This document discusses database management systems and decision support systems. It begins by outlining some of the challenges with traditional information processing approaches, such as data redundancy and lack of flexibility. It then introduces database management systems as a solution, highlighting their ability to reduce redundancy and integrate related data. Key features of DBMS like logical data structures and relational models are explained. The document also covers decision support systems, noting that they provide interactive support during decision making by using analytical models, specialized databases, and the insights of decision makers. Major components of DSS like model bases are outlined.

1. Introduction
Perhaps the most important challenge facing information systems is to provide users
with timely and versatile access to data stored in computer files. In a dynamic
business environment there are many unanticipated needs for information. Often the
basic underlying data satisfy these information needs are contained in computer files
but cannot be accessed and output in a suitable format on a timely basis. Database
management systems have the potential to meet this challenge.
Traditional Approach to Information Processing
Data Redundancy
Data redundancy increases data editing, maintenance, and storage costs. In
addition, data stored on two master files (which should in theory be identical) are
often different for good reason, but such differences inevitably create confusion.
Lack of Data Integration
To change a file structure (e.g.: add a new field) in a traditional programming language like
COBOL
• Make a new file with new field old file structure
• Copy data from old file in to new file
• Delete old file
• Rename new file as old file’s name
A B
copy
Old file
New file (extended
fields)
Delete A
B A
Rename
A
Delete A

2. Program/Data Dependence
Lack of Flexibility
The information-retrieval capabilities of most traditional systems are limited to
predetermined requests for data. Therefore, the system produces information in the
form of scheduled reports and queries which it has been previously programmed to
handle. If management needs unanticipated data, the information can perhaps be
provided if it is in the files of the system. Extensive programming is often involved.
Thus, by the time the programming is completed, the information may no longer be
required or useful. This problem has long plagued information systems. Management
knows that a particular piece of information can be produced on a one-time basis, but
the expense and time involved are generally prohibitive. Ideally, information
processing should be able to mix related data elements from several different files
and produce information with a fast turnaround to service unanticipated requests for
information.
Name Surname Tel.No
Name Surname Tel.No Birth Day
New File
Old File

3. Database Management Systems (ORACLE, SYBASE, MS-SQL, MySQL, NOSQL,
PROGRESS)
It is very important to select a proper programming tool when building a MIS.
A DBMS should be:
• Effective
• Fast
• Easy to communicate to other DBMSs.
• Cheap
• Safe (reliable)
• Good security functions
• Upgrade utilities (an old version program working with new ones) -immigration
• Independent to hardware (after preparing a program with your IBM
compatible PC, it should be working in a Unix operating system platform)
Case study: In a big Turkish company
• Marketing department enter customer orders to the computer
• They print out the customer orders and give this report to production planning
department.
• Production planning department reenter these data to their database for
planning.
Logical Data-Base Structures
Logical Data-Base Structures
Two key features of a DBMS are the ability to reduce data redundancy and the ability
to associate related data elements such as related fields and records. These
functions are accomplished through the use of keys, embedded pointers, and linked
lists.
Tree Structures
Figure illustrates student data in a tree (hierarchical) structure. The lower part of the
figure shows the data fields in each record. A tree structure consists of records
(often called segments) that are linked to related records in a one-to-many

4. relationship. Each record may have only one parent but an unlimited number of
children. The top record is called the root. As shown in figure, each student can
attend many semesters and take many courses in each semester. However, each
course is tied to a single semester, and the data in each semester record are in turn
tied to a single student.
Figure: A tree structure (Example: product tree, bill of materials)
Fields in the student record
Fields in the semester record
Fields in the Course Record
Network Structures
A network structure allows many-to-many relationship among the nodes in the
structure. Figure illustrates a network structure between courses and students. Each
student can enroll in several classes, each class has many students.
The physical storage as well as data linkage in a network structure involve
embedded pointers in each record as in a tree structure. There are several
schemes for using pointers with network structures. One is similar to the scheme
CAR 307
STUDENT
ENGINE-FALL ELECTRICAL
SYST. SPRING
VALVES Course record
ENG1020
Course record
ACCT2111
Course record
MSCI2011
Student
Number
Student
Name
Addres
s
To-Date
Grade-Point
Average
Date
Admitte
d
Major
Semeste
r
Semeste
r Fees
Fees
Paid
Semester
Grade-Point
Average
Course
Number
Course
Grade

5. discussed under tree structures, where each course record (for example, course
1) contains the address of the first student in the course, and then the first student
record, in turn, contains the address of the second student in the course, and so
on, thereby forming a linked list.
Relational Structures
Most business data have traditionally been organized in the form of simple tables
with only columns and rows. In a relational DBMS, these tables are called
relations. This data structure is known as the relational model, since it is based on
mathematical theory of relations. One of the greatest advantages of relational
model is its conceptual simplicity. The relational or tabular model of data is used
in a large variety of applications, ranging from your weekly shopping list to the
annual report of the world’s largest corporation. Most people are familiar with the
relational model as table. But the relational model does use some unfamiliar
terminology. What we have come to know as a file is called either a table or
relation. Each row in the table is called a tuple (rhymes with couple). A tuple is the
same as a record in regular file terminology. The columns of the table are known
as attributes and they are equivalent to fields within records. Instead of using the
formal relational terminology of relations, tuples, and attributes we will use the
more familiar files, records, and fields as do most real-world relational database
systems.

6. Stock-master
Stock-code Stock-name Unit price
3000 ABC AD 20
3001 XYZ AD 100
Depot-master
Depot no Depot name Depot manager
1 İSTANBUL AD
2 ANKARA UC
Stock-status
Stock-code Depot no Quantity
3000 1 300
3001 2 200
3001 3 50
3001 4 10
Materials inventory weekly Report
Stock
Code
Stock
Name
Depot
No
Depot
Name
Quantity Unit Price Amount
3000 ABC 1 İstanbul 300 AD 20 6,000
3001 XYZ 2 Ankara 200 AD 100 20,000
3001 XYZ 3 Yalova 50 AD 100 5,000
3001 XYZ 4 İzmit 10 AD 100 1,000
Enterprise Resource Planning: The Business Backbone
What is ERP?

