and behaviors, allowing businesses to make proactive,
knowledge-driven decisions. The most valuable results from data mining
include clustering, classifying, and estimating the things that occur together.
There are many kinds of tools that play a role in data mining and they
include neural networks, decision trees, visualization, general algorithms,
fuzzy logic, etc.
Data model: This is a well-organized abstraction of the data. Sikkim Manipal University Page No. 247 Business Intelligence and Tools Glossary Data Modeler: An individual in a BI project who responsible for taking the
data structure that exists in the enterprise and model it into a schema that is
suitable for OLAP analysis.
Data Modeling: A method used to define and analyze data requirements
needed to support the business functions of an organization.
Data of revisions (also known as incremental data capture): This includes
the revisions since the last time data was captured. If the source data is
transient, the capture of the revisions is a difficult exercise.
Data Partitioning: The term ‘partition’ refers to the physical status of a data
structure that has been divided into two or more separate structures. But
logical partitioning of the data is also required to better understand and use
the data. In such a case, the logical partitioning overlaps with the physical
Data Profiling: Data Profiling is a critical step in the data migration that
automates the identification of problematic data and metadata, and enables
organizations correct inconsistencies, redundancies and inaccuracies in
Data visualization is the process by which numerical data are converted
into meaningful images. Here, the data may come from any type of sources
viz., satellite photos, undersea sonic measurements, surveys, or computer
Data Visualization: Data visualization involves examining the data
represented by dynamic images rather than pure numbers. These are the
techniques that turn the data into information by using the high capacity of
the human brain to visually recognize patterns and trends.
Data warehouse: A subject-oriented, integrated, non-volatile, time-variant
collection of data designed to support the decision-making requirements of
Sikkim Manipal University Page No. 248 Business Intelligence and Tools Glossary Data: A set of collected facts.
Database Administrator (DBA): An individual in a BI project who keeps the
database available for the applications run smoothly and also involves in
planning and executing a backup/recovery plan, as well as performance
DataStage: DataStage provides a set of powerful tools for developing a
Decentralized Warehouse: A remote data source that users can
query/access via a central gateway that provides a logical view of corporate
data in terms that users can understand. The gateway parses and
distributes queries in real time to remote data sources and returns result
sets back to users
Decision trees: This technique offers a conceptually simple mathematical
method of following the effect of each event, or decision, on successive
Deduplication: Some companies may maintain several records for a single
customer and so duplicates are the result of the additional records.
Therefore, it is suggested to keep a single record for one customer and link
all the duplicates in the source systems to this single record in your data
warehouse. This process is called deduplication.
Deferred Data Extraction: All the methods in the immediate data extraction
involve the real-time data capture. In contrast, these deferred data
extraction methods do not capture the changes in real time and does the
same in later period.
Derived Data: This is the data that has been derived or created perhaps by
aggregating or averaging the real-time data through a defined process. This
data can represent a view of the business at a specific point of time or can
be a historical record of the business over a period of time. Reconciled Data
Sikkim Manipal University Page No. 249 Business Intelligence and Tools Glossary Destructive Merge: W hen you apply the incoming data to the target data,
the destructive merge process updates target record, if the primary key of
an incoming record matches with the key of an existing record. The
incoming record simply gets added to the target table, if the incoming record
is a new record.
Detailed raw data: This is the lowest level of detailed transaction data
available within a data warehouse or a data mart without any aggregation or
Dimension: A dimension is a collection of members or units of the same
type of views. Usually, it is represented by an axis. In a dimensional model,
every data point in the fact table is associated with one and only one
member from each of the multiple dimensions.
Dimensional modeling: This is one of the data modeling techniques. It
uses three basic concepts; Facts, Dimensions and Measures. Dimensional
modeling is powerful in representing the requirements of the business user
in the context of database tables and also in the area of data warehousing.
DOLAP: This stands for desktop online analytical processing. DOLAP is a
variation of ROLAP.
Domain: A domain consists of all the possible acceptable values and
categories that are allowed for an attribute. It is the set of all real possible
Drill-down: This is the capability to browse the information through a
Enrichment: This is a data transformation task that involves the
rearrangement and simplification of individual fields to make them useful for
the data warehouse environment. Sikkim Manipal University Page No. 250 Business Intelligence and Tools Glossary Enterprise Data Model: This is an approach to develop a data warehouse
data model. An EDM is a consistent definition of all the data elements
common to the business, from a high-level business view to a generic
logical data design. Using this model, you can derive the general scope and
understanding of the business requirements and the model also includes
links to the physical data designs of the individual applications.
