gunggus

Week 7-ER Normalization

2009-05-03T00:34:00.000-07:00

ER-Diagram

ERD in the above, there are 2 that confusion can lead to an error occurs, namely:

1. On the relationship between the entity and the project does not have a clear cardinality.

2. There is a ternary relationship have on the relationship.

Based on the above confusion, the ERD can be improved:

Mapping of ERD image above:

1 NF

First normal form (1NF or Minimal Form) is a normal form used in database normalization. A relational database table that adheres to 1NF is one that meets a certain minimum set of criteria. These criteria are basically concerned with ensuring that the table is a faithful representation of a relation and that it is free of repeating groups.

Second Normal form (NF 2) is:

Meets 1 of NF (normal first).
Non-key attributes must depend on the function key / primery key.

So that the second normal form for each table / file must be specified attribute keys.

3NF/Third Normal Form

Definition of Third Normal Form (NF 3) is:

Meet the 2 forms of NF (both normal).
Non-key attributes do not have a transitive dependency on the key / key primery.

Same as in the second normal form, the changes only on the table pegawai only.

Database normalization

2009-04-26T03:22:00.000-07:00

Database design

Database design is the process of producing a detailed data model of a database. This logical data model contains all the needed logical and physical design choices and physical storage parameters needed to generate a design in a Data Definition Language, which can then be used to create a database. A fully attributed data model contains detailed attributes for each entity.

The term database design can be used to describe many different parts of the design of an overall database system. Principally, and most correctly, it can be thought of as the logical design of the base data structures used to store the data. In the relational model these are the tables and views. In an object database the entities and relationships map directly to object classes and named relationships. However, the term database design could also be used to apply to the overall process of designing, not just the base data structures, but also the forms and queries used as part of the overall database application within the database management system (DBMS).

Database normalization

Normalization is the process of efficiently organizing data in a database. There are two goals of the normalization process: eliminating redundant data (for example, storing the same data in more than one table) and ensuring data dependencies make sense (only storing related data in a table). Both of these are worthy goals as they reduce the amount of space a database consumes and ensure that data is logically stored.

Normalization done by because to Optimalisasi Structure of is tables, Improving speed, Eliminating same data inclusion, More efficient in storage media use, Lessening redundans, Avoiding anomali ( insertion anomalies, deletion anomalies, update the anomalies) and improved Data integrity.

A tables told by goodness of if fulfilling 3 criterion :

· If there is decomposition of is tables of, hence decomposition have to be secured ( Lossless-Join Decomposition

· The looking after of functional depending at the (time) of data change ( Dependency Preservation).

· Do not impinge the Normal Boyce-Code of Form ( BCNF)

If third criterion ( BCNF) cannot be fullfiled, hence at least the tables do not impinge the Normal Form of third phase ( 3rd Normal of Form / 3NF )

functional dependency

functional dependency (FD) is a constraint between two sets of attributes in a relation from a database.

Given a relation R, a set of attributes X in R is said to functionally determine another attribute Y, also in R, (written X → Y) if and only if each X value is associated with precisely one Y value. Customarily we call X the determinant set and Y the dependent attribute. Thus, given a tuple and the values of the attributes in X, one can determine the corresponding value of the Y attribute. For the purposes of simplicity, given that X and Y are sets of attributes in R, X → Y denotes that X functionally determines each of the members of Y - in this case Y is known as the dependent set. Thus, a candidate key is a minimal set of attributes that functionally determine all of the attributes in a relation.

(Note: the "function" being discussed in "functional dependency" is the function of identification.)

A functional dependency FD:X\to Y is called trivial if Y is a subset of X.

The determination of functional dependencies is an important part of designing databases in the relational model, and in database normalization and denormalization. The functional dependencies, along with the attribute domains, are selected so as to generate constraints that would exclude as much data inappropriate to the user domain from the system as possible.

For example, suppose one is designing a system to track vehicles and the capacity of their engines. Each vehicle has a unique vehicle identification number (VIN). One would write VIN → EngineCapacity because it would be inappropriate for a vehicle's engine to have more than one capacity. (Assuming, in this case, that vehicles only have one engine.) However, EngineCapacity → VIN, is incorrect because there could be many vehicles with the same engine capacity.

This functional dependency may suggest that the attribute EngineCapacity be placed in a relation with candidate key VIN. However, that may not always be appropriate. For example, if that functional dependency occurs as a result of the transitive functional dependencies

\mbox{VIN}\,\to\,\mbox{VehicleModel}, \mbox{VehicleModel}\,\to\,\mbox{EngineCapacity},

then that would not result in a normalized relation.

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 keyis a key which the database designer has designated for this purpose.

First Normal Form (1NF)

First Normal Form (1NF) is now generally considered part of the formal definition of a relation. Historically, 1NF was intended to disallow multi-valued attributes. 1NF dictates that the domains (allowable values) of attributes must include only atomic (simple, indivisible) values and that any given value of an instance of an attribute must be a single value from the domain of that attribute. In short, a given cell of a column in a table can contain only one value.

The following table violates 1NF because the second row contains more than one value in the COLOR column:

To ensure a table is in 1NF, one simply needs to decompose grouped attributes into separate rows (or in some case, tables). The

following representation of the above data is in 1NF (and others):

Second Normal Form

Second Normal Form (2NF) is based on the concept of "full" functional dependency. A functional dependency, X ® Y, is a full functional dependency if removal of any attribute from X means that the dependency does not hold anymore. For example, Given a table that tracks hours (HOURS) a given employee (SSN) devotes to a given project (PROJNUM), we note that HOURS is functionally dependent on the combination of SSN and PROJNUM because a given employee can work on more than one project. Removal of either SSN or PROJNUM from the functional dependency results in an incorrect relationship. For example, if we were to remove SSN from the previous functional dependency, we are left with HOURS and PROJNUM, but we don’t know which SSN worked those HOURS! Thus, we say SSN, PROJNUM is a full functional dependency of

HOURS.

A table is in 2NF if that table is in 1NF and every non-prime (is not involved in a primary key of the table) attribute in that table is fully functionally dependent on the primary key of the table.

For example, the following table is in 1NF but is not in 2NF because PNAME and PLOCATION are dependent on only part of the primary key (PROJNUM and SSN). Likewise, ENAME is also only dependent on SSN. (Primary keys will be denoted in this document by underlining the included columns.) Employee_Project_Table

To correct this schema, we need to create additional tables and decompose the partial dependencies into these new tables, as in figure 4.

(Please note that, for simplicity, these diagrams will not denote the resulting referential integrity, such as foreign keys, that would need to be added to these decomposed schemas.)

Employee_Table

Third Normal Form

Third Normal Form (3NF) is based on the concept of "transitive dependency". A transitive dependency can be loosely defined as a dependency that does not involve the primary key. For example, in the table below, we see that while all elements have functional dependencies on the key, SSN, there also exist other, transitive, dependencies. Namely, DEPTNAME is dependent on DEPTNUM.

Employee_Department_Table

A table is in 3NF if it is in 2NF and no non-key attributes are dependent on other non-key attributes.