7. ERP is the technological backbone of e-business, an enterprise-wide transaction
framework with links into sales order processing, inventory management and control,
production and distribution planning, and finance.
Benefits and challenges of ERP
ERP systems can generate significant business benefits for a company. Many other
companies have found major business value in their use of ERP in several basic
ways:
• Quality and efficiency: ERP creates a framework for integrating and
improving a company’s internal business processes that result in significant
improvements in the quality and efficiency of customer service, production,
and distribution.
• Decreased costs: Many companies report significant reductions in
transaction processing costs and hardware, software, and IT support staff
compared to the nonintegrated legacy systems that were replaced by their
new ERP systems.
• Decision support: ERP provides vital cross-functional information on
business performance quickly to managers to significantly improve their ability
to make better decisions in a timely manner across the entire business
enterprise.
• Enterprise agility: Implementing ERP systems breaks down many former
departmental and functional walls or “silos” of business processes, information
systems, and information resources. This results in more flexible
organizational structures, managerial responsibilities, and work roles, and
therefore a more agile and adaptive organization and workforce that can more
easily capitalize on new business opportunities.
Causes of ERP failures
What have been the major causes of failure in ERP projects? In almost every case,
the business managers and IT professionals of these companies underestimated the
complexity of the planning, development, and training that were needed to prepare
for a new ERP system that would radically change their business processes and
information systems. Failure to involve affected employees in the planning and

8. development phases and change management programs, or trying to do too much
too fast in the conversion process, were typical causes of failed ERP projects.
Insufficient training in the new work tasks required by the ERP system, and failure to
do enough data conversion and testing, were other causes of failure. In many cases,
ERP failures were also due to over-reliance by company or IT management on the
claims of ERP software vendors or the assistance of prestigious consulting firms
hired to lead the implementation. The following experiences of companies that did it
right give us a helpful look at what is needed for a successful ERP implementation.
ERP - 2.ppt
sap 1.ppt
Trends in ERP
Today, ERP is still evolving – adapting to developments in technology and the
demands of the market. Four important trends are shaping ERP’s continuing
evolution: improvements in integration and flexibility, extensions to e-business
applications, a broader reach to new users, and the adoption of Internet
technologies.
(B2B: BUSINESS TO BUSINESS, B2C: BUSINESS TO
CUSTOMERS/CONSUMERS)
Customer Relationship Management: The Business Focus
What is CRM?
Managing the full range of the customer relationship involves two related objectives:
one, to provide the organization and all of its customer-facing employees with a
single, complete view of every customer at every touch point and across all channels;
and, two, to provide the customer with a single, complete view of the company and
its extended channels.
Major application components of a CRM system:
• Contact and account management
• Sales
• Marketing and fulfillment
• Customer service and support
• The performance of marketing & sales personnel

9. • Retention and loyalty programs
(DATA WAREHOUSE, DATA MINING)
Salesforce.Com, Sugarcrm
SUPPLY CHAIN MANAGEMENT: THE BUSINESS NETWORK
What is SCM?
Legacy supply chains are clogged with unnecessary steps and redundant stockpiles.
For instance, a typical box of breakfast cereal spends an incredible 104 days getting
from factory to supermarket, struggling its way through an unbelievable maze of
wholesalers, distributors, brokers, and consolidators, each of which has a
warehouse. The e-commerce opportunity lies in the fusing of each company’s
internal system to those of its suppliers, partners, and customers. This fusion forces
companies to better integrate inter-enterprise supply chain processes to improve
manufacturing efficiency and distribution effectiveness.
Unilever Beer Game (trick) www.masystem/beergame
book order by using amazon.com
GM & TOYOTA # OF PURCHASING DEPT. PERSONNEL
ERP DSS-8-SC-ERP.ppt
MRP : MATERIALS REQUIREMENT PLANNING
MRP II: MANUFACTURING RESOURCES PL.
Electronic Data Interchange
Electronic data interchange (EDI) was one of the earliest uses of information
technology for supply chain management. EDI involves the electronic exchange of
business transaction documents over the Internet and other networks between
supply chain trading partners (organizations and their customers and suppliers). Data
representing a variety of business transaction documents (such as purchase orders,
invoices, requests for quotations, and shipping notices) are automatically exchanged
between computers using standard document message formats. Typically, EDI
software is used to convert a company’s own document formats into standardized
EDI formats as specified by various industry and international protocols. Thus, EDI is

10. an example of the almost complete automation of an e-commerce supply chain
process. And EDI over the Internet, using secure virtual private networks, is a
growing B2B e-commerce application.
Figure: The supply chain management functions and potential benefits offered by the SCM module in
the mySAP e-business software suite.
SCM Functions
Planning
Supply chain design • Optimize network of suppliers, plants, and distribution centers
Collaborative demand
and supply planning
• Develop an accurate forecast of customer demand by sharing
demand and supply forecasts instantaneously across multiple tiers
• Internet-enable collaborative scenarios, such as collaborative
planning, forecasting, and replenishment (CPFR), and vendor-
managed inventory
Execution
Materials management • Share accurate inventory and procurement order information
• Ensure materials required for production are available in the place
at the right time
• Reduce raw material spending, procurement costs, safety stocks,
and raw material and finished goods inventory
Collaborative
manufacturing
• Optimize plans and schedules while considering resource, material,
and dependency constraints
Collaborative fulfillment • Commit to delivery dates in real time
• Fulfill orders from all channels on time with order management,
transportation planning, and vehicle scheduling
• Support the entire logistics process, including picking, packing,
shipping and delivery in foreign countries
Supply chain event
management
• Monitor every stage of the supply chain process, form price
quotation to the moment the customer receives the product, and
receive alerts when problems arise
Supply chain
performance
management
• Report key measurements in the supply chain, such as filling rates,
order cycle times, and capacity utilization
Trends in SCM
The supplier-facing applications arena will see the continued growth of public as well
as private networks that transform linear and inflexible supply chains into nonlinear
and dynamic fulfillment networks. Supplier-facing applications will also evolve along
another dimension: from automation and integration of supply chains to collaborative
sourcing, planning, and design across their supplier networks.
Figure: Stages in the use of supply chain management

