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this is 1nf

Picture 2nf

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Informally, a relational database table $(the computerized representation of a relation) is often described as “normalized” if it is in the third normal form (3NF). Most 3NF tables are free of insertion, update, and deletion anomalies, i.e. in most cases 3NF tables adhere to BCNF, 4NF, and 5NF (but typically not 6NF).

A standard piece of database design guidance is that the designer should begin by fully normalizing the design, and selectively denormalize only in places where doing so is absolutely necessary to address performance issues However, some modeling disciplines, such as the dimensional modeling approach to data warehouse design, explicitly recommend non-normalized designs, i.e. designs that in large part do not adhere to 3NF.

Objective of normalization

A basic objective of the first normal form defined by Codd in 1970 was to permit data to be queried and manipulated using a “universal data sub-language” grounded in first-order logi^[(SQL is an example of such a data sub-language, albeit one that Codd regarded as seriously flawed. Querying and manipulating the data within an unnormalized data structure, such as the following non-1NF representation of customers' credit card transactions, involves more complexity than is really necessary:

To each customer there corresponds a repeating group of transactions. The automated evaluation of any query relating to customers' transactions therefore would broadly involve two stages:

1. Unpacking one or more customers' groups of transactions allowing the individual transactions in a group to be examined, and
2. Deriving a query result based on the results of the first stage

For example, in order to find out the monetary sum of all transactions that occurred in October 2003 for all customers, the system would have to know that it must first unpack the Transactions group of each customer, then sum the Amounts of all transactions thus obtained where the Date of the transaction falls in October 2003.

One of Codd's important insights was that this structural complexity could always be removed completely, leading to much greater power and flexibility in the way queries could be formulated (by users and applications) and evaluated (by the DBMS). The normalized equivalent of the structure above would look like this:

Now each row represents an individual credit card transaction, and the DBMS can obtain the answer of interest, simply by finding all rows with a Date falling in October, and summing their Amounts. All of the values in the data structure are on an equal footing: they are all exposed to the DBMS directly, and can directly participate in queries, whereas in the previous situation some values were embedded in lower-level structures that had to be handled specially. Accordingly, the normalized design lends itself to general-purpose query processing, whereas the unnormalized design does not.

The objectives of normalization beyond 1NF were stated as follows by Codd:

1. To free the collection of relations from undesirable insertion, update and deletion dependencies;

2. To reduce the need for restructuring the collection of relations as new types of data are introduced, and thus increase the life span of application programs;

3. To make the relational model more informative to users;

4. To make the collection of relations neutral to the query statistics, where these statistics are liable to change as time goes by.

a deletion anomaly. All information about Dr. Giddens is lost when he temporarily ceases to be assigned to any courses.

When an attempt is made to modify (update, insert into, or delete from) a table, undesired side-effects may follow. Not all tables can suffer from these side-effects; rather, the side-effects can only arise in tables that have not been sufficiently normalized. An insufficiently normalized table might have one or more of the following characteristics:

• The same information can be expressed on multiple rows; therefore updates to the table may result in logical inconsistencies. For example, each record in an "Employees' Skills" table might contain an Employee ID, Employee Address, and Skill; thus a change of address for a particular employee will potentially need to be applied to multiple records (one for each of his skills). If the update is not carried through successfully—if, that is, the employee's address is updated on some records but not others—then the table is left in an inconsistent state. Specifically, the table provides conflicting answers to the question of what this particular employee's address is. This phenomenon is known as an update anomaly.
• There are circumstances in which certain facts cannot be recorded at all. For example, each record in a "Faculty and Their Courses" table might contain a Faculty ID, Faculty Name, Faculty Hire Date, and Course Code—thus we can record the details of any faculty member who teaches at least one course, but we cannot record the details of a newly-hired faculty member who has not yet been assigned to teach any courses. This phenomenon is known as an insertion anomaly.
• There are circumstances in which the deletion of data representing certain facts necessitates the deletion of data representing completely different facts. The "Faculty and Their Courses" table described in the previous example suffers from this type of anomaly, for if a faculty member temporarily ceases to be assigned to any courses, we must delete the last of the records on which that faculty member appears. This phenomenon is known as a deletion anomaly.

[inimize redesign when extending the database structure

When a fully normalized database structure is extended to allow it to accommodate new types of data, the pre-existing aspects of the database structure can remain largely or entirely unchanged. As a result, applications interacting with the database are minimally affected.

Make the data model more informative to users

Normalized tables, and the relationship between one normalized table and another, mirror real-world concepts and their interrelationships.

Avoid bias towards any particular pattern of querying

Normalized tables are suitable for general-purpose querying. This means any queries against these tables, including future queries whose details cannot be anticipated, are supported. In contrast, tables that are not normalized lend themselves to some types of queries, but not others.

Background to normalization: definitions

• Functional dependency: Attribute B has a functional dependency on attribute A (i.e., A → B) if, for each value of attribute A, there is exactly one value of attribute B. If value of A is repeating in tuples then value of B will also repeat. In our example, Employee Address has a functional dependency on Employee ID, because a particular Employee ID value corresponds to one and only one Employee Address value. (Note that the reverse need not be true: several employees could live at the same address and therefore one Employee Address value could correspond to more than one Employee ID. Employee ID is therefore not functionally dependent on Employee Address.) An attribute may be functionally dependent either on a single attribute or on a combination of attributes. It is not possible to determine the extent to which a design is normalized without understanding what functional dependencies apply to the attributes within its tables; understanding this, in turn, requires knowledge of the problem domain. For example, an Employer may require certain employees to split their time between two locations, such as New York City and London, and therefore want to allow Employees to have more than one Employee Address. In this case, Employee Address would no longer be functionally dependent on Employee ID.

Another way to look at the above is by reviewing basic mathematical functions:

Let F(x) be a mathematical function of one independent variable. The independent variable is analogous to the attribute A. The dependent variable (or the dependent attribute using the lingo above), and hence the term functional dependency, is the value of F(A); A is an independent attribute. As we know, mathematical functions can have only one output. Notationally speaking, it is common to express this relationship in mathematics as F(A) = B; or, B → F(A).

