Database Management Systems

Dr. K.T. Subhaschandra.MBA – Coordinator,Dept of Commerce and ManagementGovt. R.C.College of Commerce

The Three-Tier Architecture of Web-Based Data Warehousing:

Client

Application Server

Data warehouse

Inte

rnet

In

tran

et, /

Ext

rane

t

Web-Server

Web - Pages

Web-Browser

Data, Information, Data, Information, Knowledge Knowledge

&& WisdomWisdom By:

Dr. K. T. Subhas chandra Dept. of Commerce & Management

Govt. R. C. College of Commerce & Management

There is probably no segment of activity in the world attracting as much attention at present as that of

knowledge management

Data, Information, Knowledge, and WisdomRussell Ackoff, professor of organizational change, categorized the

content of the human mind as:

– Data: symbols

– Information: data that are processed to be useful; provides

answers to "who", "what", "where", and "when" questions

– Knowledge: application of data and information; answers "how"

questions

– Understanding: appreciation of "why"

– Wisdom: evaluated understanding.

Ackoff indicates that the first four categories relate to the past; they deal with what has been or what is known. Only the fifth category, wisdom, deals with the future because it incorporates vision and design. With wisdom, people can create the future rather than just grasp the present and past. But achieving wisdom isn't easy; people must move successively through the other categories

A further elaboration of Ackoff's definitions follows:

• Data... data is raw. It simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself. In computer parlance, a spreadsheet generally starts out by holding data.

• Information... information is data that has been given meaning by way of relational connection. This "meaning" can be useful, but does not have to be. In computer parlance, a relational database makes information from the data stored within it.

Knowledge... knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process. When someone "memorizes" information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge. For example, elementary school children memorize, or amass knowledge of, the “Multiplications table". They can tell you that "2 x 2 = 4" because they have amassed that knowledge (it being included in the table). But when asked what is "1267 x 300", they can not respond correctly because that entry is not in their Multiplications table. To correctly answer such a question requires a true cognitive and analytical ability that is only encompassed in the next level... understanding. In computer parlance, most of the applications we use (modeling, simulation, etc.) exercise some type of stored knowledge.

Understanding... understanding is an interpolative and probabilistic process. It is cognitive and analytical. It is the process by which I can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between "learning" and "memorizing". People who have understanding can undertake useful actions because they can synthesize new knowledge, or in some cases, at least new information, from what is previously known (and understood). That is, understanding can build upon currently held information, knowledge and understanding itself. In computer parlance, AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge.

Wisdom... wisdom is an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.). It beckons to give us understanding about which there has previously been no understanding, and in doing so, goes far beyond understanding itself. It is the essence of philosophical probing. Unlike the previous four levels, it asks questions to which there is no (easily-achievable) answer, and in some cases, to which there can be no humanly-known answers period. Wisdom is therefore, the process by which we also distinguish, or judge, between right and wrong, good and bad. I personally believe that computers do not have, and will never have the ability to posses’ wisdom. Wisdom is a uniquely human state, or as I see it, wisdom requires one to have a soul, for it resides as much in the heart as in the mind. And a soul is something machines will never possess (or perhaps I should reword that to say, a soul is something that, in general, will never possess a machine).

The following diagram represents the transitions from data, to information, to knowledge, and finally to wisdom, and it is understanding that support the transition from each stage to the next. Understanding is not a separate level of its own.

• Data represents a fact or statement of event without relation to other things.

Ex: It is raining.• Information embodies the understanding of a relationship of

some sort, possibly cause and effect.Ex: The temperature dropped 15 degrees and then it started raining.

• Knowledge represents a pattern that connects & generally provides a high level of predictability as to what is described or what will happen next.

Ex: If the humidity is very high and the temperature drops substantially the atmospheres is often unlikely to be able to hold the moisture so it rains.

• Wisdom embodies more of an understanding of fundamental principles embodied within the knowledge that are essentially the basis for the knowledge being what it is. Wisdom is essentially systemic.

Ex: It rains because it rains. And this encompasses an understanding of all

the interactions that happen between raining, evaporation, air currents, temperature gradients, changes, and raining.