11. Decision Support Systems
Decision support systems are computer-based information systems that provide
interactive information support to managers and business professionals during the
decision-making process.
Information
Sharing,
Product/Sales Data
Sourcing Help
Logistics,
Order Fulfillment
Order Management,
Inventory
Management,
Resource Allocation
Systems Use and
Integration
Collaborative
Marketing,
Sales and Service
SCM Optimization,
Collaborative
Design and
Delivery
SCM Stage 1
Current supply chain
improvement
Supply chain, e-commerce
loosely coupled
SCM Stage 2
Intranet/extranet links to
trading partners
Supplier network expansion
SCM Stage 3
Collaborative planning and
fulfillment
Extranet and exchange-
based collaboration

12. Decision support systems use (1) analytical models, (2) specialized databases, (3) a
decision maker’s own insights and judgments, and (4) an interactive, computer-
based modeling process to support the making of semi-structured and
unstructured business decisions. See Figure: Comparing decision support
systems and management information systems
Example: An example might help at this point. Sales managers typically rely on
management information systems to produce sales analysis reports. These
reports contain sales performance figures by product line, salesperson, sales
region, and so on. A decision support system, on the other hand, would also
interactively show a sales manager the effects performance of changes in variety
of factors (such as promotion expense and salesperson compensation). The
DSS could use several criteria (such as expected gross margin and market
share) to evaluate and rank several alternative combinations of sales
performance factors
Figure: Comparing decision support systems and management information systems.
Management Information
Systems
Decision Support
Systems
Decision support provided Provide information about
the performance of the
organization
Provide information and
decision support
techniques to analyze
specific problems or
opportunities
Information form and
frequency
Periodic, exception,
demand, and push
reports and responses
Interactive inquiries and
responses
Information format Pre-specified, fixed format Ad hoc, flexible, and
adaptable format
Information processing
methodology
Information produced by
extraction and
manipulation of
business data
Information produced by
analytical modeling of
business data.

13. Therefore, DSS are designed to be ad hoc, quick-response systems that are initiated
and controlled by business decision makers. Decision support systems are thus
able to directly support the specific types if decision and the personal decision-
making styles and needs of individual executives, managers, and business
professionals.
DSS Components
Unlike management information systems, decision support systems rely on model
bases as well as databases as vital system resources. A DSS model base is a
software component that consists of models used in computational and analytical
routines that mathematically express relationships among variables. For
example, a spreadsheet program might contain models that express simple
accounting relationships among variables such as Revenue – Expenses = Profit.
Or a DSS model base could include models and analytical techniques used to
express much more complex relationships. For example, it might contain linear
programming models, multiple regression forecasting models, and capital
budgeting present value models. Such models may be stored in the form of
spreadsheet models or templates, or statistical and mathematical programs and
program modules.
DSS software packages can combine model components to create integrated models
that support specific types of decisions. DSS software typically contains built-in
analytical modeling routines and also enables you to build your own models.
Many DSS packages are now available in microcomputer and Web-enables
versions. Of course, electronic spreadsheet packages also provide some of the
model building (spreadsheet models) and analytical modeling (what-if and goal-
seeking analysis) offered by more powerful DSS software.

14. DSS Packages
Retail: Information Advantage and Unisys offer the Category Management Solution
Suite, an OLAP (OnLine Analytical Processing) decision support system and
industry-specific data model.
Insurance: Computer Associates offers RiskAdvisor, an insurance risk decision
support system whose data model stores information in insurance industry
specific tables designed for optimal query performance.
Telecom: NCR and SABRE Decision Technologies have joined forces to create the
NCR Customer Retention program for the communications industry including
data marts for telephone companies to use for decision support in managing
customer loyalty, quality of service, network management, fraud, and marketing.
Using Decision Support Systems
Using a decision support system involves an interactive analytical modeling
process. For example, using a DSS software package for decision support may result
in a series of displays in response to alternative what-if changes entered by the
manager. This differs from the demand responses of management information
systems, since decision makers are not demanding prespecified information. Rather,
they are exploring possible alternatives. Thus, they do not have to specify their
information needs in advance. Instead, they use the DSS to find the information they
need to help them make a decision. That is the essence of the decision support
system concept.
Figure: Activities and examples of the major types of analytical modeling
Type of Analytical Modeling Activities and Examples
What-if analysis Observing how changes to selected
variables affect other variables.
Example: What if we cut advertising by
10%? What would happen to sales?
Sensitivity analysis Observing how repeated changes to a
single variable affect other variables.
Example: Let’s cut advertising by $1000
repeatedly so we can see its relationship

15. to sales.
Goal-seeking analysis Making repeated changes to selected
variable reaches a target value.
Example: Let’s try increases in
advertising until sales reach $1 million.
Optimization analysis Finding an optimum value for selected
variables, given certain constraints.
Example: What’s the best amount of
advertising to have, given our budget and
choice of media?
Using a decision support system involves four basic types of analytical modeling
activities: (1) what-if analysis, (2) sensitivity analysis, (3) goal-seeking analysis, and
(4) optimization analysis. Let’s briefly look at each type of analytical modeling that
can be used for decision support. See Figure: Activities and examples of the major
types of analytical modeling.
Executive Information Systems
Executive information systems (EIS) are information systems that combine many of
the features of management information systems and decision support systems.
When they were first developed, their focus was on meeting the strategic information
needs of top management. Thus, the first goal of executive information systems was
to provide top executives with immediate easy access to information about a firm’s
critical success factors (CSFs), that is, key factors that are critical to accomplishing
an organization’s strategic objectives. For example, the executives of a retail store
chain would probably consider factors such as its e-commerce versus traditional
sales results, or its product line mix to be critical to its survival and success.
Implementing Business Systems
Evaluating hardware, software, and services
How do companies evaluate and select hardware, software and IT services, such as
those shown in the Figure? Large companies may require suppliers to present bids
and proposals based on system specifications developed during the design stage of
systems development. Minimum acceptable physical and performance
characteristics and all government agencies formalize these requirements by listing