We can decompose the above table into 3NF by creating a second table for department. Thus, the following structure is in 3NF:

Employee_Table

Department_Table

There is a subtle difference between 2NF and 3NF. In 2NF, we were concerned about non-key fields being dependent on subsets of the key. In 3NF, we are concerned about non-key fields being dependent on other non-key fields. Another way to say this has been nicely summarized as: any non-key field must be "… Dependent on the key, the whole key, and nothing but the key."

Elementary Key Normal Form

Elementary Key Normal Form (EKNF) is a subtle enhancement on 3NF (by definition, EKNF tables are also in 3NF) that most often occurs when there is more than one unique composite key (more than one column) which overlap (one or more columns are involved in both keys) in a table 3. Such cases can cause redundant information in the overlapping column(s). For example, in the following table, let’s assume that a subject title (SUBJECTTITLE) is also a unique identifier for a given subject in the following table:

Enrollment_Table

The primary key of the above table is the combination of STUDENTNUM and SUBJECTCODE. However, we can also see a (non-primary) uniqueness constraint (alternate key) that should span the STUDENTNUM and SUBJECTTITLE columns as well. The above schema could result in update and deletion anomalies because values of both SUBJECTCODE and SUBJECTTITLE tend to be repeated for a given subject. The following schema is a decomposition of the above table in order to satisfy EKNF:

Subject_Table

Enrollment_Table

For reasons that will become obvious in the following section, ensuring a table is in EKNF is usually skipped, as most designers will move directly on to Boyce-Codd Normal Form after ensuring that a schema is in 3NF. Thus, EKNF is included here only for reasons of historical accuracy and completeness.

Boyce-Codd Normal Form

Like EKNF, the only time a table is in 3NF but is not in Boyce-Codd Normal form (BCNF) is when the table contains two or more candidate keys that overlap. Beyond that, there is only a subtle difference between EKNF and BCNF, which I will outline below. Consider the same example we used to illustrate EKNF, but we now add a column (GRADE) to denote a student’s grade received in the course. (Further, for illustrative simplicity, let’s assume that a student can only take a course once.)

Enrollment_Grade_Table

We see here that the GRADE column is dependent only on a given enrollment pair and that the keys are now elementary 3 (which satisfies EKNF). However, SUBJECTTITLE is dependent on SUBJECTCODE. Since the key of the table is STUDENTNUM and SUBJECTCODE, we decompose this structure into the following two tables which satisfy BCNF:

One may note that this would also have happened to solve our EKNF problem in the previous section. For that very reason, most designers seldom worry about EKNF and move straight on to BCNF.

Fourth Normal Form

The final normal forms are concerned with multi-valued facts. We can also note that they are concerned with composite keys, as they tend to minimize the number of fields involved in a composite key. A table is in Fourth Normal form if it is in BCNF and all functional dependencies are "single valued". Another way to state this is to say that a table cannot contain two or more independent "multivalued" 4 facts. By "independent", we mean to say that there is no direct connection between the two (or more) multivalued facts. This vague definition is better handled by example. In the following table (in BCNF, since it is entirely composed of attributes involved in the key), we record people (NAME), instruments they play (INSTRUMENT), and music styles (MUSICSTYLE) they play.

We see that redundancy occurs because a given person (NAME) can play more than one INSTRUMENT and play more than one MUSICSTYLE (the fact that ‘Hallock’ plays the ‘Piano’ is repeated, as is the fact that he plays the ‘Blues’ and ‘Classical’). Further, this table seems to suggest a link between instruments and music styles. Can ‘Hallock’ play ‘Blues’ with a French Horn 5? (Yes, and if you know Hallock, you know he plays the blues with anything, including spoons!)

In other words, we see that there are two independent multi-valued facts in the above table. The first is that a person (NAME) can play more than one INSTRUMENT while the second is that a person (NAME) can play more than one MUSICSTYLE. These facts are independent because these two facts have no bearing on each other. Decomposing this table into two tables (below) solves the problem.

Figure 12: The table in Figure 11 has been decomposed into a 4NF schema.

One should note that 4NF only applies to tables with three or more attributes (it eliminates overlapping multi-valued dependencies, which, by definition, require three or more attributes) and only when all attributes compose the primary key of the table.

Fifth Normal Form and Project Join Normal Form

Cases where a table is in 4NF but is not in Fifth Normal Form (5NF) are extremely rare. Further, Project Join Normal Form (PJNF) is a slightly stronger (although this is debated) case of 5NF, and in virtually all cases it can be treated as an equivalent. Therefore, PJNF is included here for completeness. As in 4NF, 5NF considerations apply only to tables with three or more attributes, all of which comprise the primary key. The formal definition of 5NF and PJNF requires that we must first define a "projection". A projection of a table is a subset of the total number of columns with no duplicate rows. For example, the following table:

has the following projections:

The training table in Figure 13 may or may not be in 5NF depending on the business rules. Say we have to enforce the rule:

An EMPLOYEE trains a CLASSTYPE for a COMPANY if and only if an EMPLOYEE trains a CLASSTYPE, the EMPLOYEE trains for a COMPANY, and the COMPANY the EMPLOYEE trains for makes a tool that implements the CLASSTYPE the EMPLOYEE trains.

If we enforce the above rule the table in Figure 13 is not in 5NF and must be reduced to three tables represented by the above projections of the original table.

To achieve 5NF, one checks all-key tables for decompositions whose joins result in the same information. A cautionary note, however, is that such decompositions can lead to a loss of constraint knowledge. For example, in the above case, we need to create database code to handle the specified rule between an EMPLOYEE, the CLASSTYPEs they train, and the COMPANY who makes the tool that implements the CLASSTYPE.

The root concept behind 4NF, 5NF, and PJNF is that the tables not in these normal forms can be derived from simpler, more fundamental relationships. Further, 5NF does not differ from 4NF unless there are other rules (symmetric constraints) that dictate correct data population Lastly, 5NF differs from 4NF in that the fact combinations we are concerned with are no longer independent from each other (due to the semantic constraints).

Reffrences :

http://databases.about.com/od/specificproducts/a/normalization.htm

http://en.wikipedia.org/wiki/Functional_dependency

http://en.wikipedia.org/wiki/Database_design

http://www3.gpf.or.th/knowledge/adb/im/sample4.pdf

DATABASE AND ER-Diagram

2009-04-19T04:49:00.000-07:00

DATABASE

A database is a structured collection of records or data that is stored in a computer system. The structure is achieved by organizing the data according to a database model. The model in most common use today is the relational model. Other models such as the hierarchical model and the network model use a more explicit representation of relationships.

DBMS ( Database Management System )

A database management system (DBMS) is computer software that manages databases. DBMSes may use any of a variety of database models, such as the network model or relational model. In large systems, a DBMS allows users and other software to store and retrieve data in a structured way.

BIT, BYTE, FIELD

Bit

A bit is a binary digit, taking a logical value of either "1" or "0" (also referred to as "true" or "false" respectively). Binary digits are a basic unit of information storage and communication in digital computing and digital information theory. Information theory also often uses the natural digit, called either a nit or a nat. Quantum computing user qubits; single piece of quantum information encoded on a two level quantum system and hence having the potential to exist in superposition of "true" and "false".

