Businesses may or may not differentiate between a decision support

system (DSS) and an expert system. Some consider each one,

alternately, to be a subcategory of the other. Whether or not they are one

in the same, closely related, or completely independent is frequently

debated in trade and professional literature. Like expert systems, the DSS

relies on computer hardware, software, and information to function

effectively. The debatable distinction, however, between an expert system

and a DSS seems to lie in their practical applications. Decision support

systems are used most often in specific decision-making activities, while

expert systems operate in the area of problem-solving activities. But this

distinction may be blurry in practice, and therefore investigation of an

expert system often implies research on DSS as well.

Four interactive roles form the activities of the expert system:

diagnosing

interpreting

predicting

instructing

The systems accomplish each of these by applying rules and logic

specified by the human expert during system creation or maintenance or

determined by the system itself based on analysis of historical

precedents. Instruction, in particular, emerges as a result of the expert

system's justification system. Synthesizing feedback with various

combinations of diagnostic, interpretative and predictive curriculum, the

expert system can become a finely tuned personal tutor or a fully

developed and standardized group class. Computer-aided instruction

(CAI) thrives as a field of inquiry and development for businesses.

Read more: Expert Systems - benefits http://www.referenceforbusiness.com/encyclopedia/Ent-Fac/Expert-Systems.html#ixzz1eJuY6uM6

DSS system that supports technological and managerial decision making by assisting in the organization of knowledge about ill-structured, semistructured, or unstructured issues. A structured issue has a framework comprising elements and relations between them that are

known and understood. Structured issues are generally ones about which an individual has considerable experiential familiarity. A decision support system (DSS) is not intended to provide support to humans about structured issues since little cognitively based decision support is generally needed.

Emphasis in the use of a decision support system is upon provision of support to decision makers in terms of increasing the effectiveness of the decision-making effort. This support involves the systems engineering steps of formulation of alternatives, the analysis of their impacts, and interpretation and selection of appropriate options for implementations.

The primary components of a decision support system are a database management system (DBMS), a model-base management system (MBMS), and a dialog generation and management system (DGMS). An appropriate database management system must be able to work with both data that are internal to the organization and data that are external to it. Model-base management systems provide sophisticated analysis and interpretation capability. The dialog generation and management system is designed to satisfy knowledge representation, and control and interface requirements.

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Expert SYSTEMS

Computer-based system designed to respond like a human expert in a given field. Expert systems are built on knowledge gathered from human experts, analogous to a database but containing rules that may be applied to solving a specific problem. An interface allows the user to specify symptoms and to clarify a problem by responding to questions posed by the system. Software tools exist to help designers build a special-purpose expert system with minimal effort. An outgrowth of work in artificial intelligence, expert systems show promise for an ever-widening range of applications. There are now widely used expert systems in the fields of medicine, personnel screening, and education.

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I think both DSS and Expert systems can help managers. Expert Systems research provides one source of

technologies for building Decision Support Systems -- I call them Knowledge-Driven DSS. Integration of

Artificial Intelligence and Expert System technologies may occur in some other types of DSS.

“What are the differences between the two?”

DSS is a broad category that includes Data-Driven DSS, Model-Driven, Communication-Driven, Document-

Driven and Knowledge-Driven DSS. The later category are built using Expert Systems tools.

“Would the evolution of DSS end up in Expert Systems?”

NO, I don't think so. -- BUT Knowledge-Driven DSS or Suggestion DSS will become more common and

more powerful. The tools are getting better.

A good reference on integrating DSS and Expert Systems is El-Najdawi, M. K., and Anthony C. Stylianou,

"Expert Support Systems: Integrating AI Technologies", Communications of the ACM, New York:

Association for Computing Machinery, December 1993.

El-Najdawi and Stylianou feel both DSS and Expert Systems "can be used to facilitate and improve the

quality of decision making by reducing information overload and by augmenting the cognitive limitations and

rationality bounds of decision makers." They concluded "A small number of systems have adopted the idea

of DSS/ES integration."

A major contribution is that El-Najdawi and Stylianou identify several forms of integration that have been

discussed in the literature. The major approaches to integration are:

* Expert System integration into various DSS components,

* Expert System integration as a separate component of the DSS,

* DSS models and data access as a component of an Expert System.

Intelligent Decision Support Systems

1 Decisions; Decision Support Systems and Expert Systems

1.1 Normative vs. Descriptive Decision Theory vs. Connectionistic Views

The meaning of the term 'decision' varies widely. Very often it is used without defining its interpretation and this frequently leads to misinterpretations of statements about decisions, decision analysis or decision support systems. To avoid misunderstandings we shall select from the large number of possible definitions three, which are of particular relevance to this paper:

a) Decision Logic (formal or normative) Here a "decision" is defined as an abstract, timeless, contextfree "act of choice", which is best be described as a quintuple D (A, S, E, U, P), were A is the action space, S the state space, E the event space, U the utility space and P the probability space. Often this purely formal model for "rational" decision making is illustrated by examples which suggest a relationship of this model to the reality of decision making, obliterating the fact that decision logic is a purely formal, mathematical or logical theory, focusing on rationality in acts of choice. Nevertheless, this model of a decision underlies many methods for or theories about decision making.

b) Cognitive decision theory This is an empirical, descriptive, non-statistical, context related process theory [13] and considers a "decision" as a decision making process very similar to a problem solving process, which is a special, time consuming, context dependent information processing process. The human decision maker is considered in analogy to a computer system, i. e. data and knowledge has to be fed into the system. This and the type of information processing performed determines the outcome [24].

c) The connectionist paradigm Neural nets also model living (human) information processing, but on a more physical and not so functional level. Information is processed from input via hidden to output layers of artificial neurones. One of the differences between the "cognitive" and the "neural" decision model is, that the latter includes explicitly and even concentrates on learning and on topological features, while the former does not exclude learning but does not consider it as one of the points of major interest.