16. them in a document called an RFP (request for proposal) or RFQ (request for
quotation). Then they send the RFP and RFQ to appropriate vendors, who use it as
the basis for preparing a proposed purchase agreement.
Companies may use a scoring system of evaluation when there are several
competing proposals for a hardware or software acquisition. They give each
evaluation factor a certain number of maximum possible points. Then they assign
each competing proposal points for each factor, depending on how well it meets the
user’s specifications. Scoring evaluation factors for several proposals helps organize
and document the evaluation process. It also spotlights the strengths and
weaknesses of each proposal. Another method for selecting is AHP:
http://www.ufukcebeci.com/EditModule.aspx?tabid=42&mid=44&def=News
%20Article%20View&ItemId=7
YOU PLAN TO OPEN A PIZZA RESTAURANT . DEFINE HARDWARE, SOFTWARE AND MIS &
SUBMODULES NEEDED.
Figure: An example of the implementation process activities and time lines for a company installing an
intranet-based employee benefits system in its human resource management department.
Intranet Implementation Activities Month 1 Month 2 Month 3 Month 4
Acquire and install server hardware and software
Train administrators
Acquire and install browser software
Acquire and install publishing software
Train benefits employees on publishing software
Convert benefits manuals and add revisions
Create Web-based tutorials for the intranet
Hold rollout meetings
Whatever the claims of hardware manufacturers and software suppliers, the
performance of hardware and software must be demonstrated and evaluated.
Independent hardware and software information services (such as Datapro and
Auerbach) may be used to gain detailed specification information and evaluations.
Other users are frequently the best source on information needed to evaluate the
claims of manufacturers and suppliers. That’s why Internet newsgroups established
to exchange information about specific software or hardware vendors and their

17. products have become one of the best sources for obtaining up-to-date information
about the experiences of users of the products.
Large companies frequently evaluate proposed hardware and software by requiring
the processing of special benchmark test programs and test data. Benchmarking
simulates the processing of typical jobs on several computers and evaluates their
performances. Users can then evaluate test results to determine which hardware
device or software package displayed the best performance characteristics.
Hardware evaluation factors
When you evaluate the hardware needed by a new business application, you should
investigate specific physical and performance characteristics for each computer
system or peripheral component to be acquired. Specific questions must be
answered concerning many important factors. Ten of these hardware evaluation
factors and questions are summarized in the figure.
Figure: A summary of ten major hardware evaluation factors.
Hardware Evaluation Factors Rating
Performance
What are the speed, capacity, and throughput?
Cost
What is its lease or purchase price? What will be its cost of operations and maintenance?
Reliability
What are the risk of malfunction and its maintenance requirements?
What are its error control and diagnostic features?
Compatibility
Is it compatible with existing hardware and software? Is it compatible with hardware and
software provided by competing suppliers?
Technology
In what year of its product life cycle is it? Does it use a new untested technology or does it
run the risk of obsolescence?
Ergonomics
Has it been “human factors engineered” with the user in mind? Is it user-friendly, designed to
be safe, comfortable, and easy to use?
Connectivity
Can it be easily connected to wide area and local area networks that use different types of
network technologies and bandwidth alternatives?
Scalability
Can it handle the processing demands of a wide range of end users, transactions, queries,
and other information processing requirements?
Software
Is the system and application software available that can best use this hardware?
Support
Are the services required to support and maintain it available?
Overall Rating

18. Notice that there is much more to evaluating hardware than determining the fastest
and cheapest computing device. For example, the question of obsolescence must be
addressed by making a technology evaluation. The factor of ergonomics is also very
important. Ergonomic factors ensure that computer hardware and software are user-
friendly, that is, safe, comfortable, and easy to use. Connectivity is another important
evaluation factor, since so many network technologies and bandwidth alternatives
are available to connect computer systems to the Internet, intranet, and extranet
networks.
Software evaluation factors
You should evaluate software according to many factors that are similar to those
used for hardware evaluation. Thus, the factors of performance, cost, reliability,
availability, compatibility, modularity, technology, ergonomics, and support should be
used to evaluate proposed software acquisitions. In addition, however, the software
evaluation factors summarized in figure must also be considered. You should
answer the questions they generate in order to properly evaluate software
purchases. For example, some software packages are notoriously slow, hard to use,
bug-filled, or poorly documented. They are not good choice, even if offered at
attractive prices.
Figure: A summary of ten major software evaluation factors.
Software Evaluation Factors Rating
Quality
Is it bug-free, or does it have many errors in its program code?
Efficiency
Is the software a well-developed system of program code that does not use much CPU time,
memory capacity, or disk space?
Flexibility
Can it handle our e-business processes easily, without major modification?
Security
Does it provide control procedures for errors, malfunctions, and improper use?
Connectivity
Is it Web-enabled so it can easily access the Internet, intranets, and extranets, on its own, or
by working with Web browsers or other network software?
Language
Is it written in a programming language that is familiar to our own software developers?
Documentation
Is the software well documented? Does it include help screens and helpful software agents?
Hardware
Does existing hardware have the features required to best use this software?
Other factors
What are its performance, cost, reliability, availability, compatibility, modularity, technology,
ergonomics, scalability, and support characteristics?

19. Overall Rating
Evaluating IS services
Most suppliers of hardware and software products and many other firms offer a
variety of IS services to end users and organizations. Examples include assistance
during e-commerce website development, installation or conversion of new hardware
and software, employee training, and hardware maintenance. Some of these
services are provided without cost by hardware manufacturers and software
suppliers.
Other types of IS services needed by a business can be outsourced to an outside
company for a negotiated price. For example, systems integrators take over
complete responsibility for an organization’s computer facilities when an organization
outsources its computer operations. They may also assume responsibility for
developing and implementing large systems development projects that involve many
vendors and subcontractors. Value-added resellers (VARs) specialize in providing
industry-specific hardware, software, and services from selected manufacturers.
Many other services are available to end users, including systems design, contract
programming, and consulting services. Evaluation factors and questions for IS
services are summarized in the figure.
Figure: Evaluation factors for IS services.
Evaluation Factors for IS Services Rating
Performance
What has been their past performance in view of their past promises?
Systems development
Are website and other e-business developers available? What are their quality and cost?
Maintenance
Is equipment maintenance provided? What are its quality and cost?
Conversion
What systems development and installation services will they provide during the conversion
period?
Training
Is the necessary training of personnel provided? What are its quality and cost?
Backup (timed backup)
Are similar computer facilities available nearby for emergency backup purposes?
Accessibility
Does the vendor provide local or regional sites that offer sales, systems development, and
hardware maintenance services? Is a customer support center at the vendor’s website
available? Is a customer hot line provided?
Business position
Is the vendor financially strong, with good industry market prospects?
Hardware
Do they provide a wide selection of compatible hardware devices and accessories?