• References:

• Ackoff, R. L., "From Data to Wisdom", Journal of Applies Systems Analysis, Volume 16, 1989 p 3-9.

• Gadomski, Adam Maria, Information, Preferences and Knowledge, An Interesting Evolution in Thought

• Sharma, Nikhil, The Origin of the Data Information Knowledge Wisdom Hierarchy

DATABASE AND DATABASE MANAGEMENT SYSTEMS

Data is the key element that drives information systems of any organization. The objective of Management Information System is to transform data into meaningful management information. To make decisions and plan for the future, managers need information that originates from a database: the business Management Information System model is built on a database foundation: Database is the mortar of MIS which runs all sub information sub-systems of an organization.

Since the inception of electronic computers the most challenging tasks of the managers is Data Resource Management [DRM]. In DRM functions the organizations have faced a lot of inconvenience in using electronic media. The persistence and regular efforts of the IT industry resulted in the invention of new database management devices / technology as solutions for all types of inconvenience faced by the organizations. The database management systems software [DBMS/RDBMS], distributed databases, date warehousing and data mining, object-oriented database, web-based hypermedia database, technologies are the stage-by-stage growth of such inventions.

FILE MANAGEMENT METHODS An efficient information system provides users with timely, accurate, and

relevant information. This information is stored in manual or computer files. In computer based management information systems file we mean computer files. When files are properly maintained, users can easily access and retrieve the information they need.

A computer system organizes data in a hierarchy that starts with bits and bytes and progresses to fields, records, and databases. A Bit is a smallest unit of data [i.e., either 0 or 1] a computer can handle. A group of such bits viz 8 bits called a Byte, which represent a single character {i.e., A to Z, a to z, numbers, special characters, space, etc.,}. A grouping of characters into a word, (a group of words, or complete number, such as a person’s name , age, etc.,) is called a field. A group of related fields is called record. A group of record of the same type is called file.

A record describes an Entity. An entity is a person, place, thing, or event on which we maintain information. Each entity contains so many piece of information describing a particular entity such a piece of information is called attribute.

Every record in a file should contain at least one field that uniquely identifies that record so that the record can be retrieved, updated, or sorted. Such identifier field is called a KEY-FIELD. Finally a related file is organized into a database.

ILLUSTRATIVE FIGURE OF DATA HIERARCHY IN COMPUTER FILE MANAGEMENT

SYSTEMS HIERARCHY OF DATA EXAMPLE OF EMPLOYEE DATABASE DATABASE Employee - File Welfare Benefit File

Pay-Roll File File Emp_name ID no. Dept. Shift Address Ph.no Record 1. Gopal Ab. 09909 A II …B’lore 3354126

Field 1. Gopal { Name field}

Byte G 01110100(ASCII code of 1st letter of name field Gopal)

Bit 0 OPERATION REQUIRED FOR PROCESSING RECORDS IN A FILES ARE:

a. File creation, b. Locating a record, c. Adding a record, d. Deleting a record, e. Modifying a record

Emp_name ID no. Dept. Shift Address Ph.no 1. Gopal Ab. 09909 A II …B’lore 3354126 2. Ramu Cl. 67890 C I ..mysore 48765 3. Chandra Dx.09860 D G …B’lore. 2225760

LIMITATION OF FILE MANAGEMENTData Redundancy: It means the presence of duplicate data in multiple data files.

This occurs when different divisions, functional areas, and groups in an organization independently collect the same piece of information. The same piece of information collected and stored in different files for different applications has different meaning for programmers, and analysts work in isolation on different applications. So that creates lots of confusion.

Program-data dependence: Program-data dependence is the tight relationship between data stored in files and the specific programs required to update and maintain those files. Every computer program has to describe the location and nature of the data with which it works. Any change in data requires a change in all programs that access the data.

Lack of flexibility: The file management system can deliver routine scheduled reports after extensive programming efforts, but it cannot deliver ad hoc reports or respond to unanticipated information requirements in a timely fashion.

Poor security: Because there is little control or management of data, access to and dissemination of information are virtually out of control. What limits on access exist tend to be the result of habit and tradition, as well as of the sheer difficulty of finding information.