There are also functions of more than one independent variable—commonly, this is referred to as multivariable functions. This idea represents an attribute being functionally dependent on a combination of attributes. Hence, F(x,y,z) contains three independent variables, or independent attributes, and one dependent attribute, namely, F(x,y,z). In multivariable functions, there can only be one output, or one dependent variable, or attribute.

Trivial functional dependency

A trivial functional dependency is a functional dependency of an attribute on a superset of itself. {Employee ID, Employee Address} → {Employee Address} is trivial, as is {Employee Address} → {Employee Address}.

Full functional dependency

An attribute is fully functionally dependent on a set of attributes X if it is

functionally dependent on X, and

· not functionally dependent on any proper subset of X. {Employee Address} has a functional dependency on {Employee ID, Skill}, but not a full functional dependency, because it is also dependent on {Employee ID}.

Transitive dependency

A transitive dependency is an indirect functional dependency, one in which X→Z only by virtue of X→Y and Y→Z.

Multivalued dependency

A multivalued dependency is a constraint according to which the presence of certain rows in a table implies the presence of certain other rows.

Join dependency

A table T is subject to a join dependency if T can always be recreated by joining multiple tables each having a subset of the attributes of T.

Superkey

A superkey is an attribute or set of attributes that uniquely identifies rows within a table; in other words, two distinct rows are always guaranteed to have distinct superkeys. {Employee ID, Employee Address, Skill} would be a superkey for the “Employees’ Skills” table; {Employee ID, Skill} would also be a superkey.

Candidate key

A candidate key is a minimal superkey, that is, a superkey for which we can say that no proper subset of it is also a superkey. {Employee Id, Skill} would be a candidate key for the “Employees’ Skills” table.

Non-prime attribute

A non-prime attribute is an attribute that does not occur in any candidate key. Employee Address would be a non-prime attribute in the “Employees’ Skills” table.

Primary key

Most DBMSs require a table to be defined as having a single unique key, rather than a number of possible unique keys. A primary key is a key which the database designer has designated for this purpose.

Normal forms

The normal forms (abbrew. NF) of relational database theory provide criteria for determining a table’s degree of vulnerability to logical inconsistencies and anomalies. The higher the normal form applicable to a table, the less vulnerable it is to inconsistencies and anomalies. Each table has a “highest normal form” (HNF): by definition, a table always meets the requirements of its HNF and of all normal forms lower than its HNF; also by definition, a table fails to meet the requirements of any normal form higher than its HNF.

The normal forms are applicable to individual tables; to say that an entire database is in normal form n is to say that all of its tables are in normal form n.

The Normal Forms

The database community has developed a series of guidelines for ensuring that databases are normalized. These are referred to as normal forms and are numbered from one (the lowest form of normalization, referred to as first normal form or 1NF) through five (fifth normal form or 5NF). In practical applications, you’ll often see 1NF, 2NF, and 3NF along with the occasional 4NF. Fifth normal form is very rarely seen and won’t be discussed in this article.

Before we begin our discussion of the normal forms, it’s important to point out that they are guidelines and guidelines only. Occasionally, it becomes necessary to stray from them to meet practical business requirements. However, when variations take place, it’s extremely important to evaluate any possible ramifications they could have on your system and account for possible inconsistencies. That said, let’s explore the normal forms.

First Normal Form (1NF)

First normal form (1NF) sets the very basic rules for an organized database:

• Eliminate duplicative columns from the same table.
• Create separate tables for each group of related data and identify each row with a unique column or set of columns (the primary key).

Second Normal Form (2NF)

Second normal form (2NF) further addresses the concept of removing duplicative data:

• Meet all the requirements of the first normal form.
• Remove subsets of data that apply to multiple rows of a table and place them in separate tables.
• Create relationships between these new tables and their predecessors through the use of foreign keys.

Third Normal Form (3NF)

Third normal form (3NF) goes one large step further:

• Meet all the requirements of the second normal form.
• Remove columns that are not dependent upon the primary key.

Fourth Normal Form (4NF)

Finally, fourth normal form (4NF) has one additional requirement:

• Meet all the requirements of the third normal form.
• A relation is in 4NF if it has no multi-valued dependencies.

Remember, these normalization guidelines are cumulative. For a database to be in 2NF, it must first fulfill all the criteria of a 1NF database.

Denormalization

One major premise of this tutorial is that you should learn to develop the “best” possible design—which really focuses on the database structure itself. By doing this, you should be able to avoid many of the problems, bugs, inconsistencies, and maintenance nightmares that frequently plague actual systems in use today.

• However, your database will always be part of a larger system, which will include at least a user interface and reporting structure, perhaps with a large amount of application code written in a language such as Java or C++. Your database could also be the back-end of a Web site, with both middle-tier business logic and front-end presentation code dependent on it. It is not uncommon for developers to “break the rules” of database design in order to accommodate other parts of a system.

• An example of denormalization, using our “phone book” problem, would be to store the city and state attributes in the basic contacts table, rather than making a separate zip codes table. At the cost of extra storage, this would save one join in a SELECT statement. Although this would certainly not be needed in such a simple system, imagine a Web site that supports thousands of “hits” per second, with much more complicated queries needed to produce the output. With today’s terabyte disk systems, it might be worth using extra storage space to keep Web viewers from waiting excessively while a page is being generated. On the other hand, similarly-increasing processor power makes it less likely that this tradeoff will actually have to be made in practice.

• The key to successful denormalization is to make sure that end users of the system never have to manually duplicate or maintain the redundant data. Possible techniques for doing this include using materialized views, writing triggers (code executed by the database itself—not available on all systems), or writing application code that takes care of it at data-entry time.

Definition of database

A .Group of on file data in magnetic disk, optical disk or depositorer sekunder

B. Inwrought collection from data which each other [is] interconnected the than an enterprise ( company, governmental institution or private sector)

C. Manufacturing business of production planning Data, data produce the aktual, data of material ordering, hospital of patient Data, doctor, nurse.