20. Software
Do they offer a variety of useful e-business software and application packages?
Overall Rating
ASP: Application Service Provider
Other implementation activities
 How to design forms: DESIGN A PURCHASING (CUSTOMER) ORDER
FORM
 How to code materials:
• Code must be unique.
• First use main groups, Then the sub groups
• Code considering extentions in time for material/product. (Code should
be open-ended)
• Don’t use /,:, %, ; and other specific characters
• Don’t use roman characters (XII, V)
• If the code is too long, it is hard to remember (TALKING CODES)
• If possible, use national and int. compatible standards such as barcodes,
etc.
DE 31030, FE 43002
BAR CODE CONTROL:
100315421004 3
1+0+0+3…. = 21  2+1 = 3
Testing, documentation, and training are the keys to successful implementation of a
new business system.
Testing
System testing may involve testing website performance, testing and debugging
software, and testing new hardware. An important part of testing is the review of
prototypes of displays, reports, and other output. Prototypes should be reviewed by

21. end users of the proposed systems for possible errors. Of course, testing should not
occur only during the system’s implementation stage, but throughout the system’s
development process. For example, you might examine and critique prototypes of
input documents, screen displays, and processing procedures during the systems
design stage. Immediate end user testing is one of the benefits of a prototyping
process.
They say 30 % of software in NASA is written in FORTRAN.
If it works, don’t touch.
Software testing
Testing methods[edit]
Static vs. dynamic testing[edit]
There are many approaches available in software testing. Reviews, walkthroughs,
or inspections are referred to as static testing, whereas actually executing programmed code with
a given set of test cases is referred to as dynamic testing. Static testing is often implicit, as
proofreading, plus when programming tools/text editors check source code structure or compilers
(pre-compilers) check syntax and data flow as static program analysis. Dynamic testing takes
place when the program itself is run. Dynamic testing may begin before the program is 100%
complete in order to test particular sections of code and are applied to discrete functions or
modules. Typical techniques for this are either using stubs/drivers or execution from
a debugger environment.
Static testing involves verification, whereas dynamic testing involves validation. Together they
help improve software quality. Among the techniques for static analysis, mutation testing can be
used to ensure the test cases will detect errors which are introduced by mutating the source
code.
The box approach[edit]
Software testing methods are traditionally divided into white- and black-box testing. These two
approaches are used to describe the point of view that a test engineer takes when designing test
cases.
White-box testing[edit]
Main article: White-box testing
White-box testing (also known as clear box testing, glass box testing, transparent box testing and
structural testing, by seeing the source code) tests internal structures or workings of a program,
as opposed to the functionality exposed to the end-user. In white-box testing an internal
perspective of the system, as well as programming skills, are used to design test cases. The

22. tester chooses inputs to exercise paths through the code and determine the appropriate outputs.
This is analogous to testing nodes in a circuit, e.g. in-circuit testing (ICT).
While white-box testing can be applied at the unit, integration and system levels of the software
testing process, it is usually done at the unit level. It can test paths within a unit, paths between
units during integration, and between subsystems during a system–level test. Though this
method of test design can uncover many errors or problems, it might not detect unimplemented
parts of the specification or missing requirements.
Techniques used in white-box testing include:
• API testing – testing of the application using public and private APIs (application
programming interfaces)
• Code coverage – creating tests to satisfy some criteria of code coverage (e.g., the test
designer can create tests to cause all statements in the program to be executed at least
once)
• Fault injection methods – intentionally introducing faults to gauge the efficacy of testing
strategies
• Mutation testing methods
• Static testing methods
Code coverage tools can evaluate the completeness of a test suite that was created with any
method, including black-box testing. This allows the software team to examine parts of a system
that are rarely tested and ensures that the most important function points have been tested.
[22]
Code coverage as a software metric can be reported as a percentage for:
• Function coverage, which reports on functions executed
• Statement coverage, which reports on the number of lines executed to complete the
test
• Decision coverage, which reports on whether both the True and the False branch of a
given test has been executed
100% statement coverage ensures that all code paths or branches (in terms of control flow)
are executed at least once. This is helpful in ensuring correct functionality, but not sufficient
since the same code may process different inputs correctly or incorrectly.
Black-box testing[edit]
Main article: Black-box testing
Black box diagram
Black-box testing treats the software as a "black box", examining functionality without any
knowledge of internal implementation, without seeing the source code. The testers are only
aware of what the software is supposed to do, not how it does it.[23]
Black-box testing methods
include: equivalence partitioning, boundary value analysis, all-pairs testing, state transition
tables, decision table testing, fuzz testing, model-based testing, use casetesting, exploratory
testing and specification-based testing.
Specification-based testing aims to test the functionality of software according to the
applicable requirements.[24]
This level of testing usually requires thorough test cases to be
provided to the tester, who then can simply verify that for a given input, the output value (or
behavior), either "is" or "is not" the same as the expected value specified in the test case.
Test cases are built around specifications and requirements, i.e., what the application is