Lack of data sharing and availability: The lack of control over access to data in this confused environment does not make it easy for people to obtain information. Because pieces of information in different files and different parts of the organization cannot be related to one another, it is virtually impossible for information to be shared or accessed in a timely manner.

DATABASE & DATABASE MANAGEMENT SYSTEMS The DATABASE Technology can cut through many of the problems

created by file management systems. It can be defined as follows.

A collection of interrelated data stored together with controlled redundancy to serve one or more applications in optimal fashion; the data are stored so that they are independent of programs which use the data; a common and controlled approach is used in adding new data and modifying and retrieving existing data within the data base.

- Jerome Kanter “ Management Information Systems” – Third Edition- Prentice Hall of India private Ltd., New Delhi – 110 001. Pp.90- 127

“DATABASE is a collection of data organized to serve many applications efficiently by centralizing the data and minimizing redundant data. Rather than storing data in separate files for each application, data are stored physically to appear to users as being stored in only one location. A single database services multiple applications

- KENNETH C. LAUDON & JANE P. LAUDON “Essentials of Management Information Systems” Third Edition – pp. 199 –229.

The following figure illustrates an example for database for HUMAN RESOURCES MANAGEMENT.

INTEGRATED HUMAN RESOURCE MANAGEMENT

Employees: Name Personnel Address department Social security number Position Marital status Payroll: Hours Worked Payroll Pay rate department Gross pay Deductions Net pay Benefits: Benefits Life insurance department Group insurance Health care plan Provident fund Retirement benefits

Database Management Systems [ DBMS ]

Personnel application Programs

Payroll application Programs

Benefits application Programs

DATABASE & DATABASE MANAGEMENT SYSTEMS A database is a mechanized, formally defined,

centrally controlled collection of data in an organization. The data structure is physically organized and stored to promote share ability, availability, evoluability, and integrity.

– GORDON B. DAVIS & MARGRETHE H. OLSON “Management Information Systems” Third Edition – pp. 205-234

The database approach is made operational by a DATABASE

MANAGEMENT SYSTEMS {DBMS}, A SOFTWARE SYSTEM, WHICH

PERFORMS THE FUNCTIONS OF DEFINING, CREATING,

REVISING, AND CONTROLLING THE DATABASE. That is DBMS has

a specialized function to create and maintain a database and enable

individual business applications to extract the data they need without

having to create separate files of data definitions in computer

programs. The DBMS software provides facilities for retrieving data,

generating reports, revising data definitions, updating data, and

building applications.The DBMS ACTS AS AN INTERFACE BETWEEN

APPLICATION PROGRAMS AND THE PHYSICAL DATA FILES.

Conceptual Model of DBMS

Database query language

Database definitionDatabase creationDatabase redefinitionData restructureIntegrity controls

Database programming language interface

Applicationprogram

Database

As per the above Database Model there are 3 types of users 1. The Non-Programming Users: this users are not writing any

program to use the database. Usually an analyst or end user with special training. Programs ad hoc queries and reports using a database query languages.

2. The Programming Users: An applications programmer who does the analysis and programming of applications. Uses special database interface instructions to program application access to the database through the database management system. The instructions call the database management system to request data, perform up-datates, etc. the programming users can also use the database query language for special assignments.

3. The Database Administrators: The DBA uses special instructions and facilities of the database management system (a data definition language or DDL) to define, create, redefine, and restructure the database and to implement integrity controls.

Objectives of DATABASE & DATABASE MANAGEMENT SYSTEMS 1. Availability: Data should be made available for use by applications

(both current and future) and by queries.

2. Shareablility; Data items prepared by one application are available to all applications or queries. No data items are ‘owned’ by an application.

3. Evaluability: The database can evolve as application usage and query needs evolve.

4. Data Independence: The users of the database establish their view of the data and its structure without regard to the actual physical storage of the data.

5. Data Integrity: The database establishes a uniform high level of accuracy and consistency. Validation rules are applies by the daabase management system.

6. Reduced Redundancy: The presence of duplicate data in multiple

data files in file structure of collection of data is completely eliminated.