B. DBMS(Database management system )

Combined Database with the application software being based on database

This application programe used to access and look after the database

Especial target of DBMS is provide an efficient and easy environment for the use of, withdrawal and depository of data and information

Bit,Byte,field

· Bit represent part of containing smallest data assess 0 or 1

· Byte Corps from bit which of a kind

· Field byte-byte which of a kind, in data bases used by a attribute term

Type-type atribut

· Single multivalue

o Single only can be filled by at most one value

o Multivalue can be filled with interest from one value with same type

· Atomic vs composition

o Indiscrete Atomic into smaller attribute

o Composition represent the merger from some smaller attribute

· Derived Attribute

o attribute which its value can be yielded from other;dissimilar attribute value, for example age yielded from attribute of date of delivering birth

· Null Value Attribute

o Attribute which do not own the value to a[n record

Mandatory Value Attribute

o Mandatory Value Attribute

Record

Representing an data line in an relationship

Consist of attribute corps of where the attribute interact to inform the entity / relationship completely

Key

• Superkey a set of one or more attributes which, taken collectively, allow us to identify uniquely an entity in the entity set.
• A superkey may contain extraneous attributes, and we are often interested in the smallest superkey. A superkey for which no subset is a superkey is called a candidate key.
• A primary key is a candidate key (there may be more than one) chosen by the DB designer to identify entities in an entity set.
• Alternate Key [is] attribute from candidate key which is not chosen become the primary key.
• Foreign Key represent any attribute subjecting to primary key [of] [at] other tables. Foreign Key will be became of by a[n relationship owning kardinalitas one to many ( one to a lot of) or many to many ( a lot of to a lot of). Foreign Key usually is always placed at tables aiming to a lot of.
• External key represent a[n lexical attribute ( or gathering of lexical attribute) what its values always identify.

ERD(Entity relation diagram)

ERD is an network model using wording kept in system in abstraction.

· Difference of between DFD and ERD.

o DFD represent a[n model of function network to be executed by system

ERD represent the model of data network emphasizing at structure and relationship data

ENTITY

ER of Diagram Entity depicted with the long form square. Rntity is something that there [is] in real system and also abstraction [of] where on file data or where there are data.

At ER of Diagram relationship can be depicted with a lozenged. Relationship [is] natural [relation/link] that happened [among/between] entitas. Generally called with the elementary vb. so that facilitate to doconduct the its relationship read.

Relationship Degree

is the number of entities participating in a relationship. Degree which is often used in the ERD.

Atribute

is the nature or characteristics of each entity and relationship

Cardinalitas

tupel indicates the maximum number that can be berelasi with entities on the other entity

Degree of relationship

1. Unary Relationship

model is the relationship between the entity originating from the same entity set.

2. Binary Relationship

model is the relationship between 2 entities.

3.Ternary Relationship

is a relationship between the instance of

Cardinalitas.

• Mapping Cardinalities: express the number of entities to which another entity can be associated via a relationship. For binary relationship sets between entity sets A and B, the mapping cardinality must be one of:
  1. One-to-one: An entity in A is associated with at most one entity in B, and an entity in B is associated with at most one entity in A.
  2. One-to-many: An entity in A is associated with any number in B. An entity in B is associated with at most one entity in A.
  3. Many-to-one: An entity in A is associated with at most one entity in B. An entity in B is associated with any number in A.
  4. Many-to-many: Entities in A and B are associated with any number from each other.

Notation

An E-R diagram express the overall logical structure of a database graphically.

· 1.) Use rectangles representing entity sets.

· 2.) Use ellipses representing attributes

· 3.) Use ellipses representing attributes.

· 4.) Use diamonds representing relationship sets.

· 5.) Use lines linking attributes to entity sets and entity sets to relationship sets

Colecctive entity

Atributte a as key

Colecctive relation

Link

Data Flow Diagram

approach of structure Scheme started from early 1970. Structure approach provided with [by] the appliance ( tools) and technic – technic ( techniques) required in system development, so that end result from system developed will be obtained system which its structure is defined better and clear. [Through/ passing] structure approach, problems which komplek in solvable organization and result of from easy system to be looked after, flexible, more gratifying of its wearer, having good documentation, timely, as according to budget development, can improve the productivity and its quality will be better ( mistake).

Data of Flow Diagram ( DFD) [is] model making appliance conducive [of] system professional to depict the system as a(n) connective [by] functional process network one [is] of equal other;dissimilar with the data path, either through manual and also computerize. DFD this [is] often referred [as] also by the name of Bubble chart, Bubble Diagram, model the process, diagram groove the job, or model the function. This DFD [is] one of appliance of model making which [is] often used, specially if/when system function represent the more shares important and [is] complex the than [at] data which manipulation by system. Equally, DFD [is] appliance of model making giving emphasis only at system function. This DFD represent the appliance of system scheme orienting [at] data path with the concept dekomposisi applicable to depiction analyse and also easy system device communicated by system professional to and also wearer maker of program. approach of structure scheme started from early 1970. Structure approach provided with [by] the appliance ( tools) and teknikteknik ( techniques) required in system development, so that end result from system developed will be obtained system which its structure [is] defined better and clear. [Through/ passing] structure approach, problems which complex [in] solvable organization and result of from easy system akam to be looked after, flexible, more gratifying [of] its wearer, having good documentation, timely, as according to budget development, can improve the productivity and its quality will be better ( mistake).

Data flow diagram component

According to Yourdan and DeMarco

Terminator process Data store Data grove

Contex diagram

Consist of one process and depict the scope from an system representing highest level from DFD depicting entire/all input to system and output from system limited by boundary ( depicted by dash line) There may not be any depository ( storage).

Context diagrams are typically drawn using:

• labeled boxes to represent each of the external entities and
• another labeled box to represent the system being developed.
• Relationship are drawn as a line between the entities and the system being developed.
• Relationships are labeled with a subject-verb-object format.

Proses specification

Each;Every process of [ DFD have to own the specification of process at top level method used to depict the process earn by using descriptive sentence more level detailed that is [at] process most under ( functional primitive) requiring more specification is structure Specification of process will become the guidance for programmer in making program ( coding) Method used in specification of process: breakdown of process in the form of story, decision table, decision tree.

Data flow

Representing place emit a stream of [it] information. Depicted with the connective straight line of component from system. Data current shown with the direction bow and mark with lines called [by] for data current emiting a stream of Data current emit a stream of among process, data storage and show the data current from data which is in the form of input for the system.

Name

a) name of data Stream consisted of some word stream attributed to by a continued line.

b) There may not be any data stream which its name [is] of equal and name gift have to mirror the its contents.

c) Data stream consisted of some element can be expressed by grup [is] element.

d) Avoid the word use ‘ data’ and ‘ information’ to give the name [of] [at] data stream.

e) As possible name of data stream writed complete.