23. supposed to do. It uses external descriptions of the software, including specifications,
requirements, and designs to derive test cases. These tests can be functional or non-
functional, though usually functional.
Specification-based testing may be necessary to assure correct functionality, but it is
insufficient to guard against complex or high-risk situations.[25]
One advantage of the black box technique is that no programming knowledge is required.
Whatever biases the programmers may have had, the tester likely has a different set and
may emphasize different areas of functionality. On the other hand, black-box testing has
been said to be "like a walk in a dark labyrinth without a flashlight."[26]
Because they do not
examine the source code, there are situations when a tester writes many test cases to check
something that could have been tested by only one test case, or leaves some parts of the
program untested.
This method of test can be applied to all levels of software
testing: unit, integration, system and acceptance. It typically comprises most if not all testing
at higher levels, but can also dominate unit testing as well.
Visual testing[edit]
The aim of visual testing is to provide developers with the ability to examine what was
happening at the point of software failure by presenting the data in such a way that the
developer can easily find the information she or he requires, and the information is expressed
clearly.[27][28]
At the core of visual testing is the idea that showing someone a problem (or a test failure),
rather than just describing it, greatly increases clarity and understanding. Visual testing
therefore requires the recording of the entire test process – capturing everything that occurs
on the test system in video format. Output videos are supplemented by real-time tester input
via picture-in-a-picture webcam and audio commentary from microphones.
Visual testing provides a number of advantages. The quality of communication is increased
drastically because testers can show the problem (and the events leading up to it) to the
developer as opposed to just describing it and the need to replicate test failures will cease to
exist in many cases. The developer will have all the evidence he or she requires of a test
failure and can instead focus on the cause of the fault and how it should be fixed.
Visual testing is particularly well-suited for environments that deploy agile methods in their
development of software, since agile methods require greater communication between
testers and developers and collaboration within small teams.[citation needed]
Ad hoc testing and exploratory testing are important methodologies for checking software
integrity, because they require less preparation time to implement, while the important bugs
can be found quickly. In ad hoc testing, where testing takes place in an improvised,
impromptu way, the ability of a test tool to visually record everything that occurs on a system
becomes very important in order to document the steps taken to uncover the bug.[clarification needed]
[citation needed]
Visual testing is gathering recognition in customer acceptance and usability testing, because
the test can be used by many individuals involved in the development process.[citation needed]
For
the customer, it becomes easy to provide detailed bug reports and feedback, and for program
users, visual testing can record user actions on screen, as well as their voice and image, to
provide a complete picture at the time of software failure for the developers.
Further information: Graphical user interface testing
Grey-box testing[edit]
Main article: Gray box testing

24. Grey-box testing (American spelling: gray-box testing) involves having knowledge of internal
data structures and algorithms for purposes of designing tests, while executing those tests at
the user, or black-box level. The tester is not required to have full access to the software's
source code.[29][not in citation given]
Manipulating input data and formatting output do not qualify as grey-
box, because the input and output are clearly outside of the "black box" that we are calling
the system under test. This distinction is particularly important when conducting integration
testing between two modules of code written by two different developers, where only the
interfaces are exposed for test.
However, tests that require modifying a back-end data repository such as a database or a log
file does qualify as grey-box, as the user would not normally be able to change the data
repository in normal production operations.[citation needed]
Grey-box testing may also include reverse
engineeringto determine, for instance, boundary values or error messages.
By knowing the underlying concepts of how the software works, the tester makes better-
informed testing choices while testing the software from outside. Typically, a grey-box tester
will be permitted to set up an isolated testing environment with activities such as seeding
a database. The tester can observe the state of the product being tested after performing
certain actions such as executing SQL statements against the database and then executing
queries to ensure that the expected changes have been reflected. Grey-box testing
implements intelligent test scenarios, based on limited information. This will particularly apply
to data type handling, exception handling, and so on.[30]
Testing levels[edit]
There are generally four recognized levels of tests: unit testing, integration testing,
component interface testing, and system testing. Tests are frequently grouped by where they
are added in the software development process, or by the level of specificity of the test. The
main levels during the development process as defined by the SWEBOK guide are unit-,
integration-, and system testing that are distinguished by the test target without implying a
specific process model.[31]
Other test levels are classified by the testing objective.[31]
There are two different levels of tests from the perspective of customers: low-level testing
(LLT) and high-level testing (HLT). LLT is a group of tests for different level components of
software application or product. HLT is a group of tests for the whole software application or
product.[citation needed]
Unit testing[edit]
Main article: Unit testing
Unit testing, also known as component testing, refers to tests that verify the functionality of a
specific section of code, usually at the function level. In an object-oriented environment, this
is usually at the class level, and the minimal unit tests include the constructors and
destructors.[32]
These types of tests are usually written by developers as they work on code (white-box style),
to ensure that the specific function is working as expected. One function might have multiple
tests, to catch corner cases or other branches in the code. Unit testing alone cannot verify the
functionality of a piece of software, but rather is used to ensure that the building blocks of the
software work independently from each other.
Unit testing is a software development process that involves synchronized application of a
broad spectrum of defect prevention and detection strategies in order to reduce software
development risks, time, and costs. It is performed by the software developer or engineer
during the construction phase of the software development lifecycle. Rather than replace
traditional QA focuses, it augments it. Unit testing aims to eliminate construction errors before

25. code is promoted to QA; this strategy is intended to increase the quality of the resulting
software as well as the efficiency of the overall development and QA process.
Depending on the organization's expectations for software development, unit testing might
include static code analysis, data-flow analysis, metrics analysis, peer code reviews, code
coverage analysis and other software verification practices.
Integration testing[edit]
Main article: Integration testing
Integration testing is any type of software testing that seeks to verify the interfaces between
components against a software design. Software components may be integrated in an
iterative way or all together ("big bang"). Normally the former is considered a better practice
since it allows interface issues to be located more quickly and fixed.
Integration testing works to expose defects in the interfaces and interaction between
integrated components (modules). Progressively larger groups of tested software
components corresponding to elements of the architectural design are integrated and tested
until the software works as a system.[33]
Component interface testing[edit]
The practice of component interface testing can be used to check the handling of data
passed between various units, or subsystem components, beyond full integration testing
between those units.[34][35]
The data being passed can be considered as "message packets"
and the range or data types can be checked, for data generated from one unit, and tested for
validity before being passed into another unit. One option for interface testing is to keep a
separate log file of data items being passed, often with a timestamp logged to allow analysis
of thousands of cases of data passed between units for days or weeks. Tests can include
checking the handling of some extreme data values while other interface variables are
passed as normal values.[34]
Unusual data values in an interface can help explain unexpected
performance in the next unit. Component interface testing is a variation of black-box testing,
[35]
with the focus on the data values beyond just the related actions of a subsystem
component.
System testing[edit]
Main article: System testing
System testing, or end-to-end testing, tests a completely integrated system to verify that the
system meets its requirements.[36]
For example, a system test might involve testing a logon
interface, then creating and editing an entry, plus sending or printing results, followed by
summary processing or deletion (or archiving) of entries, then logoff.
Operational Acceptance testing[edit]
Main article: Operational acceptance testing
Operational Acceptance is used to conduct operational readiness (pre-release) of a product,
service or system as part of a quality management system. OAT is a common type of non-
functional software testing, used mainly in software development and software
maintenance projects. This type of testing focuses on the operational readiness of the system
to be supported, and/or to become part of the production environment. Hence, it is also
known as operational readiness testing (ORT) or Operations readiness and
assurance (OR&A) testing. Functional testing within OAT is limited to those tests which are
required to verify the non-functional aspects of the system.