Figure 1: The decision as information process

1.2 Decision Support Systems vs. Expert Systems

Decision Support Systems (DSS), as successors of Management Information Systems (MIS), traditionally follow the decision logic line of thinking and include in MIS algorithmic tools to improve the choice activity of decision makers. This includes optimization methods, mathematical programming, multi criteria models etc. They are "structure related", normally assume that the decision problem can be formulated mathematically and do not stress information processing and display.

By contrast to DSS, Expert System (ES) or knowledge-based systems, as successors of the "General Problem Solver" [Newell, Simon 1972] follow more the process paradigm of cognitive decision theory; they not necessarily assume that the decision problems can be formulated as mathematical models; they substitute human expertise for missing efficient algorithms and they are not structure- but context related, with much smaller domains of application than DSS. "Knowledge" is represented in many different ways, such as, frames, semantic nets, rules etc. Knowledge is processed in inference machines, which normally perform symbol processing, i. e. truth values of antecedents, conclusions etc. In the more recent past the border between DSS and ES has become pretty fuzzy. Some experts consider ES as part of DSS [Turban 1988], others see DSS and ES as basically different systems and others combine the two approaches into "Knowledge-based DSS" [Klein, Methlic 1995]. Even though all of these systems can support decisions in one or the other way, for the sake of argument I shall keep the distinction between algorithmic DSS and Knowledge-based ES.

1.3 Scientific Background

As already mentioned above, "decision" is regarded differently in different decision theories; and, furthermore, different sciences are contributing to decision making paradigms. This fact will be important for the conclusions drawn at the end of this paper. Therefore, figure 3 sketches some of the relationships.

1.4 Main Defiencies of DSS

Let us consider DSS in the broad sense as the application oriented result of decision analysis and consider some of their deficiencies, which are relevant for the interface with "Fuzzy

Logic": DSS and ES-technology share the dichotomy character, which leads, however, to different weaknesses on either side. While on the DSS side models and algorithms become sometimes pretty bad approximations of real problems, on the ES-side this leads to symbol processing rather than to knowledge processing. The former might be much harder to detect than the latter. Both, DSS and ES share the suffering from the size of realistic problems: nowadays there is often an abundance of data rather than a lack of them. Both areas are influenced by the discrepancy between demand and supply: while scientific contributions normally are very specific, i. e. developed in one scientific discipline and focusing on one (small) and imagined problem, the practitioner on the demand side is looking for tools and solutions to his problems, which are frequently multi disciplinary, and not for approaches that solve a part of his problem, probably even impairing other parts. This is more serious for DSS than for ES, because the latter generally have a much smaller domain of application. This and some other factors often impair userfriendliness and, hence, user acceptance to a degree that the use of the tools never really occurs.

Different between expert system and decision support system?

DSS aid in problem solving by allowing for manipulation of data & models whereas ES allow experts to teach computers about their field so that the system may support more of the decision making process for less expert decision makers. DSS most often contain equations that the system uses to solve problems or update reports immediately, and the users makes the final decisions on the basis

Posted in Computers & Technology by gilli... at 4:41 PM on November 04, 2008

Tags different, expert, system, decision, support

Advantages of 64-bit Operating system?

...] This hardware decision is already entrenched.If the historical pattern repeats, three years after 64-bit hardware arrives, the market will adopt a new standard operating system. This places the obsolescence cope with more memory.The three contenders are Windows-64, Linux, and MacOS X. The winner still could be any of them. Just as the hardware decision was uncertain in late 2003, the operating system

Posted in Computers & Technology by Arun Pandian at 11:44 PM on September 27, 2008

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What is Data Warehousing & What is Difference between data warehousing and data mining?

, sometimes local databases. The latter idea is known as the data mart.Applications of data warehouses include data mining, Web mining, and decision support systems DSS.Data mining is sorting through data warehouses include data mining, Web mining, and decision support systems DSS.Data mining is sorting through data to identify patterns and establish relationships.Data mining parameters include: Association

Posted in Computers & Technology by Aryan Gusain at 9:01 PM on June 25, 2008

Tags data, warehousing, difference, mining

MIS Means ?

of human decision making, e.g. Decision Support Systems, Expert systems, and Executive information systems...Management information system. A general term for all automated hardware and software used decision making, e.g. Decision Support Systems, Expert systems, and Executive information systems.[1...]...Stands for Management information system.MIS is the designation for the field of computer...