Three components of DBMS are :A Data Definition Language: A formal language used by the programmers to specify the content

and structure of the database. DDL defines each data element as it appears in the database before that data element is translated into the forms required by application programs.

A Data Manipulation language: This language is used in conjunction with some conventional third or fourth generation programming languages to manipulate the data in the database. This language contains commands that permit end users and programming specialists to extract data from the database to satisfy information requests and develop applications. The most prominent data manipulation language today is Structured Query Language [SQL]. The SQL is standard data manipulation language for relational database management systems (RDBMS).

A Data Dictionary: This is an automated or manual file, which includes definitions of data elements and data characteristics such as usage, physical representation, ownership (person/s responsible for maintaining the data in the organization), authorization, and security. Many data dictionaries can produce lists and reports of data utilization, groupings, program locations, and so on. A Data element represents a field. In simple data dictionary is a repository of information about data. It contains following information about data:

– The name of the data item.– A description of the data items.– Sources of data i.e., various sources of input.– Impact analysis i.e., users of the data including screens, reports, programs, and

organizational positions that access and use the data item.– Key words used for categorizing and searching for data item descriptions.

LOGICAL AND PHYSICAL VIEWS OF DATA:

DBMS separates logical and physical views of the data, relieving the programmer or end user from the task of understanding where and how the data are actually stored.

LOGICAL VIEW: It is a representation of data as they would appear (be perceived by ) to an application programmer or end user.

PHYSICAL VIEW: It shows how data are actually organized and structured on physical storage media.

Logical structure or model (i.e., relationship among data)

HIERARCHICAL DATA MODEL: this model presents data to users in a tree like structure. Example. IBM’s IMS (Information Management System). In this system a record is subdivided into segments that are connected to each other in one-to-many or parent-child relationships. Data are physically linked to one another by a series of pointers that form chains of related data segment.

NETWORK DATA MODEL: this model allows many-to-many relationships among records (i.e., the net-work model allows entry into a database at multiple points, because any data element or record can be related to any number of other data elements).

RELATIONAL DATA MODEL: this model represents all data in the database as simple two-dimensional tables called relations. In each table the rows (tuples) are unique records and the columns (attributes) are fields (data elements). The relational data model can relate data stored in one table to data in another as long as the two tables share a common data element (key field).

In a relational database, three basic operations are used to develop useful sets of data: viz., SELECT, PROJECT, & JOIN. The select operation creates a subset consisting of all records in the file that meet stated criteria. The join operation combines relational tables to provide the user with more information than is available in individual table. The project operation creates a subset consisting of columns in a table, permitting the user to create new tables that contain only the information required.

Resent Trends in Database

Billions of bytes of business-critical data are being created by many organisations’ computer systems daily, yet only small portion of them are used in business related analysis, which causes most companies are “data rich” but “information poor” because their ability to manipulate data and deliver information lags far behind the growth rate of data. To overcome such difficulty there is also a significant development in hardware, telecommunications, & database technologies, accompanied by increased rate of computer literacy of end-users. The following are some of the latest development in database technology.

Distributed Databases: A distributed database is one that is stored in more than one physical

location. Some parts of the database are stored physically in one location and other parts are stored and maintained in other locations. There are main two ways of distributing a database viz., 1.Replicated database. It provides duplicate of all data at all site this database is recommended if it is necessary for every location to have frequent access to the same data. 2. Partitioned database. In this method the database is divided into segments that are appropriate locations and those segments distributed only to those locations. Example the database may be partitioned along functional lines viz., Financial, Logistic, Human-resource-management, Manufacturing, Marketing, etc., Data may be kept at corporate office and relevant production and personnel data at each manufacturing plant and office site.

Partitioning may also be achieved along geographical lines. That is, all information- Financial, Logistic, Human-resource-management, Manufacturing, Marketing, etc., may be kept at each of the separate locations of an organization.

Many organizations with many locations partition the database hierarchically. Detailed data, such as payroll and sales data are kept close to their source-the local site. Regional and national locations receive increasingly less detailed summaries of the detailed data as these data are transmitted up through the organisation’s hierarchy.