Other provisions

a) name of data flow which come into an process may not [is] equal to secretory data stream name from the process.

b) name of data flow which come into an process may not [is] equal to secretory data stream name from the process.

· Sysple data flow and easy to comprehended

· Data flow draw all data item

c) There may not be any data stream from terminal to data storage or on the contrary because terminal [of] non part of system, terminal [relation/link] with the data storage have to [through/ passing] process.

Process

Process to represent what done by system process earn the of data or incoming data stream become the data stream go out the Process function the mentransformasikan one or some input data become one or some output data as according to specification wanted Each;Every process own one or some input and also yield one or some output Process often [is] also referred [as] [by] bubble.

Pedoman pemberian nama proses

· Name process consisted of [by] the vb. and noun mirroring the process function.

· Don’T use the word process as part of name [of] a[n bubble.

There may not be any some process owning same name.

· Process have to be given [by] the number. Number sequence as possible follow the stream or sequence process, but that way meaningless number sequence absolutely represent the sequence process chronologically

Data storage

data Storage represent the existing data repository in system Symbol with the couple [of] parallel line or two line wrongly one side from other side opened Process can take the data from or give the data to database

.

Guidelines of the name

Ø Name have to mirror the data storage.

Ø When its name more than one word hence have to be said the word to [by] joint.

Data dictionary

Function to assist the system perpetrator to interpret the application in detail and organizational all data element used by a system precisely so that wearer and system analyst have the same congeniality base about input, output, depository and process at phase analyse the, data dictionary used as by unication means of among/between system analyst with the wearer at phase of system scheme, data dictionary used to design the input, report and database Data current of DAD have the character of global, boldness more detailed visible data dictionary

Kamus data memuat hal-hal sebagai berikut :

I. Name of data flow: have to be noted by reader to needing furthermore clarification about an data flow can look for it easily.

II. Alias: alias or name is differ from data can be writedby if there are any.

III. Data form: used to group the data dictionary into its use time of system scheme

IV. Data flow showing where from data emit a stream of and where data go

V. Clarification: giving clarification of about meaning from data current.

Restrictions in dfd

Data current may not from direct external entitas [go] to the other external entitas without [through/ passing] a[n process Data current may not from direct data deposit go to the external entitas without [through/ passing] a[n process Data current may not from direct data deposit [go] to the other data deposit without [through/ passing] a[n process Data current from one direct process [go] to the other process without [through/ passing] a[n data deposit better / can [is] possible avoided

Why require to develop sisten information?

system development can mean to compile a[n system [of] a[n new system to replace the system old ones as a whole or improve;repair the system which there have. system old ones require to be improve;repaired or changed [by] the disebakan [of] because several things, that is as follows:

Ø Existence of Problems ( problems)

Ø Ketidakberesan in Organizational Growth System

Ø To Reach for the opportunity ( opportunities)

Ø Existence of instruction ( directives)

System Development Target

v Solving problems

v Reach for the opportunity

v Fulfilling given instruction

What expected new system?

· Performance ( performance)

- Measured to use the throughput and response time

· Information ( information)

- Make-Up of information quality

· Economy ( economic)

- Make-Up of benefit of vs decreasing cost

· Control ( operation)

- Detecting and improve;repairing mistake

· Efficiency ( efficiency)

- Efficient [of] operational

· Services ( service)

- Make-Up of system service

System development phase:

1. System planning

2. System analysis

3. System design

4. System selection

5. System implementation and maintenance

System development life cycle with step-step utama

front

end Masing-masing \

phase Siklus menghasilkan

sebagai

back

end

phase

Systems Development Life Cycle

System development life cycle or software development life cycle in system engineering and software engineering refers to the process of creating or altering systems, and the models and methodologies that people use to develop these systems. The concept generally refers to computer or information systems

In systems engineering and software engineering refers to the process of creating or altering systems, and the models and methodologies that people use to develop these systems. The generally refers to computer or information systems.

Overview Systems Development Life Cycle (SDLC) is any logical process used by a systems analyst to develop an information systems. Including requirements, validation, training, and user ownership. An SDLC should result in a high quality system that meets or exceeds customer expectations, reaches completion within time and cost estimates, works effectively and efficiently in the current and planned Information Technology infrastructure, and is inexpensive to maintain and cost-effective to enhance. Computer systems have become more complex and often (especially with the advent of Service-Oriented Architecture) link multiple traditional systems potentially supplied by different software vendors. To manage this level of complexity, a number of system development life cycle (SDLC) models have been created: “waterfall,” “fountain,” “spiral,” “build and fix,” “rapidprototyping,” “incremental,” and “synchronize and stabilize.” Although the term SDLC can refer to various models, it typically denotes a waterfall methodology. In project management a project has both a life cycle and a “systems development life cycle,” during which a number of typical activities occur. The project life cycle (PLC) encompasses all the activities of the project, while the systems development life cycle focuses on realizing the product requirements. History Systems development life cycle is the oldest formalized methodology for building informatio systems, intended to develop information systems in a very deliberate, structured and methodical way, reiterating each stage of the life cycle. The traditional systems development life cycle originated in the 1960s to develop large scale functional business systems in an age of large scale business conglomerates. Information systems activities resolved around heavy data processing and number crunching routines. In the 1980s the Structured Systems Analysis and Design Method (SSADM) was based in SDLC. SSADM is a systems approach to the analysis and design of information systems, produced for the Office of Government Commerce, a UK government office concerned with the use of technology in government. Since the 1980s the traditional life cycle approaches to systems development has been increasingly replaced with alternative approaches and frameworks, which attempted to overcome some of the inherent deficiencies of the traditional SDLC. System development phase.

Systems Development Life Cycle (SDLC) adheres to important phases that are essential for developers, such as planning, analysis, design, and implementation, and are explained in the section below. There are several Systems Development Life Cycle Models in existence. The oldest model, that was originally regarded as “the Systems Development Life Cycle” is the waterfall model a sequence of stages in which the output of each stage becomes the input for the next. These stages generally follow the same basic steps but many different waterfall methodologies give the steps different names and the number of steps seems to vary between 4 and 7. There is no definitively correct Systems Development Life Cycle model, but the steps can be characterized and divided in several steps.