Posted in Computers & Technology by king99h at 11:05 PM on December 19, 2007

Tags means

what is the application of computer in pharmacy

work with pharmacy information management systems that help the pharmacist make excellent decisions about patient drug therapies with respect to, medical insurance records, drug interactions, as well work with pharmacy information management systems that help the pharmacist make excellent decisions about patient drug therapies with respect to, medical insurance records, drug interactions, as well

Posted in Computers & Technology by harshit at 12:31 AM on June 24, 2008

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What in MOSS in .net?plz give some information.

organizational resources, search for experts and corporate information, manage content and workflow, and leverage business insight to make better-informed decisions.1.Enterprise Search Quickly and easily find with team members, find organizational resources, search for experts and corporate information, manage content and workflow, and leverage business insight to make better-informed decisions.1. Collaboration

Posted in Computers & Technology by Rahul Sharma at 6:09 PM on May 07, 2008

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Explain role of use case model in software development?

in the system. A viewpoint is the way that the system is seen by its different users bear in mind that some of the users may not be human, but other computer systems. Some experts suggest that identification of achieving that goal. Use cases typically avoid technical jargon, preferring instead the language of the end user or domain expert. Use cases are often co-authored by systems analysts and end users. The UML

Posted in Computers & Technology by mr007 at 9:42 PM on September 11, 2008

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How does a linux system boot up with initrd ?

if it encounters an error at this point; these error messages and their explanations can be found in this part of the Troubleshooting Expert. 8. The BIOS performs a "system inventory" of sorts, doing morewell,initrd provides the capability to load a RAM disk by the boot loader.This RAM disk can then be mounted as the root file system and programscan be run from it. Afterwards, a new root file system

Posted in Computers & Technology by Harvinder at 11:34 PM on September 04, 2008

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What is SAP in network

decisions.Start by adding the needed services to the /etc/services file.sapdp 32/tcp SAP Dispatcher. 3200 System-Numbersapgw 33/tcp SAP Gateway. 3300 System-Numbersapsp 34/tcp 3400 System-Numbersapms 36 to make the right decisions.Start by adding the needed services to the /etc/services file.sapdp 32/tcp SAP Dispatcher. 3200 System-Numbersapgw 33/tcp SAP Gateway. 3300 System-Numbersapsp 34/tcp 3400

Posted in Computers & Technology by Om Prakash at 5:47 PM on September 26, 2008

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A Data Warehouse is not an individual repository product. Rather, it is an overall strategy, or process, for building decision support systems and a knowledge-based applications architecture and environment that supports both everyday tactical decision making and long-term business strategizing. The Data Warehouse environment positions a business to utilize an enterprise-wide data store to link information from diverse sources and make the information accessible for a variety of user purposes, most notably, strategic analysis. Business analysts must be able to use the Warehouse for such strategic purposes as trend identification, forecasting, competitive analysis, and targeted market research.

Data Warehouses and Data Warehouse applications are designed primarily to support executives, senior managers, and business analysts in making complex business decisions. Data Warehouse applications provide the business community with access to accurate, consolidated information from various internal and external sources.

The primary objective of Data Warehousing is to bring together information from disparate sources and put the information into a format that is conducive to making business decisions. This objective necessitates a set of activities that are far more complex than just collecting data and reporting against it. Data Warehousing requires both business and technical expertise and involves the following activities:

- Accurately identifying the business information that must be contained in the Warehouse- Identifying and prioritizing subject areas to be included in the Data Warehouse- Managing the scope of each subject area which will be implemented into the Warehouse on an iterative basis- Developing a scaleable architecture to serve as the Warehouse’s technical and application foundation, and identifying and selecting the hardware/software/middleware components to implement it- Extracting, cleansing, aggregating, transforming and validating the data to ensure accuracy and consistency- Defining the correct level of summarization to support business decision making- Establishing a refresh program that is consistent with business needs, timing and cycles- Providing user-friendly, powerful tools at the desktop to access the data in the Warehouse- Educating the business community about the realm of possibilities that are available to

them through Data Warehousing - Establishing a Data Warehouse Help Desk and training users to effectively utilize the desktop tools- Establishing processes for maintaining, enhancing, and ensuring the ongoing success and applicability of the Warehouse

Until the advent of Data Warehouses, enterprise databases were expected to serve multiple purposes, including online transaction processing, batch processing, reporting, and analytical processing. In most cases, the primary focus of computing resources was on satisfying operational needs and requirements. Information reporting and analysis needs were secondary considerations. As the use of PCs, relational databases, 4GL technology and end-user computing grew and changed the complexion of information processing, more and more business users demanded that their needs for information be addressed. Data Warehousing has evolved to meet those needs without disrupting operational processing.

In the Data Warehouse model, operational databases are not accessed directly to perform information processing. Rather, they act as the source of data for the Data Warehouse, which is the information repository and point of access for information processing. There are sound reasons for separating operational and informational databases, as described below.

- The users of informational and operational data are different. Users of informational data are generally managers and analysts; users of operational data tend to be clerical, operational and administrative staff.

- Operational data differs from informational data in context and currency. Informational data contains an historical perspective that is not generally used by operational systems.

- The technology used for operational processing frequently differs from the technology required to support informational needs.