Byte

A byte is a basic unit of measurement of information storage in computer science In many computer architecture it is a unit of memory addressing . There is no standard but a byte most often consists of eight bits.

A byte is an ordered collection of bits, with each bit denoting a single binary value of 1 or 0. The byte most often consists of 8 bits in modern systems; however, the size of a byte can vary and is generally determined by the underlying computer operating system or hardware. Historically, byte size was determined by the number of bits required to represent a single character from a Western character set. Its size was generally determined by the number of possible characters in the supported character set and was chosen to be a divisor of the computer's word zize. Historically bytes have ranged from five to twelve bits.

Field

Field is a set of byte-byte similar, in the database used the term attribute

Attribute/field

Attribute or Field is a characteristic from entity, which preparing detailed explanation about that's entity.
A relation could have atribute too.
Example of Attribute :

Student : NIM, Name, Sex, Address
Car : Plat Number, Color, CC
Book : ID, title, author

Type of Attribute

Single vs multivalue

Single > can only be filled at most one value

Multivalue > can be filled with more than one value with the same type of

Atomic vs composition

Atomic > can’t be divided into the attributes of smaller

composition > is a combination of several attributes of a smaller

Derived Attribute

attribute value can be derived from other attribute values, for example: age of the attributes generated from the date of birth.

Null Value Attribute

Attributes that have no value to a record

Mandatory Value Attribute

Attributes must have values

Record / Tuple

Record is a data line in an relation. Consist of attributes where there attribute can interaction to completely information a entity / relation.

Entity or File

Entity is a collection of same kind of record and having same element, same attribute however different in their data value
type of Entity:
in processing application, file can be categorized likes:
- mains file
- transaction file
- report file
- history file
- protector file
- work file

Domain

Domain is collection of values which enabled to stay in one or more attribute. Every attribute in a relational database is defined like a domain

Key of element data

Key is the element of record which used to find the record when accessing or can also used to identify every entity / record / row

Type of key :

superkey

A superkey is defined in the relational model of database organization as a set of attributes of a relation variable (relvar) for which it holds that in all relations assigned to that variable there are no two distinct tuples (rows) that have the same values for the attributes in this set. Equivalently a superkey can also be defined as a set of attributes of a relvar upon which all attributes of the relvar are functionally dependent.

Candidate key

In the relational model, a candidate key of a relvar (relation variable) is a set of attributes of that relvar such that

1. at all times it holds in the relation assigned to that variable that there are no two distinct tuples with the same values for these attributes and

2. there is not a proper subset of this set of attributes for which (1) holds.

Since a superkey is defined as a set of attributes for which (1) holds, we can also define a candidate key as a minimal superkey, i.e. a superkey of which no proper subset can also be called as a candidate key.

The importance of candidate keys is that they tell us how we can identify individual tuples in a relation. As such they are one of the most important types of database constraint that should be specified when designing a database schema. Since a relation is a set (no duplicate elements), it holds that every relation will have at least one candidate key (because the entire heading is always a superkey). Since in some RDBMSs tables may also represent multisets (which strictly means these DBMSs are not relational), it is an important design rule to specify explicitly at least one candidate key for each relation. For practical reasons RDBMSs usually require that for each relation one of its candidate keys is declared as the primary key, which means that it is considered as the preferred way to identify individual tuples. Foreign keys, for example, are usually required to reference such a primary key and not any of the other candidate keys.

Primary Key

One of the key attributes of the candidate can be selected / specified a

primary key with the three following criteria:

1. Key is more natural to use as reference

2. Key is more simple

3. Key is guaranteed unique

Alternate Key

is an attribute of the candidate key is not selected to be primary key.

Foreign Key

is any attribute that points to the primary key in another table. Foreign key will be going on a relationship that has kardinalitas one to many or many to many. Foreign key is usually always put on the table that point to many.

External Key

is a lexical attribute (or set of lexical attributes) that values are always identify an object instance.

ERD (ENTITY RELATIONSHIP DIAGRAM)

Data models are tools used in analysis to describe the data requirements and assumptions in the system from a top-down perspective. They also set the stage for the design of databases later on in the SDLC.

There are three basic elements in ER models:

Entities are the "things" about which we seek information.

Attributes are the data we collect about the entities.

Relationships provide the structure needed to draw information from multiple entities.

Generally, ERD's look like this:

ELEMENTS OF THE ERD

Entity

In the ER Diagram Entity is described with the form of a rectangle. entity is something that exists in the real system and the abstract where the data stored or where there are data.

Relationship

ER diagram on the relationship can be described with a lozenge. Relationship is a natural relationship that occurs between entities. In general, the name given to the verb base making it easier to do readings relationships.

Relationship Degree

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

Attribute

is the nature or characteristics of each entity and relationship

Kardinalitas

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

Relationship Degree

Unary Relationship is a relationship model between entity coming from same entity set.

Binary Relationship is a relationship model between 2 entity.

Ternary Relationship is a relationship between instance from 3 type of entitas unilaterally.

Cardinality

Cardinality indicates the maximum number of tables that can be relation with the entity on the other entity
Type of Cardinality :

One to One:

Level one to one relationship with the one stated in the entity's first event, only had one relationship with one incident in which the two entities and vice versa.

One to Many or Many to One:

Level one to many relationship is the same as the one to many depending on the direction from which the relationship is viewed. For one incident in the first entity can have many relationships with the incident on the second entity, if the one incident in which two entities can have only one incident hubugan with the first entity.

Many To Many:

if any incident occurs in an entity has many relationships with other entities in the incident.

NOTATION (E-R DIAGRAM)

Symbolic notation in the ER diagram is:

1. Rectangle represent the collective entity

2. Circle represent the attributes

3. Rhomb represent collective relationships

4. Line as the set of relations between the Association and the collective entity with entity attributes

Reference:

ER Ngurah Agus Sanjaya. Slide Part 5 - Database Dan Er-Diagram.

http://en.wikipedia.org/wiki/Database

http://en.wikipedia.org/wiki/Database_management_system

http://kur2003.if.itb.ac.id/file/SE6162%20ERD.pdf

http://www.umsl.edu/~sauterv/analysis/er/er_intro.html

"DFD" Data Flow Diagram

2009-04-05T01:28:00.000-07:00

Data Flow Diagram

Data flow diagram also called Data Flow Diagram (DFD). DFD is to describe the system modules in a smaller and less easy for the user to understand to understand the field of computer systems that will be done.

DFD also serves to describe the existing system or the new system will be developed logically without considering the physical environment where the data flows, or where data are stored. DFD is a tool that is used in the system development methodology is structured (structured analysis and design). DFD can describe the flow of data within the system with a structured and clear.

The result is a series of diagrams that represent the business activities in a way that is clear and easy to communicate. A business model comprises one or more data flow diagrams (also known as business process diagrams). Initially a context diagram is drawn, which is a simple representation of the entire system under investigation. This is followed by a level 1 diagram; which provides an overview of the major functional areas of the business. Don't worry about the symbols at this stage, these are explained shortly. Using the context diagram together with additional information from the area of interest, the level 1 diagram can then be drawn.