26. In addition, the software testing should ensure that the portability of the system, as well as
working as expected, does not also damage or partially corrupt its operating environment or
cause other processes within that environment to become inoperative.[37]
Bugzilla.
Bugzilla is a Web-based general-purpose bugtracker and testing tool originally
developed and used by the Mozilla project, and licensed under the Mozilla Public
License.
Released as open source software by Netscape Communications in 1998, it has
been adopted by a variety of organizations for use as a bug tracking system for
both free and open source software and proprietary projects and products. Bugzilla is
used, among others, by Mozilla Foundation, Wikimedia
Foundation, WebKit, NASA, Yahoo!, GNOME, KDE, Red Hat and Novell.[2]
Documentation
Developing good user documentation is an important part of the implementation
process. Sample data entry display screens, forms, and reports are good examples
of documentation. When computer-aided systems engineering methods are used,
documentation can be created and changed easily since it is stored and accessible
on disk in a system repository. Documentation serves as a method of communication
among the people responsible for developing, implementing, and maintaining a
computer-based system. Installing and operating a newly designed system or
modifying an established application requires a detailed record of that system’s
design. Documentation is extremely important in diagnosing errors and making
changes, especially if the end users or systems analyst who developed a system are
no longer with the organization.
Training
Training is a vital implementation activity. IS personnel, such as user consultants,
must be sure that end users are trained to operate a new e-business system or its
implementation will fail. Training may involve only activities like data entry, or it may

27. also involve all aspects of the proper use of a new system. In addition, managers and
end users must be educated in how the new technology impacts the company’s
business operations and management. This knowledge should be supplemented by
training programs for any new hardware devices, software packages, and their use
for specific work activities.
- Seminars
o General
- On the job training
o Specific
Conversion methods
The initial operation of a new business system can be a difficult task. This typically
requires a conversion process from the use of a present system to the operation of
a new or improved application. Conversion methods can soften the impact of
introducing new information technologies into an organization. Four major forms of
system conversion are illustrated in figure.
Figure: Conversion methods.
Old System
New System
Old System New System
Old System New System
Parallel
Pilot
Phased

28. Old System
New System
Conversions can be
• Parallel
• Phased
• Pilot
• Plunge or direct cutover
Conversions can be done on a parallel basis, whereby both the old and the new
systems are operating until the project development team and end user management
agree to switch completely over to the new system. It is during this time that the
operations and results of both systems are compared and evaluated. Errors can be
identified and corrected, and the operating problems can be solved before the old
system is abandoned. Installation can also be accomplished by a direct cutover or
plunge to a newly developed system.
Conversion can also be done on a phased basis, where only parts of a new
application or only a few departments, branch offices, or plant locations at a time are
converted. A phased conversion allows a gradual implementation process to take
place within an organization. Similar benefits accrue from using a pilot conversion,
were one department or other work site serves as a test site. A new system can be
tried out at this site until developers feel it can be implemented through out the
organization.
IS maintenance
Once a system is fully implemented and is being used in business operations, the
maintenance function begins. System maintenance is the monitoring, evaluating,
and modifying of operational business systems to make desirable or necessary
improvements. For example, the implementation of a new system usually results in
the phenomenon known as the learning curve. Personnel who operate and use the
system will make mistakes simply because they are not familiar with it. Though such
Plunge (direct)

29. errors usually diminish as experience is gained with a new system, they do point out
areas where a system may be improved.
Maintenance is also necessary for other failures and problems that arise during the
operation of a system. End users and information systems personnel then perform a
troubleshooting function to determine the causes of and solutions to such problems.
The maintenance activity includes a post-implementation review process to ensure
that newly implemented systems meet the business objectives established for them.
Errors in the development or use of a system must be corrected by the maintenance
process. This includes a periodic review or audit of a system to ensure that it is
operating properly and meeting its objectives. This audit is in addition to continually
monitoring a new system for potential problems or necessary changes.
Maintenance also includes making modifications to an established system due to
changes in the business organization or the business environment. For example,
new tax legislation, company reorganizations, and new e-commerce initiatives may
require major changes to current business system.
E-learning;
ITU Hospital Management Certificate Program
http://www.enoctaakademi.com
Online analytical processing
From Wikipedia, the free encyclopedia
(Redirected from OLAP)
In computing, online analytical processing, or OLAP (pronounced /ˈoʊlæp/) is an approach to swiftly answer multi-
dimensional analytical queries.[1]
OLAP is part of the broader category of business intelligence, which also
encompasses relational reporting and data mining.[2]
Typical applications of OLAP include business reporting for
sales, marketing,management reporting, business process management (BPM)[3]
, budgeting and forecasting, financial
reporting and similar areas, with new applications coming up, such as agriculture [4]
. The term OLAP was created as a
slight modification of the traditional database term OLTP (Online Transaction Processing).[5]
Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad-hoc queries
with a rapid execution time. They borrow aspects of navigational databases and hierarchical databases that are faster
than relational databases.[6]

30. The output of an OLAP query is typically displayed in a matrix (or pivot) format. The dimensions form the rows and
columns of the matrix; the measures form the values.
Data warehouse
From Wikipedia, the free encyclopedia
Data Warehouse Overview
In computing, a data warehouse or enterprise data warehouse (DW, DWH, or EDW) is a database used
for reporting and data analysis. It is a central repository of data which is created by integrating data from
multiple disparate sources. Data warehouses store current as well as historical data and are used for
creating trending reports for senior management reporting such as annual and quarterly comparisons.
The data stored in the warehouse are uploaded from the operational systems (such as marketing, sales
etc., shown in the figure to the right). The data may pass through an operational data store for additional
operations before they are used in the DW for reporting.
The typical ETL-based data warehouse uses staging, integration, and access layers to house its key
functions. The staging layer or staging database stores raw data extracted from each of the disparate
source data systems. The integration layer integrates the disparate data sets by transforming the data from
the staging layer often storing this transformed data in an operational data store (ODS) database. The
integrated data are then moved to yet another database, often called the data warehouse database, where
the data is arranged into hierarchical groups often called dimensions and into facts and aggregate facts.
The combination of facts and dimensions is sometimes called a star schema. The access layer helps users
retrieve data.[1]
A data warehouse constructed from an integrated data source systems does not require ETL, staging
databases, or operational data store databases. The integrated data source systems may be considered to
be a part of a distributed operational data store layer. Data federation methods or data
virtualization methods may be used to access the distributed integrated source data systems to consolidate
and aggregate data directly into the data warehouse database tables. Unlike the ETL-based data

