System Models
System models are graphical representation that describes business processes, the trouble to be solved and the system that is to be urbanized.
One can use models in the analysis process to develop an understanding of the existing system that is to be replaced or enhanced or to specify the new system that is required.
For example,
1) An exterior perspective, where the context or environment of the system is modeled.
2) A behavioral perspective, where the behavior of the system is modeled.
Types of Model
Different types of system are based on different approaches to abstraction. A data flow model, e.g., concentrates on the flow of data and the functional transformation on that data .It leaves out details of the data structure.
Examples of Types of System Models
1) Data Flow Model: Data flow models show the principal sub-system that make-up a system.
2) Composition Model: A composition or aggregation model shows how entities in the system are composed of other entities.
3) Architectural Model: Architectural models show the principal sub-system that make-up a system.
4) Classification Model: Object class/inheritance diagrams show how entities have common characteristics.
5) Stimulus-Response Model: A stimulus-response model, or state transition diagram. Shows how the system reacts to internal and external events.
One can use models in the analysis process to develop an understanding of the existing system that is to be replaced or enhanced or to specify the new system that is required.
For example,
1) An exterior perspective, where the context or environment of the system is modeled.
2) A behavioral perspective, where the behavior of the system is modeled.
Types of Model
Different types of system are based on different approaches to abstraction. A data flow model, e.g., concentrates on the flow of data and the functional transformation on that data .It leaves out details of the data structure.
Examples of Types of System Models
1) Data Flow Model: Data flow models show the principal sub-system that make-up a system.
2) Composition Model: A composition or aggregation model shows how entities in the system are composed of other entities.
3) Architectural Model: Architectural models show the principal sub-system that make-up a system.
4) Classification Model: Object class/inheritance diagrams show how entities have common characteristics.
5) Stimulus-Response Model: A stimulus-response model, or state transition diagram. Shows how the system reacts to internal and external events.
Context Model
Behavioral Models
Behavioral models are used to portray the overall behavior of the system.
Two most prominent types of behavioral models:
1) Data Flow Models: Data flow models, which model the data dispensation in the system, Most business systems are primarily determined by data. They are controlled by the data inputs to the system. A data flow model may be all that is needed to symbolize the behavior of these systems.
They are an perceptive way of showing how dada is processed by a system. At the analysis level, they should be used to model the way in which data is processed in the accessible system.
2) State Machine Models: A state machine model describes how a system reacts to internal or external events. The state machine model explains system states and measures that cause transitions from one state to another. It does not prove the flow of data inside the system. This type of model is often used for modeling real-time systems because these systems are often driven by stimuli from the system’s environment.
Behavioral models are used to portray the overall behavior of the system.
Two most prominent types of behavioral models:
1) Data Flow Models: Data flow models, which model the data dispensation in the system, Most business systems are primarily determined by data. They are controlled by the data inputs to the system. A data flow model may be all that is needed to symbolize the behavior of these systems.
They are an perceptive way of showing how dada is processed by a system. At the analysis level, they should be used to model the way in which data is processed in the accessible system.
2) State Machine Models: A state machine model describes how a system reacts to internal or external events. The state machine model explains system states and measures that cause transitions from one state to another. It does not prove the flow of data inside the system. This type of model is often used for modeling real-time systems because these systems are often driven by stimuli from the system’s environment.
Data Models
Most great software systems make use of a large database of information. In some cases, this database is autonomous of the software system. An imperative part of system modeling is significant the logical form of the data processed by the system. These are sometimes called semantic data models.
Categories of Data Models:
1) Flat Model: This may not sternly qualify as a data model. The flat model surrounds of a single, two-dimensional array of data elements, where all members of a precise column are tacit to be correlated values, and all members of a row are assumed to be related to one another.
2) Hierarchical Model: In this model data is structured into a tree-like structure, implying a single upward link in all record to describe the nesting, and a sort field to maintain the records in a particular order in each same-level list.
3) Network Model: This model organizes data using two fundamental constructs, called records and sets. Records enclose fields, and sets classify one-to-many relationship between records: one owner, many members.
4) Relational Model: Relational Model is a database model based on first-order predicate logic. Its core idea is to depict a database as a collection over a predetermined set of predicate variables, relating constraints on the possible values and combinations of values.
5) Object-Relational Model: comparable to a relational database model, but objects, classes and inheritance are straightforwardly supported in database schemas and in the query language.
6) Semantic Data Model: A semantic data model in software engineering is a technique to define the meaning of data within the context of its inter-relationships with other data. A semantic data model is an abstraction which defines how the stored symbols relate to real world. A semantic data model is sometimes called a conceptual data model.
Categories of Data Models:
1) Flat Model: This may not sternly qualify as a data model. The flat model surrounds of a single, two-dimensional array of data elements, where all members of a precise column are tacit to be correlated values, and all members of a row are assumed to be related to one another.
2) Hierarchical Model: In this model data is structured into a tree-like structure, implying a single upward link in all record to describe the nesting, and a sort field to maintain the records in a particular order in each same-level list.
3) Network Model: This model organizes data using two fundamental constructs, called records and sets. Records enclose fields, and sets classify one-to-many relationship between records: one owner, many members.
4) Relational Model: Relational Model is a database model based on first-order predicate logic. Its core idea is to depict a database as a collection over a predetermined set of predicate variables, relating constraints on the possible values and combinations of values.
5) Object-Relational Model: comparable to a relational database model, but objects, classes and inheritance are straightforwardly supported in database schemas and in the query language.
6) Semantic Data Model: A semantic data model in software engineering is a technique to define the meaning of data within the context of its inter-relationships with other data. A semantic data model is an abstraction which defines how the stored symbols relate to real world. A semantic data model is sometimes called a conceptual data model.
About The Author:
Aarif Habeeb is a SEO Expert in Jaipur, Analytics and Digital Marketing Evangelist. He is a founder of Aarif Habeeb & Co. a Digital Marketing Agency. Over the last 3 years, Aarif has successfully developed and implemented online marketing, SEO, and conversion campaigns for startups and businesses of all sizes. He spends his days doing digital marketing, Industry research and blogging about content marketing, SEO, website Design. Feel free to connect with Him.
Aarif Habeeb is a SEO Expert in Jaipur, Analytics and Digital Marketing Evangelist. He is a founder of Aarif Habeeb & Co. a Digital Marketing Agency. Over the last 3 years, Aarif has successfully developed and implemented online marketing, SEO, and conversion campaigns for startups and businesses of all sizes. He spends his days doing digital marketing, Industry research and blogging about content marketing, SEO, website Design. Feel free to connect with Him.