The level 1 diagram identifies the major business processes at a high level and any of these processes can then be analyzed further - giving rise to a corresponding level 2 business process diagram. This process of more detailed analysis can then continue – through level 3, 4 and so on. However, most investigations will stop at level 2 and it is very unusual to go beyond a level 3 diagram.

Identifying the existing business processes, using a technique like data flow diagrams, is an essential precursor to business process re-engineering, migration to new technology, or refinement of an existing business process. However, the level of detail required will depend on the type of change being considered.

Data Flow Diagrams
There are only five symbols that are used in the drawing of business process diagrams (data flow diagrams). These are now explained, together with the rules that apply to them.

External Entity

An external entity is a source or destination of a data flow which is outside the area of study. Only those entities which originate or receive data are represented on a business process diagram. The symbol used is an oval containing a meaningful and unique identifier.

Process

A process shows a transformation or manipulation of data flows within the system. The symbol used is a rectangular box which contains 3 descriptive elements:
Firstly an identification number appears in the upper left hand corner. This is allocated arbitrarily at the top level and serves as a unique reference.
Secondly, a location appears to the right of the identifier and describes where in the system the process takes place. This may, for example, be a department or a piece of hardware. Finally, a descriptive title is placed in the centre of the box. This should be a simple imperative sentence with a specific verb, for example 'maintain customer records' or 'find driver'.

Data Flow

A data flow shows the flow of information from its source to its destination. A data flow is represented by a line, with arrowheads showing the direction of flow. Information always flows to or from a process and may be written, verbal or electronic. Each data flow may be referenced by the processes or data stores at its head and tail, or by a description of its contents.

Data Store

A data store is a holding place for information within the system:
It is represented by an open ended narrow rectangle.
Data stores may be long-term files such as sales ledgers, or may be short-term accumulations: for example batches of documents that are waiting to be processed. Each data store should be given a reference followed by an arbitrary number.

Resource Flow

A resource flow shows the flow of any physical material from its source to its destination. For this reason they are sometimes referred to as physical flows.
The physical material in question should be given a meaningful name. Resource flows are usually restricted to early, high-level diagrams and are used when a description of the physical flow of materials is considered to be important to help the analysis.

Data Flow Diagrams – The Rules

External Entities
It is normal for all the information represented within a system to have been obtained from, and/or to be passed onto, an external source or recipient. These external entities may be duplicated on a diagram, to avoid crossing data flow lines. Where they are duplicated a stripe is drawn across the left hand corner, like this.

The addition of a lowercase letter to each entity on the diagram is a good way to uniquely identify them.

Processes
When naming processes, avoid glossing over them, without really understanding their role. Indications that this has been done are the use of vague terms in the descriptive title area - like 'process' or 'update'.

The most important thing to remember is that the description must be meaningful to whoever will be using the diagram.

Data Flows
Double headed arrows can be used (to show two-way flows) on all but bottom level diagrams. Furthermore, in common with most of the other symbols used, a data flow at a particular level of a diagram may be decomposed to multiple data flows at lower levels.

Data Stores
Each store should be given a reference letter, followed by an arbitrary number. These reference letters are allocated as follows:

'D' - indicates a permanent computer file
'M' - indicates a manual file
'T' - indicates a transient store, one that is deleted after
processing.

In order to avoid complex flows, the same data store may be drawn several times on a diagram. Multiple instances of the same data store are indicated by a double vertical bar on their left hand edge.

Data Flow Diagrams – Relationship Grid

There are rules governing various aspects of the diagram components and how they can relate to one another.

Data Flows
For data flows the rules are as follows:
Data flows and resource flows are allowed between external entities and processes. Data flows are also allowed between different external entities. However, data flows and resource flows are not allowed between external entities and data stores.

Processes
For processes the data flow rules are as follows:
Data flows and resource flows are allowed between processes and external entities and between processes and data stores. They are also allowed between different processes. In other words processes can communicate with all other areas of the business process diagram.

Data Stores
For data stores the data flow rules are as follows:
Data flows and resource flows are allowed between data stores and processes. However, these flows are not allowed between data stores and external entities or between one data store and another. In practice this means that data stores cannot initiate a communication of information, they require a process to do this.

Data Flow Diagrams – Context Diagrams

The context diagram represents the entire system under investigation. This diagram should be drawn first, and used to clarify and agree the scope of the investigation.

The components of a context diagram are clearly shown on this screen. The system under investigation is represented as a single process, connected to external entities by data flows and resource flows.

The context diagram clearly shows the interfaces between the system under investigation and the external entities with which it communicates. Therefore, whilst it is often conceptually trivial, a context diagram serves to focus attention on the system boundary and can help in clarifying the precise scope of the analysis.

The context diagram shown on this screen represents a book lending library. The library receives details of books, and orders books from one or more book suppliers.

Books may be reserved and borrowed by members of the public, who are required to give a borrower number. The library will notify borrowers when a reserved book becomes available or when a borrowed book becomes overdue.

In addition to supplying books, a book supplier will furnish details of specific books in response to library enquiries.

Note, that communications involving external entities are only included where they involve the 'system' process. Whilst a book supplier would communicate with various agencies, for example, publishers and other suppliers - these data flow are remote from the 'system' process and so this is not included on the context diagram.

Data Flow Diagrams – Context Diagram Guidelines
Firstly, draw and name a single process box that represents the entire system.

Next, identify and add the external entities that communicate directly with the process box. Do this by considering origin and destination of the resource flows and data flows.

Finally, add the resource flows and data flows to the diagram.

In drawing the context diagram you should only be concerned with the most important information flows. These will be concerned with issues such as: how orders are received and checked, with providing good customer service and with the paying of invoices. Remember that no business process diagram is the definitive solution - there is no absolute right or wrong.

Zero Diagram

Zero diagram is a chart that describes the process of DFD. This diagram provides a view of the overall system shows that the main function of the process or the flow of data and the external entity. At this level there is a data storage.

Detailed Diagram

Is a diagram that decipher what is the process in the diagram zero level or above.

Numbering level in the DFD:

In one level there should be no more than 7 units and the maximum of 9, when more should be done in the decomposition.

Process Specification
Each process in DFD must have the process specification. in top level method used to describe process by using descriptive sentence. At more level detailed that is under process (functional primitive) required the specification structure.Process Specification will become guide to programmer in coding. Method used in the process specification : * description process in the story * decision table * decision tree

Data flow diagram levels

Level 1 (High Level Diagram)

A Level 1 Data flow diagram for the same system.

This level (level 1) shows all processes at the first level of numbering, data stores, external entities and the data flows between them. The purpose of this level is to show the major high-level processes of the system and their interrelation. A process model will have one, and only one, level-1 diagram. A level-1 diagram must be balanced with its parent context level diagram, i.e. there must be the same external entities and the same data flows, these can be broken down to more detail in the level 1, e.g. the "enquiry" data flow could be spilt into "enquiry request" and "enquiry results" and still be valid.