Enterprise data warehouse: It consists of the data drawn from multiple
operational systems of an organization. This data warehouse supports timeseries and trend analysis across different business areas of an organization
and so can be used for strategic decision-making.
Enterprise Information System (EIS): This is an information interface
system that is specially designed to facilitate the analysis of critical
information for operating an organization. These systems provide tools that
support the strategic decision making needs of the top executives of the
Entity: An entity is defined to be a person, place, thing, or event of interest
to the business or the organization. It represents a class of objects, which
are things in the real business world that can be observed and classified by
their properties and characteristics. In general, an entity has its own
business definition and a clear boundary definition that is required to
describe what is included and what is not.
ER modeling: This is one of the data modeling techniques. It produces a
data model of the specific area of interest, using two basic concepts; Entities
and the Relationships between them. A detailed ER model may also contain
attributes, which can be properties of either the entities or the relationships.
The ER model is an abstraction tool as it can be used to simplify,
understand and analyze the ambiguous data relationships in the real
Sikkim Manipal University Page No. 251 Business Intelligence and Tools Glossary ETL Developer: An individual in a BI project who involves in planning,
developing, and deploying the extraction, transformation, and loading
routine for the data warehouse from the legacy systems.
External Data Source: This is the data that is not available in the OLTP
systems, but is required to enhance the information quality in the data
warehouse. The examples of this data include the data of the competitors,
information of the regulatory and government bodies, research data of the
professional bodies and universities.
Fact: A f act is a collection of related data items, consisting of measures and
context data. A fact represents a business item, a business transaction, or
an event that can be used to analyze the business or a business process.
Format Revisions: Format revisions include changes to the data types and
lengths of individual fields. For instance, product package types in your
source systems may be indicated by codes and names in which the fields
are numeric and text data types.
Front End Developer: An individual in a BI project who develops the frontend, whether it be client-server or over the web.
Full refresh: This involves complete erasing the contents of one or more
tables and reloading with fresh data (initial load is afresh of all the tables)
Granularity: This refers to the level of details of the data provided in a data
warehouse or a data mart. A typical data warehouse will have some tables
in it that have a lot of detail and have other tables that are summarized or
aggregated, which means less detail. The more detail data that is available,
the lower the level of granularity. Sikkim Manipal University Page No. 252 Business Intelligence and Tools Glossary I
Immediate Data Extraction: The immediate data extraction is a real-time
Improvement: The positive effect of a process change effort.
Increment Load: This involves applying ongoing changes as necessary in a
Incremental improvement: Improvements that are implemented on a
Informatica: This is a popular ETL tool in the market and this suite consists
of five components and provides the complete business intelligence
Information planning phase: This is one of the phases in the phased
Enterprise data modeling. This phase provides the highly consolidated view
of the business wherein you can view the business concepts. These
business concepts can be categorized into business entity, super entity, or
subject area and each of these items maintain related data elements.
Information System Planning (ISP): This is one of the tiers in the phased
enterprise data modeling as proposed by IBM in Worldwide Solution Design
and Delivery Method.
Initial Load: This that involves populating all the data warehouse tables the
Integrated Data Warehouse: This is an earlier development phase of a
data warehouse. Data warehouses at this stage are used to generate
activity or transactions that are passed back into the operational systems for
use in the daily activity of the organization.
Intrinsic data quality: This represents the accuracy of the data. It is the
degree to which data accurately reflects the real-world object that the data
Sikkim Manipal University Page No. 253 Business Intelligence and Tools Glossary K
Key Indicator Method: This is a method employed to determine the
information needs of the senior executives in an organization. In this
method, the top executives monitor only that information where the
information is out-of-normal condition. Whenever such a condition happens,
the top executives may gather further information for making decisions
intended to correct the condition.
Knowledge: Knowledge is part of the hierarchy made up of data,
information and knowledge. Data are raw facts. Information is data with
context and perspective. Knowledge is information with guidance for action.
Linkage Analysis: The data mining techniques that employ linkage
analysis (associations) search all details or transactions from operational
systems for patterns with a high probability of repetition.
Logical Data Modeling phase: This is one of the phases in the phased
Enterprise data modeling. This phase comes into the picture after business
analyzing phase and it consists of several hundred entities and contains the
identification and definition of all entities, relationships and attributes. The
entities of the logical data model can be further portioned into views by
subject areas or by applications. This phase can be divided into two types;
‘Generic logical data model’ for the organizational level and ‘Logical
application model’ for the application level of data view.