Planning

To generate a high-level view of the intended project determine the goals of the project. The feasibility study is sometimes used to present the project to upper management in an attempt to gain funding. Projects are typically evaluated in three areas of feasibility: economical, operational, and technical. Furthermore, it is also used as a reference to keep the project on track and to evaluate the progress of the MIS team.^[6] The MIS is also a complement of those phases. This phase is also called the analysis phase.

Analysis

The goal of systems analysis is to determine where the problem is in attempt to fix the system. This step involves breaking down the system in different pieces and drawing diagrams to analyze the situation. Analysts project goals, breaking down functions that need to be created, and attempt to engage users so that definite requirements can be defined.

Design

In systems design functions and operations are described in detail, including screen layouts, business rules, process diagrams and other documentation. The output of this stage will describe the new system as a collection of modules or subsystems.

Testing

The code is tested at various levels in software testing. Unit, system and user acceptance testing are often performed. This is a grey area as many different opinions exist as to what the stages of testing are and how much if any iteration occurs. Iteration is not generally part of the waterfall model, but usually some occurs at this stage.

Types of testing:

• Data set testing
• Unit testing
• System testing
• Black box testing
• White box testing
• Module testing
• Regression testing
• Automatic testing
• User acceptaing testing

Operations and Maintenance

The deployment of the system includes changes and enhancements before the decommissioning or sunset of the system. Maintaining the system is an important aspect of SDLC. As key personnel change positions in the organization, new changes will be implemented, which will require system updates. Model Waterfall

Waterfall model

The waterfall model is a sequential development process, in which development is seen as flowing steadily downwards (like a waterfall) through the phases of requirements analysis, design, implementation, testing (validation), integration, and maintenance. The first formal description of the waterfall model is often cited to be an article published by Winston W. Royce in 1970 although Royce did not use the term “waterfall” in this article. Basic principles of the waterfall model are:

Ø Project is divided into sequential phases, with some overlap and splashback acceptable between phases.

Ø Emphasis is on planning, time schedules, target dates, budgets and implementation of an entire system at one time.

Ø Emphasis is on planning, time schedules, target dates, budgets and implementation of an entire system at one time.

Model spiral (RAD) Rapid Application Development (RAD) is a software development methodology, which involves iterative development and the construction of prototypes. Rapid application development is a term originally used to describe a software development process introduced by James Martin in 1991. Basic principles:

Ø Key objective is for fast development and delivery of a high quality system at a relatively low investment cost.

Ø Attempts to reduce inherent project risk by breaking a project into smaller segments and providing more ease-of-change during the development process.

Ø Aims to produce high quality systems quickly, primarily through the use of iterative Prototyping (at any stage of development), active user involvement, and computerized development tools. These tools may include Graphical User Interface (GUI) builders, Computer Aided Software Engineering (CASE) tools, Database Management Systems (DBMS), fourth-generation programming languages, code generators, and object-oriented techniques.

Ø Key emphasis is on fulfilling the business need, while technological or engineering excellence is of lesser importance.

Ø Project control involves prioritizing development and defining delivery deadlines or “timeboxes”. If the project starts to slip, emphasis is on reducing requirements to fit the timebox, not in increasing the deadline.

Ø Generally includes Joint Application Development (JAD), where users are intensely involved in system design, either through consensus building in structured workshops, or through electronically facilitated interaction.

Ø Active user involvement is imperative.

Ø Iteratively produces production software, as opposed to a throwaway prototype.

Ø Produces documentation necessary to facilitate future development and maintenance.

Ø Standard systems analysis and design techniques can be fitted into this framework.

Model spiral

Spiral

The spiral model is a software development process combining elements of both design and prototyping-in-stages, in an effort to combine advantages of top-down and bottom-up concepts. Basic principles:

Ø Focus is on risk assessment and on minimizing project risk by breaking a project into smaller segments and providing more ease-of-change during the development process, as well as providing the opportunity to evaluate risks and weigh consideration of project continuation throughout the life cycle.

Ø Each cycle involves a progression through the same sequence of steps, for each portion of the product and for each of its levels of elaboration, from an overall concept-of-operation document down to the coding of each individual program.

Ø Each trip around the spiral traverses four basic quadarants: (1) determine objectives, alternatives, and constrainst of the iteration; (2) Evaluate alternatives; Identify and resolve risks; (3) develop and verify deliverables from the iteration; and (4) plan the next iteration.

Ø Begin each cycle with an identification of stakeholders and their win conditions, and end each cycle with review and commitment.

Model iterative

System development approach.

· Classic approach [of] vs of structure approach

· approach of Rasher of vs of system approach

· approach of Bawah-Naik vs of approach atas-turun

· approach of Sistem-Menyeluruh vs of approach moduler

· Approach [of] jumping movement far vs approach expand

System development Approach.

v Classic approach [of] vs of structure approach

- Classic approach

· Step in SDLC

· [Do] not involve the consumer, more emphasizing [of] system analyst

· Problems: difficult development, costly treatment expense, big mistake possibility, efficacy less be well guaranted, internal issue applying

- Structure approach

· Consumer involved from early to determine the system requirement

· Using tools-tools of] like data of flow diagram

Approach of Rasher system approach.

- Rasher approach

· Emphasizing at a[n application or activity

Heedless of the target of[is overall of

System approach

· See system as one intact union

· Emphasizing at goal achievement as a whole

Approach of bottom-up vs of approach top-down

- approach bottom-up

· Started from level of under that is operational

· Representing approach marking classical

· Recognized with the term data-analysis

- approach top-down

· Started from level for that is strategy planning

· Representing marking of structure approach

· Recognized also by decision-analysis

Approach of all system vs approach moduler

- approach all system

· Developing system at a time and totally

· Representing classic approach marking

- approach moduler

· Breaking complicated system become the parts of simple

· System developed become on schedule, easy to to be comprehended and looked after

v Representing marking of structure approach

- approach of great loop vs approach expand the

· great-loop approach

· Developing system at a time use the sophisticated technology

· High risk and finish a lot of expense

- Approach expand the ( evolutionary approach)

· Applying sophisticated technology for the application of certain

· Developed to follow the requirement

· Cost effective and can keep abreast of technology

System development methodologies.

What that methodologies?

- Method used in science

What is method?