Distributed database systems usually reduce costs because; they reduce transfer of data between remote sites and the organisation’s headquarters. This system may also provide organizations with faster response times for filling orders, answering customer requests, or providing mangers with information. However, distributed database systems also magnify the problems of databases. They compound the problems of control over the database, increase problems of security, for the database, increase data redundancy and the resulting danger to data integrity, and increase the need for more computer resources. Unless the distribution of a database is done very carefully, many of the advantages of having a database in the first place can be lost.

The increased power and use of microcomputers by managers and professionals have created additional problems for database administrators. When managers download data from a centralized database to their microcomputers, there is no longer a truly centralized database. Parts of the database are segmented and distributed to these microcomputers.

Because of the backlog of requests to be filled at many management information systems departments, other departments may become so frustrated that they decide to acquire their own minicomputers or microcomputers to provide their own information services. When this happens, additional files and databases are established throughout an organization, creating much of the same redundancy, inconsistency, and incompatibility.

An important type of distributed database system is called client/server systems.The distributed database & distributed processing has significantly increased the awareness of the

data as a key corporate resource and underscored the importance of its management that is Data Resource Management (DRM). The success of DRM function in distributed environment can manifest in several different ways. Success may be reflected by the degree to which preset DRM objectives are realized. The DRM objectives relate to improvements in efficiency and effectiveness of the DRM function. Such objectives include maintaining data integrity, accuracy, security, and availability; providing timely data; designing efficient data distribution strategies; enhancing operational efficiency; setting and enforcing standards; facilitating enhanced data sharing and reducing redundancy; developing strategic data plans; and training information systems personnel and end-users, among others.

OBJECT-ORIENTED AND HYPERMEDIA DATABASESConventional database management systems are not well suited for

handling graphic-based or multimedia application such as drawings, images, photographs, voice, and full-motion video etc. An Object Oriented database, on the other hand, stores the data and procedures as objects that can be automatically retrieved and shared. The object oriented database management is becoming popular because they can be used to manage the various multimedia components or Java applets used in Web applications, which typically integrate pieces of information from a variety of sources. OODBMS are also useful for storing data types such as recursive data (An example would be parts within parts as found in manufacturing applications.).

The hypermedia database: This is an approach to DBMS that organizes data as a network of nodes linked in any pattern established by the user; the nodes can contain text, graphics, sound, full-motion video, or executable programs.

Although object oriented and hypermedia databases can store more complex types of information than relational DBMS, they are relatively slow compared with relational DBMS for processing large numbers of transactions. Hybrid object – relational systems are now available to provide capabilities of both object-oriented and relational DBMS. A hybrid approach can be accomplished in three different ways: by using tools that offer object-oriented access to relational DBMS, by using object-oriented extensions to existing relational DBMS, or by using object-relational database management system.

MULTI-DIMENSIONAL DATA ANALYSIS Some times managers

require to analyse data by viewing data from multi-dimensional angle for example Cipla Pharmaceutical Industry manufacturers different products & distributes such drugs available at different Cities and Towns. If manager (CEO) needs to know actual sales by products for each Cities or Towns and also want to compare them with budgeted sales requires multi-dimensional data analysis for Ex: for the products viz., CEFORPROX, LARPOSE, FERTOMID, LOMAC, ASTHALIN etc distributed in Cities viz., BANGALORE, MYSORE, DHARWAD, DAVANGERE & Towns viz., Kolar, Malur, Kengeri, Anekal etc., is shown as follow.

Multi-dimensional Data analysis

Area wise Budgeted & Actuals For All Products: BUDGETED: 1.Quantity 2.Price 6 3.Amount 5 ACTUALS:4. Quantity 4 5. Price 3 6. Amounts 2 PRODUCTS: 1 CEFORPROX LARPOSE FERTOMID LOMAC ASTHALIN CITIES & TOWNS

BA

NG

AL

OR

E

MY

SO

RE

DH

AR

WA

D

DA

VA

NG

ER

E

KO

LA

R

MA

LU

R

HO

SK

OT

E

KE

NG

ER

I

Budgeted &

Actual for each product

To provide this type of information organizations can use either specialized multi-dimensional database or a tool that creates multi-dimensional views of data in relational databases. Another name used for multi-dimensional data analysis is On-Line Analytical Processing [OLAP]. OLAP refers to capability for manipulating and analyzing large volume of data from multiple perspectives.