Machine learning: These techniques, such as genetic algorithms and fuzzy
logic, can derive meaning from complicated and imprecise data and can
extract patterns from and detect trends within the data that are far too
complex to be noticed by either human brain or more conventional
automated analysis techniques.
Sikkim Manipal University Page No. 254 Business Intelligence and Tools Glossary Manager: An individual charged with the responsibility for managing
resources and processes.
Measure: A measure is a numeric attribute of a fact, representing the
performance or behavior of the business relative to the dimensions.
Metadata: Metadata is data about data. The examples of metadata include
data element descriptions, data type descriptions, attribute descriptions, and
Metadata: This refers to "data about data." It is the information that
describes, or supplements the main data in a data warehouse or in a data
Metric: A standard for measurement.
Mission: An organization's purpose.
MOLAP Model: This is the more traditional way of OLAP analysis. In
MOLAP, data is stored in a multi-dimensional cube. The storage is not in the
relational database, but in proprietary formats.
Neural networks: This technique attempts to mirror the way the human
brain works in recognizing patterns by developing mathematical structures
with the ability to learn. By studying combinations of variables and how
different combinations affect datasets, these networks develop nonlinear
Normalization: This is a process of assigning the attributes to entities in a
way to reduces data redundancy, avoid data anomalies, provide a solid
architecture for updating data, and reinforce the long-term integrity of the
Null Method: This is a method employed to determine the information
needs of the senior executives in an organization. This method assumes
Sikkim Manipal University Page No. 255 Business Intelligence and Tools Glossary that the information needs of senior executives are so dynamic and fluid that
the pre-defined reports generated by the typical information systems are not
Offline Data Warehouse: This is an earlier development phase of a data
warehouse. Data warehouses in this stage are updated on a regular basis
(usually daily, weekly or monthly) from the operational systems and the data
is stored in an integrated reporting- oriented data structure.
Offline Operational Databases: This is an earlier development phase of a
data warehouse. During this stage, data warehouses are developed by
simply copying the database of an operational system to an off-line server
where the processing load of reporting does not impact on the operational
OLAP Developer: An individual in a BI project who develops the OLAP
On-Line Analytical Processing (OLAP): This is a category of software
technology that enables the users gain insight into data through fast,
consistent, interactive access to a wide variety of possible views of
information that has been transformed from raw data to reflect the real
dimensionality of the organization. This is implemented in a multi-user
client/server mode and offers consistently rapid response to queries,
regardless of database size and complexity. This software is also called
Multidimensional Analysis Software.
On-Line Transaction Processing (OLTP): This is the way the data is
processed by an end user/a computer system. Here, the data is detail
oriented, highly repetitive with larger amounts of updates and changes. The
major task of these systems is to perform on-line transaction and query
processing. These systems cover most of the day-to-day operations of the
Sikkim Manipal University Page No. 256 Business Intelligence and Tools Glossary organization, such as purchasing, inventory, manufacturing, payroll,
banking, accounting and registration.
Operational costs: These are the costs associated with running and
maintaining the data warehouse
Operational Databases: These are detail oriented databases defined to
meet the needs of complex processes of an organization. Here, the data is
highly normalized to avoid data redundancy and double-maintenance. A
large number of transactions take place every hour on these databases and
are always "up to date" and represent a snapshot of the current situation.
Contrast to these databases, there are Informational databases that are
stable over a period of time to represent a situation at a specific point in time
in the past.
Periodic Status: This is the status wherein the status value is stored with
reference to the time. For example, the data about an insurance policy is
stored as the status data of the policy at each point of time. So the history of
the changes is preserved in the source systems themselves.
Physical Data Design phase: This is one of the phases in the phased
Enterprise data modeling. This phase is useful to design for the actual
physical implementation and applies physical constraints, such as space,
performance, and the physical distribution of the data.
Policy: An overarching plan (direction) for achieving an organization's
Process: A set of interrelated work activities characterized by a set of
specific inputs and value added tasks that make up a procedure for a set of
Project Manager: An individual in a BI project who monitors the progress
on continuum basis and is responsible for the success of the project.
Sikkim Manipal University Page No. 257 Business Intelligence and Tools Glossary Q
QA Group: A group of individuals in a BI project who ensures the
correctness of the data in the data warehouse
Real Time Data Warehouse: This is an earlier development phase of a
data warehouse. Data warehouses at this stage are updated on a
transaction or event basis. The data is updated every time an operational
system performs a transaction (e.g. an order or a delivery or a booking, etc.)