- The processing characteristics for the operational environment and the informational environment are fundamentally different.

The Data Warehouse functions as a Decision Support System (DSS) and an Executive Information System (EIS), meaning that it supports informational and analytical needs by providing integrated and transformed enterprise-wide historical data from which to do management analysis. A variety of sophisticated tools are readily available in the marketplace to provide user-friendly access to the information stored in the Data Warehouse.

Data Warehouses can be defined as subject-oriented, integrated, time-variant, non-volatile collections of data used to support analytical decision making. The data in the Warehouse comes from the operational environment and external sources. Data Warehouses are physically separated from operational systems, even though the operational systems feed the Warehouse with source data.

Subject Orientation

Data Warehouses are designed around the major subject areas of the enterprise; the operational environment is designed around applications and functions. This difference in orientation (data vs. process) is evident in the content of the database. Data Warehouses do not contain information that will not be used for informational or analytical processing; operational databases contain detailed data that is needed to satisfy processing

requirements but which has no relevance to management or analysis.

Integration and Transformation

The data within the Data Warehouse is integrated. This means that there is consistency among naming conventions, measurements of variables, encoding structures, physical attributes, and other salient data characteristics. An example of this integration is the treatment of codes such as gender codes. Within a single corporation, various applications may represent gender codes in different ways: male vs. female, m vs. f, and 1 vs. 0, etc. In the Data Warehouse, gender is always represented in a consistent way, regardless of the many ways by which it may be encoded and stored in the source data. As the data is moved to the Warehouse, it is transformed into a consistent representation as required.

Time Variance

All data in Data Warehouse is accurate as of some moment in time, providing an historical perspective. This differs from the operational environment in which data is intended to be accurate as of the moment of access. The data in the Data Warehouse is, in effect, a series of snapshots. Once the data is loaded into the enterprise data store and data marts, it cannot be updated. It is refreshed on a periodic basis, as determined by the business need. The operational data store, if included in the Warehouse architecture, may be updated.

Non-Volatility

Data in the Warehouse is static, not dynamic. The only operations that occur in Data Warehouse applications are the initial loading of data, access of data, and refresh of data. For these reasons, the physical design of a Data Warehouse optimizes the access of data, rather than focusing on the requirements of data update and delete processing.

Data Warehouse Configurations

A Data Warehouse configuration, also known as the logical architecture, includes the following components:- one Enterprise Data Store (EDS) - a central repository which supplies atomic (detail level) integrated information to the whole organization. - (optional) one Operational Data Store - a "snapshot" of a moment in time's enterprise-wide data- (optional) one or more individual Data Mart(s) - summarized subset of the enterprise's data specific to a functional area or department, geographical region, or time period- one or more Metadata Store(s) or Repository(ies) - catalog(s) of reference information about the primary data. Metadata is divided into two categories: information for technical use, and information for business end-users.

The EDS is the cornerstone of the Data Warehouse. It can be accessed for both immediate informational needs and for analytical processing in support of strategic decision making, and can be used for drill-down support for the Data Marts which contain only summarized data. It is fed by the existing subject area operational systems and may also contain data from external sources. The EDS in turn feeds individual Data Marts that are accessed by end-user query tools at the user's desktop. It is used to consolidate related data from multiple sources into a single source, while the Data Marts are used to physically distribute the consolidated data into logical categories of data, such as business functional departments or geographical regions. The EDS is a collection of daily "snapshots" of enterprise-wide data taken over an extended time period, and thus retains and makes available for tracking purposes the history

of changes to a given data element over time. This creates an optimum environment for strategic analysis. However, access to the EDS can be slow, due to the volume of data it contains, which is a good reason for using Data Marts to filter, condense and summarize information for specific business areas. In the absence of the Data Mart layer, users can access the EDS directly.

Metadata is "data about data," a catalog of information about the primary data that defines access to the Warehouse. It is the key to providing users and developers with a road map to the information in the Warehouse. Metadata comes in two different forms: end-user and transformational. End-user metadata serves a business purpose; it translates a cryptic name code that represents a data element into a meaningful description of the data element so that end-users can recognize and use the data. For example, metadata would clarify that the data element "ACCT_CD" represents "Account Code for Small Business." Transformational metadata serves a technical purpose for development and maintenance of the Warehouse. It maps the data element from its source system to the Data Warehouse, identifying it by source field name, destination field code, transformation routine, business rules for usage and derivation, format, key, size, index and other relevant transformational and structural information. Each type of metadata is kept in one or more repositories that service the Enterprise Data Store.

While an Enterprise Data Store and Metadata Store(s) are always included in a sound Data Warehouse design, the specific number of Data Marts (if any) and the need for an Operational Data Store are judgment calls. Potential Data Warehouse configurations should be evaluated and a logical architecture determined according to business requirements.

