Notes #7 - Model Based Management System

8/9/2019 Notes #7 - Model Based Management System

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Types of models

Database model

A database model is a theory or specification describing how a database is

structured and used. Several such models have been suggested. Commonmodels include

Flat model

Hierarchical model

Network model

Relational model

Object-relational model

Concept Oriented Model

Star schema

Data Structure Diagram

A data structure diagram (DSD) is a diagram and data model used to describe

conceptual data models by providing graphical notations which document

entities and their relationships, and the constraints that binds them. The

basic graphic elements of DSDs are boxes, representing entities, and arrows,

representing relationships. Data structure diagrams are most useful for

documenting complex data entities.


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Entity-relationship model

An entity-relationship model is an abstract conceptual data model (orsemantic data model) used in software engineering to represent structured

data. Entity relationship models (ERMs) produce a conceptual data model of a

system, and its requirements in a top-down fashion. There are several

notations for data modeling. The actual model is frequently called "Entity

relationship model", because it depicts data in terms of the entities and

relationships described in the data.


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Geographic data model

A data model in Geographic information systems is a mathematical construct

for representing geographic objects or surfaces as data. For example, the

vector data model represents geography as collections of points, lines, and

polygons; the raster data model represent geography as cell matrixes that

store numeric values; and the Triangulated irregular network (TIN) data

model represents geography as sets of contiguous, non-overlapping triangles.

Generic data model

These models are generalizations of conventional data models. They definestandardized general relation types, together with the kinds of things that

may be related by such a relation type. Generic data models are developed

as an approach to solve some shortcomings of conventional data models. For

example, different modelers usually produce different conventional data

models of the same domain. This can lead to difficulty in bringing the models

of different people together and is an obstacle for data exchange and data

integration. Invariably, however, this difference is attributable to different


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levels of abstraction in the models and differences in the kinds of facts that

can be instantiated (the semantic expression capabilities of the models). The

modelers need to communicate and agree on certain elements which are to

be rendered more concretely, in order to make the differences less

significant.

Semantic data model

A semantic data model in software engineering is a technique to define the

meaning of data within the context of its interrelationships with other data. A

semantic data model is an abstraction which defines how the stored symbols

relate to the real world. A semantic data model is sometimes called a

conceptual data model.

More Related models

Data flow diagram

A data flow diagram (DFD) is a graphical representation of the "flow" of data

through an information system. It differs from the flowchart as it shows the

data flow instead of the control flow of the program. A data flow diagram can

also be used for the visualization of data processing (structured design). Data

flow diagrams were invented by Larry Constantine, the original developer of

structured design, based on Martin and Estrin's "data flow graph" model of

computation. It is common practice to draw a context-level Data flow diagram

first which shows the interaction between the system and outside entities.

The DFD is designed to show how a system is divided into smaller portions

and to highlight the flow of data between those parts. This context-level Dataflow diagram is then "exploded" to show more detail of the system being

modeled.

Information model

An Information model is not a type of data model, but more or less an

alternative model. Within the field of software engineering both a data model

and an information model can be abstract, formal representations of entity

types that includes their properties, relationships and the operations that can

be performed on them. The entity types in the model may be kinds of real-

world objects, such as devices in a network, or they may themselves beabstract, such as for the entities used in a billing system. Typically, they are

used to model a constrained domain that can be described by a closed set of

entity types, properties, relationships and operations.

Object model


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An object model in computer science is a collection of objects or classes

through which a program can examine and manipulate some specific parts of

its world. In other words, the object-oriented interface to some service or

system is said to be the object model of the represented service or system.

For example, the Document Object Model (DOM) is a collection of objects that

represent a page in a web browser, used by script programs to examine anddynamically change the page. E.g., there is a Microsoft Excel object model for

controlling Microsoft Excel from another program.

Object Role Model

Object Role Modeling (ORM) is a method for conceptual modeling, and can be

used as a tool for information and rules analysis. Object Role Modeling is a

fact-oriented method for performing systems analysis at the conceptual level.

The quality of a database application depends critically on its design. To help

ensure correctness, clarity, adaptability and productivity, information

systems are best specified first at the conceptual level, using concepts andlanguage that people can readily understand.

OR Models

Applying the operations research and management science (OR/MS) models to

solve managerial problems has gained in popularity as the computing power

advances and more computer systems are designed for assisting the usage of

OR/MS models. Many of these systems are designed with one specific model in

mind, for example, linear programming or the EOQ model. Due to their lack ofversatility, users have to know which OR/MS model to use in order to choose the

right package. Thus, these systems usually provide nothing more than speeding up

the process from getting inputs to a model through outputs generated.

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Notes #7 - Model Based Management System