- A[N way of systematic to do something

What that algorithm?

Procedure medley to solve an problem

Classification of development Methodologies.

v Functional Decomposition

- Emphasizing system resolving become the subsistem

- Follow the example of the: HIPO, Stepwise Refinement, iterative stepwise refinement, information hiding

v Data-Oriented

- Emphasizing at processed data characteristic

· Data-flow Oriented: module [of] according to data element type

· Data-structure Oriented: structure of input and output

v Prescriptive

- [Is] usually provided by factory of software maker

Appliance in system development.

· In form of graph: HIPO, SADT, Jackson’S Diagram, and others

· Appliance using schema:

- Activity Charting: depicting activity, follow the example of the: Gant Chart, flowchart.

- Layout Charting: depicting

- Personal Relationship charting: depicting personnel relation/link, follow the example of the: organization chart, working distribution chart

System development Technique.

· technique of project Management ? for the schedule of project, follow the example of the: CPM And PERT

· Technique find the fact ? to collect and determine the data / fact

- Interview the

- Observation

· Technique analyse the expense / benefit ? cost-benefit and cost-effectiveness analysis

· Technique run the meeting

· Inspection technique

System Analyst & Pemrogram.

· System analyst: learning problem and determine the requirement of system wearer to identify the resolving

· programer: writing code program pursuant to designing to devel/build which is]made by by a analyst

· System analyst undertake to link the knowledge difference that happened among/between wearer of system and programer

Needed knowledge.

· data processing Technology, Computer and programmer

· Business knowledge in general

· Quantitative method: regresi, linear programming,

· Trouble-Shooting membership

· Communications Membership usher the personnel

· Membership construct the usher personnel

SYSTEM
picture

System (from Latin systēma, in turn from Greek σύστημα systēma) is a set of interacting or interdependent entities, real or abstract, forming an integrated whole.

The concept of an “integrated whole” can also be stated in terms of a system embodying a set of relationships which are differentiated from relationships of the set to other elements, and from relationships between an element of the set and elements not a part of the relational regime.

The scientific research field which is engaged in the study of the general properties of systems include systems theory, systems science and systemics. They investigate the abstract properties of the matter and organization, searching concepts and principles which are independent of the specific domain, substance, type, or temporal scales of existence.

Most systems share the same common characteristics. These common characteristics include the following

* Systems are abstractions of reality.
* Systems have structure which is defined by its parts and their composition.
* Systems have behavior, which involves inputs, processing and outputs of material, information or energy.
* The various parts of a system have functional as well as structural relationships between each other.

The term system may also refer to a set of rules that governs behavior or structure.

System concepts
Environment and boundaries
Systems theory views the world as a complex system of interconnected parts. We scope a system by defining its boundary; this means choosing which entities are inside the system and which are outside – part of the environment. We then make simplified representations (models) of the system in order to understand it and to predict or impact its future behavior. These models may define the structure and/or the behaviour of the system.
Natural and man-made systems
There are natural and man-made (designed) systems. Natural systems may not have an apparent objective but their outputs can be interpreted as purposes. Man-made systems are made with purposes that are achieved by the delivery of outputs. Their parts must be related; they must be “designed to work as a coherent entity” – else they would be two or more distinct systems.
Open system

An open system usually interacts with some entities in their environment. A closed system is isolated from its environment.
Proces and tranformation process

A system can also be viewed as a bounded transformation process, that is, a process or collection of processes that transforms inputs into outputs. Inputs are consumed; outputs are produced. The concept of input and output here is very broad. E.g., an output of a passenger ship is the movement of people from departure to destination.
Subsystem
A subsystem is a set of elements, which is a system itself, and a part of a larger system.

INFORMATION

Information as a concept has a diversity of meanings, from everyday usage to technical settings. Generally speaking, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation.

Many people[who?] speak about the Information Age as the advent of the Knowledge Age[citation needed]or knowledge society, the information society, the Information revolution, and information technologies, and even though informatics, information science and computer science are often in the spotlight, the word “information” is often used without careful consideration of the various meanings it has acquired.

Etymology

According to the Oxford English Dictionary, the earliest historical meaning of the word information in English was the act of informing, or giving form or shape to the mind, as in education, instruction, or training. A quote from 1387: “Five books come down from heaven for information of mankind.” It was also used for an item of training, e.g. a particular instruction. “Melibee had heard the great skills and reasons of Dame Prudence, and her wise information and techniques.” (1386)

The English word was apparently derived by adding the common “noun of action” ending “-ation” (descended through French from Latin “-tio”) to the earlier verb to inform, in the sense of to give form to the mind, to discipline, instruct, teach: “Men so wise should go and inform their kings.” (1330) Inform itself comes (via French) from the Latin verb informare, to give form to, to form an idea of. Furthermore, Latin itself already even contained the word informatio meaning concept or idea, but the extent to which this may have influenced the development of the word information in English is unclear.

As a final note, the ancient Greek word for form was είδος eidos, and this word was famously used in a technical philosophical sense by Plato (and later Aristotle) to denote the ideal identity or essence of something (see Theory of forms). “Eidos” can also be associated with thought, proposition or even concept.

Information as a message

Information is the state of a system of interest. Message is the information materialized.
nformation is a quality of a message from a sender to one or more receivers. Information is always about something (size of a parameter, occurrence of an event, value, ethics, etc). Viewed in this manner, information does not have to be accurate; it may be a truth or a lie, or just the sound of a falling tree. Even a disruptive noise used to inhibit the flow of communication and create misunderstanding would in this view be a form of information. However, generally speaking, if the amount of information in the received message increases, the message is more accurate.

This model assumes there is a definite sender and at least one receiver. Many refinements of the model assume the existence of a common language understood by the sender and at least one of the receivers. An important variation identifies information as that which would be communicated by a message if it were sent from a sender to a receiver capable of understanding the message. In another variation, it is not required that the sender be capable of understanding the message, or even cognizant that there is a message, making information something that can be extracted from an environment, e.g., through observation, reading or measurement.

Information is a term with many meanings depending on context, but is as a rule closely related to such concepts as meaning, knowledge, instruction, communication, representation, and mental stimulus. Simply stated, information is a message received and understood. In terms of data, it can be defined as a collection of facts from which conclusions may be drawn. There are many other aspects of information since it is the knowledge acquired through study or experience or instruction. But overall, information is the result of processing, manipulating and organizing data in a way that adds to the knowledge of the person receiving it.