The Data Warehouse Process

The james martin + co Data Warehouse Process does not encompass the analysis and identification of organizational value streams, strategic initiatives, and related business goals, but it is a prescription for achieving such goals through a specific architecture. The Process is conducted in an iterative fashion after the initial business requirements and architectural foundations have been developed with the emphasis on populating the Data Warehouse with "chunks" of functional subject-area information each iteration. The Process guides the development team through identifying the business requirements, developing the business plan and Warehouse solution to business requirements, and implementing the configuration, technical, and application architecture for the overall Data Warehouse. It then specifies the iterative activities for the cyclical planning, design, construction, and deployment of each population project. The following is a description of each stage in the Data Warehouse Process. (Note: The Data Warehouse Process also includes conventional project management, startup, and wrap-up activities which are detailed in the Plan, Activate, Control and End stages, not described here.)

Business Case Development

A variety of kinds of strategic analysis, including Value Stream Assessment, have likely already been done by the customer organization at the point when it is necessary to develop a Business Case. The Business Case Development stage launches the Data Warehouse development in response to previously identified strategic business initiatives and "predator" (key) value streams of the organization. The organization will likely have identified more than one important value stream. In the long term it is possible to implement Data Warehouse solutions that address multiple value streams, but it is the predator value stream or highest priority strategic initiative that usually becomes the focus of the short-term strategy and first run population projects resulting in a Data Warehouse.

At the conclusion of the relevant business reengineering, strategic visioning, and/or value stream assessment activities conducted by the organization, a Business Case can be built to justify the use of the Data Warehouse architecture and implementation approach to solve key business issues directed at the most important goals. The Business Case defines the outlying activities, costs, benefits, and critical success factors for a multi-generation implementation plan that results in a Data Warehouse framework of an information storage/access system. The Warehouse is an iterative designed/developed/refined solution to the tactical and strategic business requirements. The Business Case addresses both the short-term and long-term Warehouse strategies (how multiple data stores will work together to fulfill primary and secondary business goals) and identifies both immediate and extended costs so that the organization is better able to plan its short and long-term budget appropriation.

Business Question Assessment

Once a Business Case has been developed, the short-term strategy for implementing the Data Warehouse is mapped out by means of the Business Question Assessment (BQA) stage. The purpose of BQA is to:- Establish the scope of the Warehouse and its intended use - Define and prioritize the business requirements and the subsequent information (data) needs the Warehouse will address- Identify the business directions and objectives that may influence the required data and application architectures- Determine which business subject areas provide the most needed information; prioritize and sequence implementation projects accordingly- Drive out the logical data model that will direct the physical implementation model- Measure the quality, availability, and related costs of needed source data at a high level- Define the iterative population projects based on business needs and data validation

The prioritized predator value stream or most important strategic initiative is analyzed to determine the specific business questions that need to be answered through a Warehouse implementation. Each business question is assessed to determine its overall importance to the organization, and a high-level analysis of the data needed to provide the answers is undertaken. The data is assessed for quality, availability, and cost associated with bringing it into the Data Warehouse. The business questions are then revisited and prioritized based upon their relative importance and the cost and feasibility of acquiring the associated data. The prioritized list of business questions is used to determine the scope of the first and subsequent iterations of the Data Warehouse, in the form of population projects. Iteration scoping is dependent on source data acquisition issues and is guided by determining how many business questions can be answered in a three to six month implementation time frame. A "business question" is a question deemed by the business to provide useful information in determining strategic direction. A business question can be answered through objective analysis of the data that is available.

Architecture Review and Design

The Architecture is the logical and physical foundation on which the Data Warehouse will be built. The Architecture Review and Design stage, as the name implies, is both a requirements analysis and a gap analysis activity. It is important to assess what pieces of the architecture already exist in the organization (and in what form) and to assess what pieces are missing which are needed to build the complete Data Warehouse architecture.

During the Architecture Review and Design stage, the logical Data Warehouse architecture is developed. The logical architecture is a configuration map of the necessary data stores that make up the Warehouse; it includes a central Enterprise Data Store, an optional Operational Data Store, one or more (optional) individual business area Data Marts, and one or more Metadata stores. In the metadata store(s) are two different kinds of metadata that catalog reference information about the primary data.

Once the logical configuration is defined, the Data, Application, Technical and Support Architectures are designed to physically implement it. Requirements of these four architectures are carefully analyzed so that the Data Warehouse can be optimized to serve the users. Gap analysis is conducted to determine which components of each architecture already exist in the organization and can be reused, and which components must be developed (or purchased) and configured for the Data Warehouse.

The Data Architecture organizes the sources and stores of business information and defines the quality and management standards for data and metadata.

The Application Architecture is the software framework that guides the overall implementation of business functionality within the Warehouse environment; it controls the movement of data from source to user, including the functions of data extraction, data cleansing, data transformation, data loading, data refresh, and data access (reporting, querying).

The Technical Architecture provides the underlying computing infrastructure that enables the data and application architectures. It includes platform/server, network, communications and connectivity hardware/software/middleware, DBMS, client/server 2-tier vs.3-tier approach, and end-user workstation hardware/software. Technical architecture design must address the requirements of scalability, capacity and volume handling (including sizing and partitioning of tables), performance, availability, stability, chargeback, and security.

The Support Architecture includes the software components (e.g., tools and structures for backup/recovery, disaster recovery, performance monitoring, reliability/stability compliance reporting, data archiving, and version control/configuration management) and organizational functions necessary to effectively manage the technology investment.