Realistic data quality: This is the degree of utility and value the data has to
support the organizational processes to accomplish the organizational
Real-time Data: This data represents the current status of the business.
Typically, real-time data is used by operational applications to run the
business and the data constantly changes as operational transactions are
Reconciled data: This is the real-time data that has been cleansed,
modified, or enhanced. This data provides an integrated source of quality
data for use of data analysts in the data analysis.
Refresh: This is a much simpler option than update. But you may have to
keep the data warehouse down for unacceptably long times if you run
refresh jobs every day.
Relationship: Relationships represent the structural interaction and
association among the entities in a model and they are represented with
lines drawn between the two specific entitles. Generally, a relationship is
named grammatically by a verb (such as owns, belongs, and has) and the
relationship between the entities can be defined in terms of the cardinality.
ROLAP Model: This methodology relies on manipulating the data stored in
the relational database to give the appearance of traditional OLAP's slicing
Sikkim Manipal University Page No. 258 Business Intelligence and Tools Glossary and dicing functionality. In this model, data is stored as rows and columns in
relational form. This model presents data to the users in the form of
Selection and Splitting/Joining: This is the basic task that is done at the
beginning of the entire data transformation process. Through use of this
task, you may select either whole records or parts of several records from
the source systems. The splitting/joining task includes the type of data
manipulation you need to perform on selected records of the source
systems. You can either split the selected parts further or join the parts
selected from many source systems. But the joining task is quite often used
in the data warehouse environment.
Sequential Discovery: Techniques that use sequencing or time-series
analysis relate events in time based on a series of preceding events viz.,
prediction of interest rate fluctuations, stock performance. This analysis
reveals various hidden trends and often highly predictive of future events.
Snowflake Model: The snowflake model is derived from the star model and
is the result of decomposing one or more of the dimensions, which
sometimes have hierarchies themselves.
Source Identification: Source identification is a critical process in the data
extraction process. For instance, you have intended to design a data
warehouse to provide strategic information on fulfillment of orders. So you
need to store the information as fulfilled and pending orders. If you deliver
the orders through multiple channels, you are also need to capture the data
about the delivery channels.
Stakeholder: Any individual, group or organization that will have a
significant impact on or will be significantly impacted by the quality of the
product or service an organization provides.
Sikkim Manipal University Page No. 259 Business Intelligence and Tools Glossary Star model: This is a basic structure for a dimensional model and has one
large central table (a fact table) and a set of smaller tables (the dimension
tables) arranged in a radial pattern around the central table
Static data: This is the capture of data at a specific point of time. For
current data, this capture includes all transient data identified for extraction.
Statistical Analysis: Statistical analysis is the most mature of all data
mining technologies and is the easiest to understand. The traditional
statistical modeling techniques such as regression analysis are useful in
building linear models that describe predictable data points.
Strategic planning: The process by which an organization envisions its
future and develops strategies, goals, objectives and action plans to achieve
Summarization: This is a data transformation task that is used in case you
find that it is not required to keep data at the lowest level of detail in your
Summarized data: This is the transaction data in a data warehouse
aggregated at the level required for the most used queries.
Supplier: A source of materials, service or information input provided to a
System: A group of interdependent processes and people that together
perform a common mission.
Task: A specific, definable activity to perform an assigned piece of work,
often finished within a certain time.
Team: A group of individuals organized to work together to accomplish a
specific objective. Sikkim Manipal University Page No. 260 Business Intelligence and Tools Glossary Technical Architect: An individual in a BI project who develops and
implements the overall technical architecture of the BI system, from the
backend hardware/software to the client desktop configurations.
Top-Down Approach: The top-down approach views the data warehouse
as the mainstay of the entire analytic environment. The data warehouse
holds atomic or transaction level data that has been extracted from the
source systems and integrated within a normalized, enterprise data model.
Later, the data is summarized, dimensionalized, and distributed to one or
more “dependent” data marts.
Total Data Quality Management (TDQM): An approach to improve the
quality of the data loaded into a warehouse.
Total study method: This is a method employed to determine the
information needs of the senior executives in an organization. According to
this approach, the information is gathered from a sample of top executives
in the organization concerning the totality of their information needs.
However, this method is more comprehensive in nature and is expensive as
Trainer: An individual in a BI project who works with the end users to get
them familiar with how the front end is set up so that the end users can get
the most benefit out of the system
Type 1 Changes - Correction of Errors: Type 1 changes are applied to
the data warehouse without any need to preserve history as these changes
are usually relate to the corrections of errors in the source systems.