Level 2 (Low Level Diagram)

A Level 2 Data flow diagram showing the "Process Enquiry" process for the same system.

This level is a decomposition of a process shown in a level-1 diagram, as such there should be a level-2 diagram for each and every process shown in a level-1 diagram. In this example processes 1.1, 1.2 & 1.3 are all children of process 1, together they wholly and completely describe process 1, and combined must perform the full capacity of this parent process. As before, a level-2 diagram must be balanced with its parent level-1 diagram.

Data Dictionary
Data dictionary is a reserved space within a database which is used to store information about the database itself. Data dictionary is also called with a system data dictionary is a catalog of facts and data information needs of an information system. In function to help system agent to interpreting application in detail and organization all of data element that utilized by system exactly so user and system analyst have same understanding basic about entry, output, storage and process. In analysis phase, data dictionary used as communication between system analyst with user. in development system phase, data dictionary used to design input, and report database. Data flow in DFD have the character of globally, boldness more detailed can be seen in data dictionary.
Data dictionary load the followings :

Name of data current: must note that readers who need further explanation about a flow of data can find it easily
Alias: alias or other name of the data can be written when there is
Forms of data: used to segment the data dictionary to use when designing the system
Flow data: indicates from which data flows and where the data
Description: to give an explanation of the meaning of the data flow

Balancing in DFD
Data flow into and out of a process must be the same as the flow of data into and out of the details of the process on the level / levels below. Name of the data flow into and out of the process must match the name of the flow of data into and out of the details of the process. Number and the name of an entity outside the process must be equal to the number of names and entities outside of the details of the process.
Things which must be gave attention to DFD owning than one level:

There are must input balance and output between one level and level next
Balance between level 0 and level 1 seen at input / output of data stream to or from terminal in level 0, while balance between level 1 and level 2 seen at input / output of data stream to/from pertinent process
Data flow name, data of storage terminal and every level must be same if its same object

Prohibition in DFD

Data flow may not from external entity directly to wend another external entity without passing a process.
Data flow may not from direct data deposit go to external entity without passing a process
Data flow may not from direct data deposit go to other data deposit without passing a process
Data flow from one direct process go to other process without passing a data deposit better possible avoided.

References:
- Slide Part 4 - DATA FLOW DIAGRAM by ER Ngurah Agus Sanjaya
- http://www.getahead-direct.com/gwbadfd.htm

- http://en.wikipedia.org/wiki/Data_flow_diagram

fouth Quiz

2009-03-29T05:00:00.000-07:00

FIRST OBSERVATION, ANALISYS REQUIREMENT

Observation

Observations or observation is one of the techniques of collecting data / facts are quite effective to learn a system. Observation is the direct observation of the decision below and the physical environment or a direct observation of activities that are currently running.

The Creator of the Observations The type of information sought during the decision-making behavior of the physical environment is the following:

a. Gather views on what is actually carried out the decision.

b. See directly the relationship between the decision makers with the other organizational members.

c. The influence that caused the decision of the elements of their physical space.

d. Understand the messages sent through the control (such as how to dress, table position)

Observations help confirm or reject the return and see what that was found through interviews, questionnaires.
The steps that need to be done in the activities of a manager's decision:

a. Decide what Observations (activities).

b. Decide on the level of the activities of these concrete Observations.

c. Creating categories is sufficient to capture the main events.

d. Setting up the scale, a list of names or other materials that are appropriate for observation.

e. Decide when making observations.

Each approach to the need to make any Observations have respective advantages and decrease. Sampling allows the analysis time
preparing the interval-specific intervals to observe the activities of the manager. While sampling the observed events display a behavioral context of the integral nature.

An activity observed

At the time of making observations, analysis can also participate or just observe only the people who are doing an activity that observers.
Observation has some good and also the lack of comparison with other data collection techniques.
Goodness of observation are as follows:

a. The data collected through observations tend to have high reliability. Sometimes observations made to check the validity of the data that has been previously obtained from individuals.

b. Can see directly what is done, work that is sometimes complicated to be difficult.

c. Can describe the physical environment of activities, such as the physical layout of equipment, lighting, sound disturbances, and others.

d. Can measure the level of a job, in terms of time needed to complete one unit job particular.

While lack of is follows:
a. Generally observed that people feel upset or uncomfortable, so that will not perform the work with proper operation.
b. Work that is being observed may not represent the level of difficulty of a particular work or special activities that are not always carried out or the volume-volume of a particular activity.
c. Can disrupt the process that is being observed.
d. People are likely to perform the work with better than usual and often cloak- ugliness.

Instructions-instructions that can be considered to make effective observations are as follows:
1. What should be done
to make observations, things to do:
a. Plan first observation to be conducted, including:
• What will observers
• Where is the location of the location of observation
• When will the observation
• Who will conduct the observations
• Who will observers
• How to implement these observations.
b. Ask for permission first from the managers and employees are involved or
c. act with humility (low profile)
d. complete notes during the observation
e. knowledge back with the results of observations of individuals involved.
2. That should not be done, namely:
a. Possible work observers individuals and other individuals.
b. Emphasize work that is not important.
c. Do not make assumptions.

CONCEPT OF INVESTIGATION SYSTEM
There are some point about Investigation System Concept, which the following:

1. Discrepancy between the objective of system and the real condition of system
2. Problem detection report is not real, because:
- the objective is too ideal
- lack of attitude and resources
- system measuring is not accurate
- the objective of system which have to be left behind
- difference between the idle system and the temporary system
3. The Problem solving
- investigation to the system which is running
- getting the ideal system consensus
- choose the best alternative
4. The objective of investigation have to show the problem which in fact is happened

PROBLEMS IN INVESTIGATION SYSTEM
There are some problems when conduct the investigation:
- Time
- Cost
- Science
- Politics
- Intervention

RECOMENDATION

A result from investigation is a recomendation which are the following:
- Does not take any action if the problem does not exist
- Perform system maintenance of the small problems
- Increasing the ability or skills
- Consider modifications to total system
- Putting the problem to occur in the development plan system which immediately made

INVESTIGATION TACTICS

Why we need tactics?
- to find all issues
- Knowing the causes of the emergence of problems
- to determine the appropriate solution
- Done so that all elements of the system can receive a solution without disrupting their activities.

Some of the tactics that can be done:
- Listen to opinions the subject of system
- Do not give the early breakthrough
- Compare the stories from the subject of system concerning in the same case
- Note the logical inconsistency problem

INVESTIGATION TECHNIQUE

Directly:
- The questionnaire
- FAQ
- Observations
Not directly:
- Flow of procedure
- Reviewing documents
- Sample
- Tabular

RECENTLY SYSTEM DESCRIPTION

- Input
- Output
- File
- Data elements
- Transaction volume and document action
- Data flow diagram

ANALYSIS REQUIREMENT

Stage of intensive interaction between the systems analyst with the end user where the system development team show their expertise to get feedback and trust so that the user get a good participation.