31. warehouse, the integrated source data systems and the data warehouse are all integrated since there is no
transformation of dimensional or reference data. This integrated data warehouse architecture supports
the drill down from the aggregate data of the data warehouse to the transactional data of the integrated
source data systems.
Data warehouses can be subdivided into data marts. Data marts store subsets of data from a warehouse.
This definition of the data warehouse focuses on data storage. The main source of the data is cleaned,
transformed, cataloged and made available for use by managers and other business professionals for data
mining, online analytical processing, market research and decision support (Marakas & O'Brien 2009).
However, the means to retrieve and analyze data, to extract, transform and load data, and to manage
the data dictionary are also considered essential components of a data warehousing system. Many
references to data warehousing use this broader context. Thus, an expanded definition for data
warehousing includes business intelligence tools, tools to extract, transform and load data into the
repository, and tools to manage and retrieve metadata.
Data mart
From Wikipedia, the free encyclopedia
A data mart is the access layer of the data warehouse environment that is used to get data out to the
users. The data mart is a subset of the data warehouse that is usually oriented to a specific business line or
team. In some deployments, each department or business unit is considered the owner of its data mart
including all the hardware, software and data.[1]
This enables each department to use, manipulate and
develop their data any way they see fit; without altering information inside other data marts or the data
warehouse. In other deployments where conformed dimensions are used, this business unit ownership will
not hold true for shared dimensions like customer, product, etc.
The related term spreadmart describes the situation that occurs when one or more business analysts
develop a system of linked spreadsheets to perform a business analysis, then grow it to a size and degree
of complexity that makes it nearly impossible to maintain.
The primary use for a data mart is business intelligence (BI) applications. BI is used to gather, store, access
and analyze data. The data mart can be used by smaller businesses to utilize the data they have
accumulated. A data mart can be less expensive than implementing a data warehouse, thus making it more
cost effective for the small business. A data mart can also be set up in much less time than a data
warehouse, being able to be set up in less than 90 days. Since most small businesses only have use for a
small number of BI applications, the low cost and quick set up of the data mart makes it a suitable method
for storing data.[2]

32. Big data
From Wikipedia, the free encyclopedia
A visualization created by IBM of Wikipedia edits. At multiple terabytes in size, the text and images of Wikipedia are a classic
example of big data.
In information technology, big data[1][2]
is a collection of data sets so large and complex that it becomes
difficult to process using on-hand database management tools. The challenges include capture, curation,
storage,[3]
search, sharing, analysis,[4]
and visualization. The trend to larger data sets is due to the additional
information derivable from analysis of a single large set of related data, as compared to separate smaller
sets with the same total amount of data, allowing correlations to be found to "spot business trends,
determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time
roadway traffic conditions."[5][6][7]
As of 2012, limits on the size of data sets that are feasible to process in a reasonable amount of time were
on the order of exabytes of data.[8][9]
Scientists regularly encounter limitations due to large data sets in many
areas, including meteorology, genomics,[10]
connectomics, complex physics simulations,[11]
and biological
and environmental research.[12]
The limitations also affect Internet search, finance andbusiness informatics.
Data sets grow in size in part because they are increasingly being gathered by ubiquitous information-
sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras,
microphones, radio-frequency identification readers, and wireless sensor networks.[13][14]
The world's
technological per-capita capacity to store information has roughly doubled every 40 months since the
1980s;[15]
as of 2012, every day 2.5 quintillion (2.5×1018
) bytes of data were created.[16]
Big data is difficult to work with using relational databases and desktop statistics and visualization
packages, requiring instead "massively parallel software running on tens, hundreds, or even thousands of
servers".[17]
What is considered "big data" varies depending on the capabilities of the organization managing
the set. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need

33. to reconsider data management options. For others, it may take tens or hundreds of terabytes before data
size becomes a significant consideration."[18]
Agile software development
From Wikipedia, the free encyclopedia
Agile software development poster
Generic diagram of an agile methodology for software development
Agile software development is a group of software development methods based on iterative and
incremental development, where requirements and solutions evolve through collaboration between self-
organizing, cross-functional teams. It promotes adaptive planning, evolutionary development and delivery,
a time-boxed iterative approach, and encourages rapid and flexible response to change. It is a conceptual
framework that promotes foreseen interactions throughout the development cycle. The Agile
Manifesto[1]
introduced the term in 2001.

34. BALANCED SCORECARD
AI: ARTIFICIAL INTELLIGENCE
http://easydiagnosis.com/ expert system
Recommender system
From Wikipedia, the free encyclopedia
(Redirected from Recommendation systems)
Recommender systems or recommendation systems (sometimes replacing "system" with a synonym
such as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or
'preference' that user would give to an item (such as music, books, or movies) or social element
(e.g. people or groups) they had not yet considered, using a model built from the characteristics of an item
(content-based approaches) or the user's social environment (collaborative filtering approaches).[1][2]
Recommender systems have become extremely common in recent years. A few examples of such systems:
• When viewing a product on Amazon.com, the store will recommend additional items based on a
matrix of what other shoppers bought along with the currently selected item.[3]
• Pandora Radio takes an initial input of a song or musician and plays music with similar
characteristics (based on a series of keywords attributed to the inputted artist or piece of music). The
stations created by Pandora can be refined through user feedback (emphasizing or deemphasizing
certain characteristics).
• Netflix offers predictions of movies that a user might like to watch based on the user's previous
ratings and watching habits (as compared to the behavior of other users), also taking into account the
characteristics (such as the genre) of the film.
http://p1m1.com/solutions/ DECISION SUPPORT SYSTEMS
SAS is a software suite developed by SAS Institute for advanced analytics, business intelligence, data management,
and predictive analytics. Statistical analysis Software
http://ecommerce-software-review.toptenreviews.com/

36. 2015:
https://www.facebook.com/BigThinkdotcom/videos/1015408123301852/