Communication theory provides a numerical measure of the uncertainty of an outcome. For example, we can say that “the signal contained thousands of bits of information”. Communication theory tends to use the concept of information entropy, generally attributed to C.E. Shannon.

Another form of information is Fisher information, a concept of R.A. Fisher. This is used in application of statistics to estimation theory and to science in general. Fisher information is thought of as the amount of information that a message carries about an unobservable parameter. It can be computed from knowledge of the likelihood function defining the system. For example, with a normal likelihood function, the Fisher information is the reciprocal of the variance of the law. In the absence of knowledge of the likelihood law, the Fisher information may be computed from normally distributed score data as the reciprocal of their second moment.

Even though information and data are often used interchangeably, they are actually very different. Data is a set of unrelated information, and as such is of no use until it is properly evaluated. Upon evaluation, once there is some significant relation between data, and they show some relevance, then they are converted into information. Now this same data can be used for different purposes. Thus, till the data convey some information, they are not useful.

Measuring information entropy

The view of information as a message came into prominence with the publication in 1948 of an influential paper by Claude Shannon, “A Mathematical Theory of Communication.” This paper provides the foundations of information theory and endows the word information not only with a technical meaning but also a measure. If the sending device is equally likely to send any one of a set of N messages, then the preferred measure of “the information produced when one message is chosen from the set” is the base two logarithm of N (This measure is called self-information). In this paper, Shannon continues:

The choice of a logarithmic base corresponds to the choice of a unit for measuring information. If the base 2 is used the resulting units may be called binary digits, or more briefly bits, a word suggested by J. W. Tukey. A device with two stable positions, such as a relay or a flip-flop circuit, can store one bit of information.

A complementary way of measuring information is provided by algorithmic information theory. In brief, this measures the information content of a list of symbols based on how predictable they are, or more specifically how easy it is to compute the list through a program: the information content of a sequence is the number of bits of the shortest program that computes it. The sequence below would have a very low algorithmic information measurement since it is a very predictable pattern, and as the pattern continues the measurement would not change. Shannon information would give the same information measurement for each symbol, since they are statistically random, and each new symbol would increase the measurement.

123456789101112131415161718192021

It is important to recognize the limitations of traditional information theory and algorithmic information theory from the perspective of human meaning. For example, when referring to the meaning content of a message Shannon noted “Frequently the messages have meaning… these semantic aspects of communication are irrelevant to the engineering problem. The significant aspect is that the actual message is one selected from a set of possible messages” (emphasis in original).

In information theory signals are part of a process, not a substance; they do something, they do not contain any specific meaning. Combining algorithmic information theory and information theory we can conclude that the most random signal contains the most information as it can be interpreted in any way and cannot be compressed.[citation needed]

Michael Reddy noted that “‘signals’ of the mathematical theory are ‘patterns that can be exchanged’. There is no message contained in the signal, the signals convey the ability to select from a set of possible messages.” In information theory “the system must be designed to operate for each possible selection, not just the one which will actually be chosen since this is unknown at the time of design”.

Information as a pattern

Information is any represented pattern. This view assumes neither accuracy nor directly communicating parties, but instead assumes a separation between an object and its representation. Consider the following example: economic statistics represent an economy, however inaccurately. What are commonly referred to as data in computing, statistics, and other fields, are forms of information in this sense. The electro-magnetic patterns in a computer network and connected devices are related to something other than the pattern itself, such as text characters to be displayed and keyboard input. Signals, signs, and symbols are also in this category. On the other hand, according to semiotics, data is symbols with certain syntax and information is data with a certain semantic. Painting and drawing contain information to the extent that they represent something such as an assortment of objects on a table, a profile, or a landscape. In other words, when a pattern of something is transposed to a pattern of something else, the latter is information. This would be the case whether or not there was anyone to perceive it.

But if information can be defined merely as a pattern, does that mean that neither utility nor meaning are necessary components of information? Arguably a distinction must be made between raw unprocessed data and information which possesses utility, value or some quantum of meaning. On this view, information may indeed be characterized as a pattern; but this is a necessary condition, not a sufficient one.

An individual entry in a telephone book, which follows a specific pattern formed by name, address and telephone number, does not become “informative” in some sense unless and until it possesses some degree of utility, value or meaning. For example, someone might look up a girlfriend’s number, might order a take away etc. The vast majority of numbers will never be construed as “information” in any meaningful sense. The gap between data and information is only closed by a behavioral bridge whereby some value, utility or meaning is added to transform mere data or pattern into information.

When one constructs a representation of an object, one can selectively extract from the object (sampling) or use a system of signs to replace (encoding), or both. The sampling and encoding result in representation. An example of the former is a “sample” of a product; an example of the latter is “verbal description” of a product. Both contain information of the product, however inaccurate. When one interprets representation, one can predict a broader pattern from a limited number of observations (inference) or understand the relation between patterns of two different things (decoding). One example of the former is to sip a soup to know if it is spoiled; an example of the latter is examining footprints to determine the animal and its condition. In both cases, information sources are not constructed or presented by some “sender” of information. Regardless, information is dependent upon, but usually unrelated to and separate from, the medium or media used to express it. In other words, the position of a theoretical series of bits, or even the output once interpreted by a computer or similar device, is unimportant, except when someone or something is present to interpret the information. Therefore, a quantity of information is totally distinct from its medium.

Information as sensory input

Often information is viewed as a type of input to an organism or designed device. Inputs are of two kinds. Some inputs are important to the function of the organism (for example, food) or device (energy) by themselves. In his book Sensory Ecology, Dusenbery called these causal inputs. Other inputs (information) are important only because they are associated with causal inputs and can be used to predict the occurrence of a causal input at a later time (and perhaps another place). Some information is important because of association with other information but eventually there must be a connection to a causal input. In practice, information is usually carried by weak stimuli that must be detected by specialized sensory systems and amplified by energy inputs before they can be functional to the organism or device. For example, light is often a causal input to plants but provides information to animals. The colored light reflected from a flower is too weak to do much photosynthetic work but the visual system of the bee detects it and the bee’s nervous system uses the information to guide the bee to the flower, where the bee often finds nectar or pollen, which are causal inputs, serving a nutritional function.