Type 2 Changes - History Preservation: Suppose there is a change in the
marital status of a customer and one of the essential requirements of your
data warehouse is to track of the orders according to the marital status. Sikkim Manipal University Page No. 261 Business Intelligence and Tools Glossary Type 3 Changes - Soft Revisions: Type 3 changes are tentative or soft
revisions. Unlike the Type 2 changes, the orders need to be maintained in
the old and new groups after an effective date.
Unit: An object on which a measurement or observation can be made. Note:
Commonly used in the sense of a "unit of product, "the entity of product
inspected in order to determine whether it is defective or non-defective.
Update: This is an application of incremental changes in the data sources
and ‘refresh’ is a complete reload of data at specified intervals. The refresh
option involves the periodic replacement of complete data warehouse
Visual Warehouse Administrative Clients: The Administrative Client also
runs on Windows NT server and provides an interface for administrative
functions, such as defining the business views, defining the target data
warehouse databases, registering data resources, filtering source data.
Visual Warehouse Agents: The architecture of visual warehouse agents is
a key enabler for scalable business intelligence solutions. These agents run
on Windows NT, OS/2, AS/400, AIX, and Sun Solaris and they handle
access to the source data, filtering, transformation, sub-setting, and delivery
of transformed data to the target warehouse as directed by the Visual
Visual Warehouse Control Database: A control database is set up in DB2
to be used by visual warehouse to store control information used by the
Server. The control database stores the metadata required to build and
manage the warehouse.
Visual Warehouse Server: The visual warehouse server runs on a
Windows NT workstation or server. It controls the interaction of the various
Sikkim Manipal University Page No. 262 Business Intelligence and Tools Glossary data warehouse components and provides for automation of data
warehousing processes through a powerful scheduling facility.
Visual Warehouse Target Databases: The target databases in a data
warehouse contain the visual warehouse data stored in structures defined
as Business Views (BVs). When visual warehouse populates a BV, the data
is extracted from the source and is transformed according to the rules
defined in the BV, which is then stored in the target database.
Visual Warehouse: It is an integrated product for building and maintaining
a data warehouse or data mart in a LAN environment. The visual warehouse
integrates many of the business intelligence component functions into a
single product and it can be used to automate the process of bringing the
data together from heterogeneous sources into a central, integrated,
informational environment. Sikkim Manipal University Page No. 263 Business Intelligence and Tools Glossary Bibliography
1. The Microsoft Data Warehouse Toolkit: with SQL Server 2005 and the
Microsoft Business Intelligence Toolset, Joy Mundy and Warren
Thornthwaite with Ralph Kimball, Wiley Publishing, Inc., ISBN-13: 978-0471-26715-7, ISBN-10: 0-471-26715-5.
2. The Data Warehouse Toolkit: The Complete Guide to Dimensional
Modeling, Ralph Kimball and Margy Ross, Wiley Publishing, Inc., ISBN81-265-0889-2.
3. Modern Data Warehousing, Mining, and Visualization: Core Concepts;
George M. Marakas, Pearson Education, ISBN:81-297-0210-X
4. The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing,
Developing, and Deploying Data Warehouses, Ralph Kimball, Laura
Reeves, Margy Ross, Warren Thornthwaite, Wiley Publishing, Inc.,
5. The Data Warehouse ETL Toolkit: Practical Techniques for Extracting,
Cleaning, Conforming, and Delivering Data, Ralph Kimball, Joe Caserta,
Wiley Publishing, Inc., ISBN: 978-0-7645-6757-5
6. The Data Warehouse Toolkit: Building the Web-Enabled Data
Warehouse, Ralph Kimball, Richard Merz, Wiley Publishing, Inc., ISBN:
7. Data Warehousing Fundamentals: A Comprehensive Guide for IT
Professionals, Paulraj Ponniah, Wiley Publishing, Inc., ISBN: 978-0-47141254-0
8. Building the Data Warehouse, William H. Inmon, Wiley Publishing, Inc.,
9. Data Warehouse Management with DB2 UDB V8.1 Warehouse
Manager, International Business Machines Corporation, Prentice-Hall of
India Pvt., Ltd., ISBN:81-203-2592-3. Sikkim Manipal University Page No. 264 ...
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This note was uploaded on 04/15/2010 for the course MBA mba taught by Professor Smu during the Spring '10 term at Manipal University.
- Spring '10