4 purpose that want to achieved:
- Explain the complete system
- describing the ideal Information System
- Bringing the ideal information system to a condition at this time to pay attention to the constraints of resources
- Providing encouragement to the confidence of user in the system development

Method:
- FAQ
- Kuisioner
- Observation
- Analysis procedure
- Document observation

Resources problems:
- Time
- Money
- Expertise
- Technology
- External Factors

Document analysis requirement

- Analysis guide: relationship with the end user, process monitoring, problems in data gathering.

- User requirement: real needs, report needs, exercise needs, and new system influence.

- System problem: explain time and cost constraint, skill, technology and external factor.

- Document such as data gathering instrument, statistic consensus, data flow according to physic and logical, first data element in data dictionary.

Generating Systems Alternatives

How to make the system at moment condition more close with the ideal system condition?

- create alternative to solve the information system problem

- Applying best alternative wisely

Strategy option

- Distributed versus centralized processing

Changing information decision from centralized data processing to decentralized end user responsibility center.

- Integrated versus dispersed database

System planner must consider what kind of incoming data to database and incoming to file.

- Surround Strategy of System Development

Environment are important strategy in overtake company case because information system from other company possible different with company at moment.

GENERATING SYSTEMS ALTERNATIVES

How to approach the condition of the system at this time with the condition of the ideal system?
- Create an alternative to solve the problem of information system
- The best alternative is applied wisely

Options Strategies
- Distributed versus centralized processing
Changes in information decision-making from the centralized data processing to the decentralized end user responsibility center
- Integrated versus dispersed database
System designer must consider the data which are entered in the database and into the file
- Surround Strategy of System Development
About important environmental strategy in the case of company takeovers because the information system of other companies may vary with the company at this time.

Options Tactics
- The design is done before the selection operation

Designing Operational Options
The design options can be grouped into:
a. Input
- Online Vs Off Line Data Entry
- Keyed Vs Machine Readable Data Entry
- Centralized Vs Decentralized Data Entry
b. Processing
- Batch Vs Realtime record update
- Sequential Vs Direct Access to records
- Single Vs Multiple Users update of records
c. Output
- Traditional Vs Turn Around Documents
- Structured Vs Inquiry-based reports

SELECTING THE PROPER SYSTEM

a. Comparing tactics: System comparison based on the costs and benefits relatively; There are 3 ways a system is superior to the other, if:
- A's cost is lower than B and both are the same benefits.
- A's cost is lower than B and A yield more advantage than B.
- A and B have the same but the A's cost advantage more than B.

b. Some methods of comparison system
- Break Even point Analyisis
- Payback Period
- Discounted PayBack period
- Internal Rate of Return

c. Category Cost
- Hardware
- Software
- People
- Suppliers
- Teleccommunications
- Physical sites

d. Fee Details
Comparing the cost of information systems through the life of the system, analysis is calculating the cost changes for the future and there are 3 models of cost of information system, namely Linear, and exponentially Step Function.

e. Cost of information system can occur only once and can also occur continously.
- The cost of information systems that occur only once is ontime cost and development cost that occur during the development of the system.
- The cost of information systems that occur on a continual basis are reccuring cost and operational cost where the cost of this is happened when the information system operates every day.

INFORMATION SYSTEM FACTORS
Qualitative factors that lead the performance of a good information system are:
- Reduce errors
- Reduce the time to fix errors
- Reducing the response time from the alternative workstation
- Accelerating the provision of information
- Increase the security system
- Have active update source record
- Increase user satisfaction

CORPORATE STRATEGY FACTORS
- Customer satisfaction
- Level sales increased
- Commitment of consumers and vendors
- Information of marketing products

PRESENTING LEARNING OF SYSTEM

- Conduct a brief presentation
- Reduce technical explanation in detail
- Presenting clearly with visual aids
- If using a model using a tool such as a laptop so that more informative
- Emphasize the benefits of the proposed information system with some alternative that is appropriate conditions experienced by the company.

Decision to continue or No

- If the company decided to develop the information system, the department will do next, namely the System Design Process.

- If then the System Development Life Cycle (SDLC) will be terminated.

- Often the problem will be found with the system and the study and top management will usually ask for the re-employment system to study.

- With the alternative, the decision to repeat a previous stage SDLC or not called Go - No-Go Decision.

- Model will describe some of the stages of the repeated and sometimes the information will make a decision before the repeat stage system of study.

Third Quiz

2009-03-08T04:33:00.001-07:00

Importance of developing an information system

Development of the system may mean preparing a new system for replace the old system or improve the overall system existing.

Importance of developing an information system:

• There is a problem (problem) that arise in the old system

The problems that arise can be:

· Irregularity

· Organizational growth

• To seize the opportunity (opportunities)

Information technology has developed so quickly

• The instructions (directives)

Goal is the development of the system:

To complete the problem (problem) that arise in the old system
The problems that arise can be:

· irregularity in the development of System Organization
In an organization there are some data that must be processed, when Organization developing processed data needed for the system the greater good, if not use the information system so that both Organization growth will be hampered
opportunity

· Growth Organization

Information systems also help in achieving opportunities. And also make it easier to capture opportunities.
Referrals Information system can more quickly provide instruction, for example, confusion is a company looking for documents, the system with the information we provide instructions directly to the system to search for it.

Expected on the new system

Has been developed with the new system, the expected increase will occur, the increase in the new system.

• Performance

Improvement of the performance of the new system which will be most effective. Performance can be measured from the throughput and response time.

• Information

Improving the quality of information presented.

• Economy

Improvement of the benefits or the benefits or a decrease in the cost-reduction.

• Control

Detect and fix errors that occur and will occur.

• Efficiency

Improvement of operational efficiency.

• Services

Improvement of the services provided by the system.

The principle of the development system

· The system is developed for management

· The system developed is a large capital investment
Investment capital should consider 2 things:
1. 1. All alternatives should be investigated
2. 2. Investment should be the best value

· System developed need educated people who
Stages of work and tasks that must be made in the system development process

· The development of the system does not have to order

· Do not be afraid to cancel the project

· Documentation must be available to guide the development of the system

Life cycle development system

1. Stages of Planning System (System Planning)
2. Stages Analysis System (System Analiysis)
3. The design phases of system (System Design)
4. Stages Selection System (System Selection)
5. System Implementation Stages (System Implementation)
6. Stages Care System (System Maintenance)

SI Model development
(Life Cycle SI)

CLASIC life cycle / waterfall model, consists of the phases of planning system (engineering system), needs analysis, design, writing programs, testing and treatment system.
prototyping model, starting with collection needs and improvements, the design quickly, the establishment of prototype, evaluation of customer prototype, repairing prototype and final product.
Rapid Application Development (RAD) model, with business activity began modeling, data modeling, process modeling, generation and application
testing.
Evolutionary model that can be a model incremental or spiral model Incremental model is a combination model sequential linier with prototyping (device word processing software with different versions). While the spiral model of analysis risk. If the analysis shows there is a risk uncertainty of demand, the development system can be stopped.
Engineering generation-to-four (4GT), starting with collection needs, design strategies, implementation and testing using a 4GL.

Waterfall Model

Contains a series of activities such as the process has been described above and presented in a separate process, such as the requirement specification, design implementation software, testing . After each step is defined, this step in the sign off and followed the development in the next step.