Information is any type of sensory input. When an organism with a nervous system receives an input, it transforms the input into an electrical signal. This is regarded information by some. The idea of representation is still relevant, but in a slightly different manner. That is, while abstract painting does not represent anything concretely, when the viewer sees the painting, it is nevertheless transformed into electrical signals that create a representation of the painting. Defined this way, information does not have to be related to truth, communication, or representation of an object. Entertainment in general is not intended to be informative. Music, the performing arts, amusement parks, works of fiction and so on are thus forms of information in this sense, but they are not necessarily forms of information according to some definitions given above. Consider another example: food supplies both nutrition and taste for those who eat it. If information is equated to sensory input, then nutrition is not information but taste is.

Information as an influence which leads to a transformation

Information is any type of pattern that influences the formation or transformation of other patterns. In this sense, there is no need for a conscious mind to perceive, much less appreciate, the pattern. Consider, for example, DNA. The sequence of nucleotides is a pattern that influences the formation and development of an organism without any need for a conscious mind. Systems theory at times seems to refer to information in this sense, assuming information does not necessarily involve any conscious mind, and patterns circulating (due to feedback) in the system can be called information. In other words, it can be said that information in this sense is something potentially perceived as representation, though not created or presented for that purpose.

When Marshall McLuhan speaks of media and their effects on human cultures, he refers to the structure of artifacts that in turn shape our behaviors and mindsets. Also, pheromones are often said to be “information” in this sense.

Information as a property in physics

Main article: Physical information

In 2003, J. D. Bekenstein claimed there is a growing trend in physics to define the physical world as being made of information itself (and thus information is defined in this way). Information has a well defined meaning in physics. Examples of this include the phenomenon of quantum entanglement where particles can interact without reference to their separation or the speed of light. Information itself cannot travel faster than light even if the information is transmitted indirectly. This could lead to the fact that all attempts at physically observing a particle with an “entangled” relationship to another are slowed down, even though the particles are not connected in any other way other than by the information they carry.

Another link is demonstrated by the Maxwell’s demon thought experiment. In this experiment, a direct relationship between information and another physical property, entropy, is demonstrated. A consequence is that it is impossible to destroy information without increasing the entropy of a system; in practical terms this often means generating heat. Another, more philosophical, outcome is that information could be thought of as interchangeable with energy. Thus, in the study of logic gates, the theoretical lower bound of thermal energy released by an AND gate is higher than for the NOT gate (because information is destroyed in an AND gate and simply converted in a NOT gate). Physical information is of particular importance in the theory of quantum computers.

Information as records

Records are a specialized form of information. Essentially, records are information produced consciously or as by-products of business activities or transactions and retained because of their value. Primarily their value is as evidence of the activities of the organization but they may also be retained for their informational value. Sound records management ensures that the integrity of records is preserved for as long as they are required.

The international standard on records management, ISO 15489, defines records as “information created, received, and maintained as evidence and information by an organization or person, in pursuance of legal obligations or in the transaction of business”. The International Committee on Archives (ICA) Committee on electronic records defined a record as, “a specific piece of recorded information generated, collected or received in the initiation, conduct or completion of an activity and that comprises sufficient content, context and structure to provide proof or evidence of that activity”.

Records may be retained because of their business value, as part of the corporate memory of the organization or to meet legal, fiscal or accountability requirements imposed on the organization. Willis (2005) expressed the view that sound management of business records and information delivered “…six key requirements for good corporate governance…transparency; accountability; due process; compliance; meeting statutory and common law requirements; and security of personal and corporate information.”

Information and semiotics

Beynon-Davies explains the multi-faceted concept of information in terms of that of signs and sign-systems. Signs themselves can be considered in terms of four inter-dependent levels, layers or branches of semiotics: pragmatics, semantics, syntactics and empirics. These four layers serve to connect the social world on the one hand with the physical or technical world on the other.

Pragmatics is concerned with the purpose of communication. Pragmatics links the issue of signs with that of intention. The focus of pragmatics is on the intentions of human agents underlying communicative behaviour. In other words, intentions link language to action.

Semantics is concerned with the meaning of a message conveyed in a communicative act. Semantics considers the content of communication. Semantics is the study of the meaning of signs – the association between signs and behaviour. Semantics can be considered as the study of the link between symbols and their referents or concepts; particularly the way in which signs relate to human behaviour.

Syntactics is concerned with the formalism used to represent a message. Syntactics as an area studies the form of communication in terms of the logic and grammar of sign systems. Syntactics is devoted to the study of the form rather than the content of signs and sign-systems.

Empirics is the study of the signals used to carry a message; the physical characteristics of the medium of communication. Empirics is devoted to the study of communication channels and their characteristics, e.g., sound, light, electronic transmission etc.

Communication normally exists within the context of some social situation. The social situation sets the context for the intentions conveyed (pragmatics) and the form in which communication takes place. In a communicative situation intentions are expressed through messages which comprise collections of inter-related signs taken from a language which is mutually understood by the agents involved in the communication. Mutual understanding implies that agents involved understand the chosen language in terms of its agreed syntax (syntactics) and semantics. The sender codes the message in the language and sends the message as signals along some communication channel (empirics). The chosen communication channel will have inherent properties which determine outcomes such as the speed with which communication can take place and over what distance.
INFORMATION SYSTEM

Information system is a collection of methods, practices, algorithms and methodologies that transforms data into information and knowledge desired by, and useful for, individual and group users in organizations and other entities. This system can involve a combination of work practices, information, people, and technologies organized to accomplish goals in an organization.

There are five types of information system. These are transaction processing systems, excutive support systems, business systems, knowledge work systems and office information systems.

Areas of work

Information Systems has a number of different areas of work:

A. Information Systems Strategy
B. Information Systems Management
C. Information Systems Development

There are a wide variety of career paths in the information systems discipline. “Workers with specialized technical knowledge and strong communications skills will have the best prospects. People with management skills and an understanding of business practices and principles will have excellent opportunities, as companies are increasingly looking to technology to drive their revenue.”

Types of Information systems

From prior studies and experiences with information systems there are at least four classes of information systems:

• Transaction processing system

• Management information system

• Decision support system

• Expert  system

• Bussines intelligence

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