DATA WAREHOUSES Data warehousing is a multi-billon dollar industry that sprang up

during the 1990s. Today most fortune 1000 firms and many smaller ones now have their own data warehouses. The industry resulted from a realization that conventional on-line transactions at a base are not adequate for decision support, data-mining, and customer relationship applications (i.e., e-CRM). To meet the requirement of DSS / GDSS, EIS, data-mining, e-CRM, Supply-chain relationship management & some other similar type of application data warehousing are emerged.

Data warehousing and Internet are the two key technologies that offer potential solutions for managing corporate data. Data warehousing liberates information and the Internet makes it easy and less costly to access information from anywhere at anytime.

Definition of data warehouseData warehouse is defined as “a subject-oriented, integrated, non-volatile, time-

variant collection of data organized to support management needs.” 2.7

A data warehouse differs from operational databases that mainly support the daily business transactions and management information to managers in the form of periodical reports. A data warehouse collects data from multiple (both internal & external) sources, and stores it in a fashion that allows end users to have faster easier, and more flexible access to key information and the data in the data warehouse are standardized (cleaned) and are available for anyone to access as needed but cannot be altered.

WILLIAM. H. INMAN “Data warehouse, Marts, Metadata, OLAP/ROLAP & Data mining Glossary” Management Accounting –Castelluccio, (4:78), 1996 pp-59-69.

Data marts: The organization build enterprise-wide data warehouses

where a central data warehouse serve the entire organization, or they can create smaller, decentralized data warehouses called data marts. A data mart is a subset of a data warehouse in which a summarized highly focused portion of the organization data is placed in a separate database for a specific population of users (functional or department wise users).

Software tools needed for data warehouse1. Warehouse Construction Software: This software is required to extract

relevant data both from operation databases & external sources to make sure the data are “clean” (free from error), transform the data into a useable form and load the data into the data warehouse. Warehouse construction software is available from IBM, Information Builders, Platinum Technology etc.,

2. Warehouse operation software: This software required for storing data and managing the data warehouse. This is accomplished by DBMSs such as Computer Associates CA-Ingres, IBM’s DB2, Oracle, Sybase Specialized warehouse management software is offered by Hewlett-Packard, IBM, Information Builders, NCR, Red-Brick, & Others.

3. Warehouse Access and Analysis Software: The widest variety of software tools is available in the warehouse access and analysis area, Information catalog tool, such as Platinum Technology’s Platinum Repository; tell the user what is in the warehouse. Reporting tools enable a user to produce customized reports from the data warehouse, perhaps on a regular basis. Information Builder’s FOCUS, a 4GL etc., widely used reporting tool.

Query tool, such as Brio-Technology’s Brio Query Enterprise, make it easy for a user to query the warehouse. For more sophisticated data analysis, specialized data-mining tools such as Thinking Machines Darwin, are available. Visualising the data may be important, using a tool such as SAS Institute’s SAS/Insight, and presenting the data through an executive Information System (EIS) can be done using Show Business Software’s Show Business EIS or a similar tools. The Figure on the next slide illustrates the data warehouse environment.

Data warehouse

model with

component of

Data

warehouse

. A Structural Model showing Components of Data Warehouse

War

ehou

se

Ope

rati

on s

oftw

are

Warehouse Construction Software

Info

rmat

ion

D

irec

tory

Warehouse Access & Analysis software

*Que

ries

&

* Rep

orts

*O

LAP

*Dat

a m

inin

g.

Data

w

are

house

Int

erne

t&

Intr

anet

Inte

rnal

dat

a

Ext

erna

l Dat

a

Ope

rati

onal

&

His

tori

cal

data

.