The steps that are important in this model is

The analysis and specification

Services, goals and constraints resulting from consultation with users system. Then everything is made in the form that can be understood by users and staff developers.

Design systems and software

Divide the system design process needs to be system software or hardware. The process produces an overall system architecture. Design software, including the functions of system software that may be in the form of the transformation in one or more programs that can be run.

Implementation and unit testing

During this stage, the design software we realize as a complete program or program unit. Test units, including units that each test according to specification.

Integration and system testing

Unit tested and integrated the program into a complete system for making sure that software requirements have been met. After testing, the system was delivered to the customer.

Operation and maintenance

This phase is long. System installed and used. Maintenance, including correction of errors not found in the previous step. Improvements to the implementation of the system and increasing service needs of the new system as found.

Weakness Waterfall

• Iterations frequent cause a new problem
• Client trouble to declare all desires explicitly beginning stage of development.
• The s / w which will be developed known long after the project development begins

Prototype model

provides a method with about a complete system, customers can see the modeling system from the side view and techniques procedural that will be built
On the efficiency development Testing algorithm, the interaction with the OS and user
Discover the top; paper model, work model, program
evaluated by the user and used to process again needs
The developers will learn understand the needs and user desires.
Identify the needs of consumers, systems analyst will be to study feasibility studies and needs to users, including interface model, engineering procedural and technology that will used.

Summary prototype

Identify the needs: analysis needs of the prospective user
Quick design: the creation of a global design to form the s / w instance
Build prototype: the creation of s / w prototype including testing and completion
Evaluate customer: evaluate prototype and refine analyst needs of prospective users
Preparation & implementation: the creation of including the actual design, coding, and testing

The weakness of the prototype

· unconsciousness user that is only a model of the early models is not the end

· Developers sometimes create implementation of the markets.

· techniques and tools that are not optimal in the prototype that will still be used on s / w indeed.

RAD Model

Rapid Application Development
The development of s / w in sequential linier
adaptation do high speed, can be made in pace with the development approach component-based
If the data, clear analysis, and the scope of small
the RAD can be used with both
Often also called a 'high speed version' of waterfall model,
Emphasis on development cycles a short
RAD approach to follow the following phases;
Business modeling, the flow of information from model with the function;
information is affecting business, who appear, who is issue, the information I given, Who processing?
Data modeling; Part of the modeling business defined in a set of data objects.
The characteristics (attributes) of each object identified and related
The process of modeling, the object data will be implemented on the business functions.
Descriptions for the built additional modifications, deletion, or return the data object.
Data modeling; Part of the modeling business defined in a set of data objects.
The characteristics (attributes) of each object identified and related
The process of modeling, the object data will be implemented on the business functions.
Descriptions for the built additional modifications, deletion, or return the data object.
Application generation, Conducting re-use components that have (if possible)
Or make use of return components if required.
Testing / turnover, the RAD emphasizes the use of return and program components are ready to test

Weakness RAD

Model of the (project scale), resources requires good and solid
Requires commitment developer and the same user to be completed quickly accordance with the plan

System development approach

• A structured approach versus classical approach (seen from the methodology used)
• Piecemeal approach versus system approach (seen from the target to be achieved)
• The bottom-up approach versus top-down approach (seen from how to determine the needs of the system)
• Total-system approach versus modular approach (seen from the way the developing)
• Great loop- approach versus evolutionary approach approach (seen from the technology that will be used)

A structured approach versus classical opponent approach

Classical approach

• Stages in SDLC-stage
• Not to include users, system analysts put more emphasis
• The problem: the development of hard, expensive treatment, the possibility of error, success is less assured, the problems in the implementation of

Structured approach

• User involvement from the beginning to determine the needs of the system
• Using tools-tools such as data flow diagrams

Piecemeal approach versus system approach

Piecemeal approach

• Emphasizing on an application or event
• Don’t override the overall target

System approach

• Viewing the system as a whole is unity
• Emphasizing the achievement of overall objectives

Bottom-up approach versus top-down approach

Bottom-up approach

• Starting from the bottom level of the operational
• It is the characteristics of classical
• Known by the term data-analysis

Top-down approach

• Starting from the top-level strategy planning
• It is the characteristics of structured
• Also known to the decision-analysis

Total-system approach versus modular approach

Total-system approach

• Develop a system simultaneously and comprehensively
• It is the characteristics of classical approach

Modular approach

• break a complex system into parts of a simple
• System to be developed on time, easy to understand and kept
• It is the characteristics of structured

Great loop- approach versus evolutionary approach approach

great-loop approach approach

• Develop a system simultaneously using the advanced technology
• At risk and spend a lot of high cost

evolutionary approach approach

• Applying advanced technology for application-specific applications
• Developed for the needs
• Save the cost and can follow the development of technology

System Development Methodology and algorithm

Methodology is: Unity methods, procedures, concepts of employment, rules and postulate used by science knowledge, art or other disciplines.

Method is: One way / technique to do something systematic. System development methodology that is usually created or proposed by:

Book Author
Researcher
Consultant
House System
Software factory

algorithm is: Sort-order the procedure to solve a problem

System Development Methodology is classified into 3 groups:

Functional Decomposition Methodologies (Functional Methodology fission) HIPO (Hierarchy Input Process Output), SR (Stepwise Refinement), Isr (Iterative Stepwise Refinement), Information Hiding
Data Oriented Methodologies (Data Oriented Methodology) Data Flow Oriented Methodologies: SADT, Composite Design, SSAD Data Structure Oriented Methodologies: JSD, W / O
Prescriptive Methodologies ISDOS, PLEXSYS, PRIDE, spectrum

Classification of development methodology

• Functional decomposition

– Emphasizing the breakthrough system subsystem

– Example : HIPO, Stepwise refinement, iterative stepwise refinement, information hiding

• Data-oriented

– Emphasizing on the characteristics of the data processed

• Data-flow oriented: modules according to the type of data elements

• Data-structure oriented: structure of the input and output

• Prescriptive

– Usually provided by the manufacturer of the software

Development Tools And Engineering System

Can be:
1. Graphic HIPO, DFD, SADT, W / O, JSD
2. Chart

Chart to describe the activity
Chart to describe the layout
Chart to describe the relationship of personnel

Techniques used for the development of the system include:

Engineering Project Management: CPM, pert
Technical Fact Finding: Interview, Observation, questionnaire, Sampling
Engineering Cost Analysis
Technical Running Meetings
Technical Inspection

The cause of the failure of the system:

Lack of adjustment of system
Failure to set the user needs and involving users
Less than perfect quality evaluation and cost analysis
The existence of damage and error design
The use of computer technology and software do not planned
and the technology is not appropriate
Development of systems that can not be kept
Implementation of a poorly planned and implemented

It is used:

SDLC
Prototype
Device modeling
Engineering Project Management
CASE
JAD
The involvement of the

Techniques used in the development a system

• Technical project management

Technique that is used to scheduling project, for example : CPM dan PERT

• Fack finding technique

to collect data and determine and find the facts

– interview

– Observation

• cost-benefit or cost-effectiveness analysis

• Technical meetings run

• walkthrough

Analyst System and programmer

Systems analyst is a person who analyzes the system with learning
problems that arise and determine the needs of users and
identify solutions that reasonless (better understand the aspects
business and computer technology).
Other names: system designers, business Analyst, systems consultant, systems
engineer, software engineer, systems programmer Analyst, information system
engineer.