Architecture Review and Design applies to the long-term strategy for development and refinement of the overall Data Warehouse, and is not conducted merely for a single iteration. This stage develops the blueprint of an encompassing data and technical structure, software application configuration, and organizational support structure for the Warehouse. It forms a foundation that drives the iterative Detail Design activities. Where Design tells you what to do; Architecture Review and Design tells you what pieces you need in order to do it.

The Architecture Review and Design stage can be conducted as a separate project that runs mostly in parallel with the Business Question Assessment stage. For the technical, data, application and support infrastructure that enables and supports the storage and access of information is generally independent from the business requirements of which data is needed to drive the Warehouse. However, the data architecture is dependent on receiving input from certain BQA activities (data source system identification and data modeling), so the BQA stage must conclude before the Architecture stage can conclude.

The Architecture will be developed based on the organization's long-term Data Warehouse strategy, so that future iterations of the Warehouse will have been provided for and will fit within the overall architecture.

Tool Selection

The purpose of this stage is to identify the candidate tools for developing and implementing the Data Warehouse data and application architectures, and for performing technical and support architecture functions where appropriate. Select the candidate tools that best meet the business and technical requirements as defined by the Data Warehouse architecture, and recommend the selections to the customer organization. Procure the tools upon approval from the organization.

It is important to note that the process of selecting tools is often dependent on the existing technical infrastructure of the organization. Many organizations feel strongly for various reasons about using tools for the Data Warehouse applications that they already have in their "arsenal" and are reluctant to purchase new application packages. It is recommended that a thorough evaluation of existing tools and the feasibility of their reuse be done in the context of all tool evaluation activities. In some cases, existing tools can be form-fitted to the Data Warehouse; in other cases, the customer organization may need to be convinced that new tools would better serve their needs.

It may even be feasible that this series of activities is skipped altogether, if the organization is insistent that particular tools be used (no room for negotiation), or if tools have already been assessed and selected in anticipation of the Data Warehouse project.

Tools may be categorized according to the following data, technical, application, or support functions:

- Source Data Extraction and Transformation- Data Cleansing- Data Load- Data Refresh- Data Access- Security Enforcement- Version Control/Configuration Management- Backup and Recovery- Disaster Recovery- Performance Monitoring- Database Management- Platform- Data Modeling- Metadata Management

Iteration Project Planning

The Data Warehouse is implemented (populated) one subject area at a time, driven by specific business questions to be answered by each implementation cycle. The first and subsequent implementation cycles of the Data Warehouse are determined during the BQA stage. At this point in the Process the first (or next if not first) subject area implementation project is planned. The business requirements discovered in BQA and, to a lesser extent, the technical requirements of the Architecture Design stage are now refined through user interviews and focus sessions to the subject area level. The results are further analyzed to yield the detail needed to design and implement a single population project, whether initial or follow-on. The Data Warehouse project team is expanded to include the members needed to construct and deploy the Warehouse, and a detailed work plan for the design and

implementation of the iteration project is developed and presented to the customer organization for approval.

Detail Design

In the Detail Design stage, the physical Data Warehouse model (database schema) is developed, the metadata is defined, and the source data inventory is updated and expanded to include all of the necessary information needed for the subject area implementation project, and is validated with users. Finally, the detailed design of all procedures for the implementation project is completed and documented. Procedures to achieve the following activities are designed:

- Warehouse Capacity Growth- Data Extraction/Transformation/Cleansing- Data Load- Security- Data Refresh- Data Access- Backup and Recovery- Disaster Recovery- Data Archiving- Configuration Management- Testing- Transition to Production- User Training- Help Desk- Change Management

Implementation

Once the Planning and Design stages are complete, the project to implement the current Data Warehouse iteration can proceed quickly. Necessary hardware, software and middleware components are purchased and installed, the development and test environment is established, and the configuration management processes are implemented. Programs are developed to extract, cleanse, transform and load the source data and to periodically refresh the existing data in the Warehouse, and the programs are individually unit tested against a test database with sample source data. Metrics are captured for the load process. The metadata repository is loaded with transformational and business user metadata. Canned production reports are developed and sample ad-hoc queries are run against the test database, and the validity of the output is measured. User access to the data in the Warehouse is established. Once the programs have been developed and unit tested and the components are in place, system functionality and user acceptance testing is conducted for the complete integrated Data Warehouse system. System support processes of database security, system backup and recovery, system disaster recovery, and data archiving are implemented and tested as the system is prepared for deployment. The final step is to conduct the Production Readiness Review prior to transitioning the Data Warehouse system into production. During this review, the system is evaluated for acceptance by the customer organization.

Transition to Production

The Transition to Production stage moves the Data Warehouse development project into the production environment. The production database is created, and the

extraction/cleanse/transformation routines are run on the operations system source data. The development team works with the Operations staff to perform the initial load of this data to the Warehouse and execute the first refresh cycle. The Operations staff is trained, and the Data Warehouse programs and processes are moved into the production libraries and catalogs. Rollout presentations and tool demonstrations are given to the entire customer community, and end-user training is scheduled and conducted. The Help Desk is established and put into operation. A Service Level Agreement is developed and approved by the customer organization. Finally, the new system is positioned for ongoing maintenance through the establishment of a Change Management Board and the implementation of change control procedures for future development cycles.