Ext

erna

l dat

a

Dat

a-ba

nk

Dat

a so

urc

es f

or

dat

a w

areh

ouse

Com

pon

ents

of

Dat

a W

areh

ouse

Dat

a m

art

-4

Dat

a m

art

-2

Dat

a m

art

-3

Dat

a m

art -

5

Dat

a m

art

-6

Dat

a m

art

-7

Dat

a m

art

-1

A data warehouse is user driven. It provides greater flexibility in using data than traditional information systems. Orr (1996) identifies eight interconnected parts of data warehouse architecture (DWA). The eight parts represent the overall structure of data, communication, processing, and presentation in a data warehouse environment. Among them, the information access layer is the layer that end users deal with when using the data warehouse. It includes the hardware and software that constructs the user interface with the data warehouses. The major goal of the user interface is to make the raw data available easily and seamlessly to the end users. Currently, most organizations implement data warehousing in either a standalone or traditional client / server environment, and most data warehousing applications implement their information access layer using applications with graphic user interface (GUI) running on desktop computers.

Drawbacks of data warehousing environment 1. The Client / Server infrastructure is expensive to establish and maintain

2 Using one user interface is no longer sufficient, because of increasing number of Mobile users. The Gartner Group estimated that by year 2003, 137 million business users worldwide will regularly work outside the boundaries of the enterprise and without continuous LAN or high-speed WAN connections (Reinaner, 1998).But, the number is increased in many folds as on today. Providing information and decision support to these users becomes an inevitable challenge to organization today.

3 System compatibility is always a problem for the traditional client / server environment: deploying multiple computing platforms enormously increases the cost of administration and maintenance.

4 Today supply-chain management (SCM) has becoming increasingly important. Successful SCM requires an organization to invest heavily in interenterprise coordination, distribution and channel partnerships and customer responsiveness. Therefore, limiting the information access to a small number of highly trained specialists within an organisation is no longer sufficient. Information access must be extended to include an organisation’s internal users, suppliers, partners, and customers.

Web-Based Data warehousing Web based data warehousing involves accessing, analyzing and distributing the information

extracted from a data warehouse through Internet, intranet, or extranet using a Web browser as the user interface. Because of the explosive development in the data warehousing and Web related technology available today. Web-based data warehousing is starting to gain more and more popularity among organizations. The most important contribution of Web-based data warehousing is assisting an organization to create innovative relationships with its suppliers, partners and customers and helps organization to have better supply chain relations to react rapidly to opportunities.

ARCHITECTURE OF WEB-BASED DATA WAREHOUSING The Architecture of Web-based data warehousing is three-tiered and includes client, Web server, and

application server. On the client side, all user needs is an Internet connection and Web-browser (preferably Java or C# -enabled). The client computer can be of any platform, including PCs, Macintoshes, UNIX machines, network computers, and so on. The Internet / intranet / extranet is the communication medium between client and servers. On the server side, a Web ser is used to manage the inflow and outflow of information between client and server. Both a data warehouse and an application server, which houses down-loadable Java applications, Common Gateway Interfaces (CGI) programs, and other applications that are utilized to manipulate the data in the data warehouse, back it. The query results are displayed on Web pages that are constructed on the fly or by Java-based data visualization tools. The three tiered Web-based data warehousing is exhibited as bellow

2.7 LEI- DACHEN AND MARK N. FROLICK “Web- Based data warehousing “(Information

Systems Management Spring –2000), pp.

Ref …..EI- DACHEN AND MARK N. FROLICK “Web- Based data warehousing “(Information Systems Management Spring –2000),

The Three-Tier Architecture of Web-Based Data Warehousing:

Client

Application Server

Data warehouse

Inte

rnet

In

tran

et, /

Ext

rane

t

Web-Server

Web - Pages

Web-Browser

The Web-based data warehousing allows end users to use Web browsers as a user interface in order to access and manipulate data. Such applications can be Internet, Intranet, or Extranet based. Web-based data warehousing offers following advantages:

1. In recent years, end users have become more experienced in using the Internet for both business and leisure purposes: this phenomenon makes the Web browser a easy-to-use interface for users of all levels of computer skill.

2. Web-based data warehousing reduces the establishment and management cost by offering a thin-client solution. The thin-client solution moves most of the application processing to the server: therefore, there is a reduced need for hardware and software cost and support on the desktop. It brings the power of many computers into one relatively simple desktop device connected to network. Pre-installation of the software is not required, in many instances, and future upgrading and maintenance are performed only on the server, which serves as a device with enormous amount of resources.