Programmer who is writing code for an application program
based on a design made by the system analysts (better understand
computer technology).

Duties and responsibilities:

System analyst:

Analyst responsibility system not only on making a computer program, but the system overall.
Knowledge systems analyst must be knowledgeable, not only in computer technology, but also in the field of application
Systems analyst job in making the program limited to the solution outline the problem.
System analyst working relationship involving many people, not limited fellow analyst on the system, but also the programmers and system managers.

Programmer:

Responsibility limited to the programmer making a computer program.
Knowledge is quite limited to programmers computer technology, the system computers, utilities and languages program is required.
Programmers job is technical and must be appropriate in the making program instructions.
Programmers do not work the relationship with people, limited on fellow programmers and system analysts who prepare building design (specification) of the program.

Knowledge and skill system analyst

Systems analyst must have a broad knowledge and expertise that is needed. Some analysts agree that the knowledge-knowledge and expertise is necessary for following a good system analyst:
a. Knowledge and technical expertise on data processing, technology
computer and computer program
Technical expertise is to be held, including expertise in
the use of tools and techniques for developing software applications
and expertise in using computers.
Technical knowledge should include knowledge of the owned
hardware, data communications technology, computer languages,
operating system, Utilities, packages and other software.
b. Knowledge about business in general
Business application is an application that is now most widely applied,
the systems analyst must have knowledge about this. Knowledge
This is required so that the system analyst can communicate with users
system. Knowledge of this business include financial accounting,
cost accounting, management accounting, management control system,
marketing of production, personnel management, finance, organizational behavior,
company policy and business aspects of the other.
c. Knowledge of methods kuantitatip
In building models of applications, many analysts use the system
quantitative methods such as linier programming, dynamic programming,
regresion, network, decision tree, trend, simulation.
d. Experts solve complex problems into smaller problems
Systems analyst must have the ability to put the problem -
complex problems faced by the business, split problem
to the part-part, and then must analysis string can return to a system that can overcome
the problems are.
e. Experts to communicate and build relationships
Systems analyst must have the ability to make good communication
verbally and in writing. Expertise is required in the interview,
presentations, meetings and preparing reports.
f. Understand information systems development methodology
Human factors that are critical in the system and the human character
with each other differently. Analysts in the rigid system that builds
working relationship with other personnel, personnel involved, will
create jobs to be ineffective. Moreover, if the systems analyst
can not build a good relationship with the system, it will be
do not get support from the system or the management and
tendency of the system will hard.

Team development of the system (I)

System development project in the small and simple, it is possible only
there is a system analyst programmer as a trap (analyst / programmer)
or a double as the programmers systems analyst (programmer / analyst).
However, the development project for a large system or complex,
This work is usually done by people in the form of a team.
Members of the team developing this system depends on the size of the
the scope of the project will be treated. The team in general can be
of personnel, personnel as follows:
1. Manager analyst system (manage systems of Analyst)
Manager systems analyst is also known as project coordinator and
have duties and responsibilities as follows:
a. As a coordinator or head of the team developing the system
b. Direct, control and manage the system development team members
another.
c. Make a schedule of the project development system that will
done.
d. Responsible for defining the problem, feasibility studies, design
system and application.
e. Provide recommendations system.
f. Represent the team to deal with the system in terms of
negotiations-negotiations and the provision of advice-giving to
management and system users.
g. Create reports of project progress (progress report).
h. Reviewing and re-examine the work of the team.
2. Chairman of the systems analyst (systems Analyst leads)
Chairman of the systems analyst is usually served as deputy manager of the analyst
system. Duties of the task is to help managers and system analysts
represent the system when the manager obstruction analyst.
3. Senior systems analyst
Senior systems analyst (senior systems Analyst) is a systems analyst
that have been experienced.
4. Junior systems analyst (systems Analyst junior)
Analysis system is a junior systems analyst who has not experienced
still need assistance and guidance-systems analyst from a more
senior. Junior systems analyst is often also called the system analyst
still trained (systems Analyst trainee).
5. Senior application programmers (senior applications programmer)
Programmers Application is a senior computer programmer who has
experienced with the task of design specifications from the application program
and coordinate the work of other programmers. Application programmer
senior sometimes also referred to the programmer / analyst.
6. Application programmer (application programmer)
Application programmers are computer programmers are experienced enough
duty and can do without having to be directly again.
7. junior application programmers (junior applications programmer)
Application programmers junior is a computer programmer who has not
experienced and are still under the direct guidance from the programmer
more senior. junior application programmers are usually only involved in the
modules making a simple program, such as making
forms of I / O. Application programmer junior is often called
with the application programmer who still trained (applications programmer
trainee).

Team development of the system (II)

1. User System
a. Users
As end-user (operator) and the user-manager who oversees the work
end-user.
b. Management
Hold role in agreeing the development plan system
and the provision of funds.

2. System Designer
a. Project Coordinator
Responsible so that the team can work in harmony and optimal
and control the implementation so that appropriate plans.
b. System Analyst & Design
Personnel who provide solutions and designing the new system.
c. Programmer
Personnel who create programs based on design the system analyst.
d. Network Designer
Responsible for network design, such as LAN, MAN, WAN.
e. Technician (Hardware)
Personnel configuration-set the hardware configuration appropriate
in order to work optimally.
f. Database Administrator
Personnel responsible against a database system, covering
pattern data structure, data integrity, rights to provide access to the user,
backup, recovery and optimizing database performance.
g. Documenter
Personnel who make the system documentation, the book covers the operational
applications, and technical systems.
h. Software Tester
Personnel who ensure that the application programmer who created
according to specifications.
i. Graphic Designer
Personnel who have expertise in designing for applications based
GUI (Graphic Interface).

References

http://en.wikipedia.org/wiki/Waterfall_model

www.klasiber.net/file.php/1/PSI_I.pdf

Analisis dan Desain Sistem Informasi

2009-02-20T22:01:00.000-08:00

INFORMATION SYSTEM
In a general sense, the term information system (IS) refers to a system of persons, data records and activities that process the data and information in an organization, and it includes the organization's manual and automated processes. In a narrow sense, the term information system (or computer-based information system) refers to the specific application software that is used to store data records in a computer system and automates some of the information-processing activities of the organization.
SYSTEM

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.
• Process and transformation 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.

Information 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:

* Information Systems Strategy
* Information Systems Management
* 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 systems
* Management information systems
* Decision support systems
* Expert systems
* Business intelligence

Information System development

The IT Department partly governs the information technology development, use, application and influence on a business or corporation. A computer based information system, following a definition of Langefors is:

* a technologically implemented medium for recording, storing, and disseminating linguistic expressions,
* as well as for drawing conclusions from such expressions.

which can be formulated as a generalized information systems design mathematical program.