Understanding the Data Warehouse ArchitectureVisual Studio 2005

Other Versions

The Team Foundation reporting warehouse is a traditional data warehouse consisting of a relational database organized in an approximate star schema and an OLAP database built on top of the relational database. The following diagram shows the high-level architecture of the Team Foundation data warehouse and the relationships between the operational stores, the data warehouse, and the team reports.

Operational Stores

Each tool or plug-in in Team Foundation use a relational database in Microsoft SQL Server 2005 to store the data used by the tool in its day-to-day operations. This relational database is often referred to as the operational store. The operational stores for Team Foundation include:

Common structure databases (TfsIntegration and TfsActivityLogging) Work item tracking databases (TfsWorkItemTracking and

TfsWorkItemTrackingAttachments) Source control database (TfsVersionControl) Team Foundation Build database (TfsBuild) Team test database (TfsBuild)

You might also have operational stores created for third-party tools.

Like most operational stores, the schema of the relational database is designed and optimized for the online transactional processing of data. As the tool or plug-in performs an activity, it writes the latest information to the operational store. Therefore, data in the operational store is constantly changing and being updated, and all data is current.

Warehouse Adapters

Because each tool or plug-in has its own schema requirements and data is stored in the operational store to optimize transactional processing, the purpose of the warehouse adapter is to put the operational data into a form usable by the data warehouse. The warehouse adapter is a managed assembly that extracts the data from the operational store, transforms the data to a standardized format compatible with the warehouse, and writes the transformed data into the warehouse relational database. There is a separate adapter for each operational data store.

The warehouse adapter copies and transforms those data fields specified in either the basic warehouse configuration or in the process template used at the time a new team project is created. If you subsequently change the process template to add or delete which data fields are written to the data warehouse, these changes are detected the next time the adapter is run. The adapter runs periodically with a frequency set by the RunIntervalSeconds property. The default setting for the refresh frequency is 3,600 seconds, so give careful consideration to the appropriate refresh frequency for your installation. For more information about changing the refresh frequency, see How to: Change the Data Warehouse Refresh Frequency.

It is important that data is not written from the relational database to the data cube while the relational database is itself being updated from the operational store. To avoid conflicts reading and writing data, the warehouse adapters that push and pull the data are synchronized. After the adapters have completed their calls, the cube is reprocessed.

The Warehouse Relational Database

Each tool describes its contribution to the data warehouse in an XML schema. The schema specifies the fields that are written to the relational database as dimensions, measures, and details. The schema is also mapped directly into the OLAP database.

The data in the warehouse are stored in a set of tables organized in a star schema. The central table of the star schema is called the fact table, and the related tables represent dimensions. Dimensions provide the means for disaggregating reports into smaller parts. A row in a fact table usually contains either the value of a measure or a foreign key reference to a dimension table. The row represents the current state of every item covered by the fact table. For example, the Work Item fact table has one row for every work item stored in Work Item operational store.

A dimension table stores the set of values that exist for a given dimension. Dimensions may be shared between different fact tables and cubes, and they may be referenced by a single fact table or data cube. A Person dimension, for example, will be referenced by the Work

Items fact table for Assigned To, Opened By, Resolved By, and Closed By properties, and it will be referenced by the Code Churn fact table for the Checked In By property.

Measures are values taken from the operational data. For example, Total Churn is a measure that indicates the number of source code changes in the selected changesets. Count is a special measure in that it can be implicit, as long as there is one record for every item that is counted. The measures defined in a fact table form a measure group in the cube.

For more information about the facts, dimensions, and measures in the data warehouse, see Understanding the Structure of the Data Warehouse Cube.

The Warehouse OLAP Cube

Fact tables are a good source of information for reports that show the current state of affairs. However, to report on trends for data that changes over time, you need to duplicate the same data for each of the time increments that you want to report on. For example, to report on daily trends for work items or test results, the warehouse needs to retain the state of every item for each day. This allows the data cube to aggregate the measures by day. The warehouse OLAP data cube aggregates both data from the underlying star schema and time data into multidimensional structures.

Each time the data cube is processed, the data stored in the star schemas in the relational database are pulled into the OLAP cube, aggregated, and stored. The data in the cube is aggregated so that high-level reports, which would otherwise require complex processing using the star schema, are simple select statements. The cube provides a central place to obtain data for reports without having to know the schema for each operational store and without having to access each store separately.

Report Designer Reports

Report Designer is a component of Visual Studio 2005 that allows you to define the Team Foundation data warehouse as a data source and then design a report interactively. Report Designer provides tabbed windows for Data, Layout, and Preview, and you can add datasets to accommodate a new report design idea, or adjust report layout based on preview results. In addition to the Data, Layout, and Preview design surfaces, Report Designer provides query builders, an Expression editor, and wizards to help you place images or step you through the process of creating a simple report. For more information about using Report Designer, see Using Report Designer for Team Foundation Server Reporting.