3. The Internet provides a way to distribute data to a large number of users in a low-cost and platform- independent environment.

Data MiningData mining forms the most crucial part of

data warehousing as it acts like a catalyst that is responsible for sifting gold out of useless pebbles. It analyses all the information stored in databases and extracts only the valuable data for deriving productive results.2.8 In data mining, the data in a data warehouse are processed to identify key factors and trends in historical patterns of business activity. This can be used to help managers make decisions about strategic changes in business operations to gain competitive advantages in the marketplace.

Ref:….2.8 SWASTI OHRI “Data Warehousing – Mining holds the Key to Success” ‘i.t’ Bureau, US. The complete Magazine on Information Technology-Sept-99.

Managerial considerations for Data Resource Management

Many different end users and a variety of application programs can access the data in database / data warehouse, the data in database/ warehouse is considered as a corporate resource. Hence it is necessary to manage and control such corporate resource. The elements of data resource management are briefly explained as follows:

1. Data Administration: It is a special organizational function for managing the organisation’s data resources, concerned with information policy, data planning, maintenance of data dictionaries, and data quality standards. The fundamental principle of data administration is that all data are the property of the organization as a whole. Data cannot belong exclusively to any one business area or organizational unit. All data are to be made available to any group that requires them to fulfill its mission. Hence the organization needs to formulate an information policy that specifies its rules for sharing, disseminating, acquiring, standardizing, classifying, and inventorying information throughout the organization. Information policy lays out specific procedures and accountabilities, specifying which organizational units share information: where information can be distributed: and who has responsibility for updating and maintaining the information.

2. Data Planning and Modeling Methodology: The organizational interests served by the DBMS are much broader than those in the traditional file environment: therefore the organization requires enterprise-wide planning for data. An Enterprise analysis, which addresses the information requirements of the entire organisation (as opposed to the requirements of individual applications), is needed to develop databases. The purpose of enterprise analysis is to identify the key entities, attributes, and relationships that constitute the organisation's data.

3. Database Technology, Management and Users: Databases require new software and a new staff specially trained in DBMS techniques, as well as new management structures. Most corporations develop a database design and management group within the corporate information system division that is responsible for the more technical and operational aspect of managing data. The functions it performs are called database administration. This group does the following:

• Defines and organizes database structure and content• Develop Security Procedures and safeguard the database• Develops database documentation• Maintains the database management software

In close cooperation with users, the design group establishes the physical database, the logical relations among elements, and the access rules and procedures.

A database serves a wider community of users than traditional systems. Relational systems with fourth generation query languages permit employees who are not computer specialists to access large databases. In addition, users include trained computer specialists and non-specialist, the database helps to optimize access for non-specialists & more resources must be devoted to training end users. Professional systems workers must be retrained in the DBMS language, DBMS application development procedures, and new software practices.

Benefits and limitations database management • The database management approach provides managerial end

users with several important benefits. Database management reduces the duplication of data and integrates data so that multiple programs and users can access them. Programs are not dependent on the format of the data and the type of secondary storage hardware being used. Users are provided with an inquiry / response and reporting capability that allows them to easily obtain information they need without having to write computer programs. Computer programming is simplified, because programs are not dependent on either the logical format of the data or their physical storage location. Finally, the integrity and security of the data stored in databases can be increased, since database management system software, a data dictionary, and a database administrator function control access to data and modification of the database.

• The limitations of database management arise from its increased technological complexity. Developing large databases of complex types and installing a DBMS can be difficult and expensive. More hardware capability is required, since storage requirements for the organisation’s data, overhead control data, and the DBMS programs are greater. Longer processing times may result from these additional data and software. Finally, if an organisation relies on centralized databases, its vulnerability to errors, fraud, and failures is increased. Yet problems of inconsistency of data can arise if a distributed database approach is used. Therefore, the security and integrity of organisation’s databases are major concerns of an organisation’s data resource management effort.

42

Database Management System and

Its Significance in Management

Any Questions ?

Thank You !