Excel Reports

Team Foundation integrates with Microsoft Excel to allow you to use Microsoft Excel to manage your project and produce reports. Microsoft Excel provides pivot tables and charts for viewing and analyzing multi-dimensional data. You can bind these pivot tables directly to the Team Foundation OLAP cube, so you can interact with the data in the cube. For more information about using Microsoft Excel for reporting, see Using Microsoft Excel for Team Foundation Server Reporting.

Security

Security for the Team Foundation data warehouse is defined at the database level, while security for team reports is at the team project level. The Team Foundation Server administrator determines who has access to the data in the data warehouse by granting or revoking permissions on the user's account. By default, write access to the warehouse is restricted to a service account under which the warehouse service runs. Each tool adapter has write access to the data warehouse because it runs in this security context. Read-only access is granted by the administrator to individual users or groups of users. A user who has permission to view the data in the warehouse for a particular team project has full access to

all of the data for that project. However, a user with permission to view the data for one team project cannot automatically view the data from another team project. For more information about granting or denying read-only access to the data warehouse, see How to: Change the Data Warehouse Security Settings.

See Also

An OLAP (On-Line Analytical Processing) server enables a more sophisticated end-user business model to be applied when navigating the data warehouse. The multidimensional structures allow the user to analyze the data as they want to view their business – summarizing by product line, region, and other key perspectives of their business. The Data Mining Server must be integrated with the data warehouse and the OLAP server to embed ROI-focused business analysis directly into this infrastructure. An advanced, process-centric metadata template defines the data mining objectives for specific business issues like campaign management, prospecting, and promotion optimization. Integration with the data warehouse enables operational decisions to be directly implemented and tracked. As the warehouse grows with new decisions and results, the organization can continually mine the best practices and apply them to future decisions.

This design represents a fundamental shift from conventional decision support systems. Rather than simply delivering data to the end user through query and reporting software, the Advanced Analysis Server applies users’ business models directly to the warehouse and returns a proactive analysis of the most relevant information. These results enhance the metadata in the OLAP Server by providing a dynamic metadata layer that represents a distilled view of the data. Reporting, visualization, and other analysis tools can then be applied to plan future actions and confirm the impact of those plans.

Profitable Applications

A wide range of companies have deployed successful applications of data mining. While early adopters of this technology have tended to be in information-intensive industries such as financial services and direct mail marketing, the technology is applicable to any company looking to leverage a large data warehouse to better manage their customer relationships. Two critical factors for success with data mining are: a large, well-integrated data warehouse and a well-defined understanding of the business process within which data mining is to be applied (such as customer prospecting, retention, campaign management, and so on).

Some successful application areas include:

A pharmaceutical company can analyze its recent sales force activity and their results to improve targeting of high-value physicians and determine which marketing activities will have the greatest impact in the next few months. The data needs to include competitor market activity as well as information about the local health care systems. The results can be distributed to the sales force via a wide-area network that enables the representatives to review the recommendations from the perspective of the key attributes in the decision process. The ongoing, dynamic analysis of the data warehouse allows best practices from throughout the organization to be applied in specific sales situations.

A credit card company can leverage its vast warehouse of customer transaction data to identify customers most likely to be interested in a new credit product. Using a small test mailing, the attributes of customers with an affinity for the product can be

identified. Recent projects have indicated more than a 20-fold decrease in costs for targeted mailing campaigns over conventional approaches.

A diversified transportation company with a large direct sales force can apply data mining to identify the best prospects for its services. Using data mining to analyze its own customer experience, this company can build a unique segmentation identifying the attributes of high-value prospects. Applying this segmentation to a general business database such as those provided by Dun & Bradstreet can yield a prioritized list of prospects by region.

A large consumer package goods company can apply data mining to improve its sales process to retailers. Data from consumer panels, shipments, and competitor activity can be applied to understand the reasons for brand and store switching. Through this analysis, the manufacturer can select promotional strategies that best reach their target customer segments.

Each of these examples have a clear common ground. They leverage the knowledge about customers implicit in a data warehouse to reduce costs and improve the value of customer relationships. These organizations can now focus their efforts on the most important (profitable) customers and prospects, and design targeted marketing strategies to best reach them.

Conclusion

Comprehensive data warehouses that integrate operational data with customer, supplier, and market information have resulted in an explosion of information. Competition requires timely and sophisticated analysis on an integrated view of the data. However, there is a growing gap between more powerful storage and retrieval systems and the users’ ability to effectively analyze and act on the information they contain. Both relational and OLAP technologies have tremendous capabilities for navigating massive data warehouses, but brute force navigation of data is not enough. A new technological leap is needed to structure and prioritize information for specific end-user problems. The data mining tools can make this leap. Quantifiable business benefits have been proven through the integration of data mining with current information systems, and new products are on the horizon that will bring this integration to an even wider audience of users.

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�Increased productivity of end-users. �Reduced backlog of applications

development for IT staff. �Retention of organizational control over the integrity of corporate data. �Reduced query drag and network

traffic on OLTP systems or on the

data warehouse. �Improved potential revenue and

profitability.

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�Example of two-dimensional query. �What is the total revenue generated by

property sales in each city, in each quarter of 2004?’

�Choice of representation is based on

types of queries end-user may ask. �Compare representation - three-field

relational table versus two- dimensional matrix.