Data Models: Definition, Concept, Approaches, Types. Data Processing Lesson Note

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Subject: Data Processing

Topic: Data Models (I)

Instructional Objectives: by the end of the lesson, the learners should be able to:

Define the term data Models;

Mention and explain the types of data Models;
State the Concept of Data Modeling;
Describe the approaches to data modeling.

INTRODUCTION
The main purpose of establising the system development life cycle (SDLC) was to produce a set of programs that automates a business process. Processing was the key drivers for information systems, not the data or information. As technology and complexity of system grew, methodologies and modelling techniques were invented to improve the quality of the deliverables and to ensure that inexperienced programmers could follow repeatable SDLC processes.

Definition of Data Modelling
Data Modeling is the process of structuring and organizing data. It is used to show the data structure and data relationship found within the database system. The data structures are then typically implemented in a database management system. In addition to defining and organising the data, data modelling may also impose constraints or limitations on data placed within the structure.
Managing large quantities of structured and unstructured data is a primary function of information systems. Data Models describe structured data for storage in data management system such as relational databases. They do not describe unstructured data such as word processing documents, email messages, pictures, digital audio and video.

Concept of Data Modelling
Data modelling techniques and tools help to capture and translate complex system designs into easily understood representation of data flows and processes, creating a blueprint for construction and re-engineering.
Data Models provide a structure for data used within information system by providing specific definition and format.
Data Model shows the dataflow and logical interrelationship among different data elements.
Compatibility of data can be achieved, if a data model is consistently used across the systems.
The term data model actually refers to two different things: a description of data structure and the way data are organized using Database Management System (DBMS).

Data Structure: A data model describes the structure of the data within a given domain and, by implication, the underlying structure of that domain itself.

Data Organisation: A data Model also describes how to organized data using a database management system.

APPROACH IN DATA MODELLING
There are three different approaches to data modelling, these are:

Conceptual Data Modelling: This data modelling shows the logical nature of data and how it is represented. It identifies the highest level relationships between different entities. This is the first step in organising the data requirements. Its focus is on what is represented in the database rather than how it is represented.

Logical Data Modelling: The logical Data modelling illustrates the specific entities, attribute and relationships involves in a business function. It serves as the basis for the creation of the physical data model.

Physical Data Modelling: The physical Data Modelling represents an application and database-specific implication of a logical data model and describes the physical means used to store data.
Implementation Model: the implementation model deals with how the data are represented in the database. That is how the data structures are implemented to show what is modelling. Implementation model handles data based organisation in the form of relational database, network database, hierarchical database, file inversions etc, all of which are discussed in lesson 2 below.

Lesson Evaluation / Test

Define data modelling.
Describe Data Structure and data Organization.
Mention three (3) approaches to Data modeling.
State the major concept of Data Modeling.

Lesson 2: Types of Data Modelling

TYPES OF DATA MODELLING
Flat Model: The flat Model (or Table) model consists of a single, two-dimensional array of data elements, where all members of a given column are assumed to be similar in values, and all member of row are assumed to be related to one another.

HIERARCHICAL MODEL: The idea behind this model was to converge smaller units to form one big unit in an ordered arrangements known as hierarchical structure. In this type of model, data is organized into an upside-down tree-like structure, implying a single upward link in each record to describe the nesting, and a sort field to keep the records in a particular order in each same-level list. Hierarchical structures were widely used in the early mainframe database management systems.

Network Model: This model organizes data using two fundamental construct, called records and sets. Records contain fields, and sets define one-to-many relationships between records: one owner, many members.

It is similar to those used for linked list. The links are used to express relationship between different items of data.

Relational Model: this model deals with collection of related entities and it is purely logical.

The relational model or relational data base model is based on first-order predicate logic. Its core idea is to describe a database as a collection of predicates over a finite set of predicate variables, describing constraints on the possible values and combinations of values.

Object-Relational Model: The object relational model is similar to relational database model, but objects, classes and inheritance are directly supported in database schemas and in the query language. An object-relational database can be said to provide a middle ground between relational databases and object-oriented databases (OODBMS)

Star Schema: The star schema is the simplest style of data warehouse schema. The star schema consists of a few “fact table” (possibly only one, justifying the name) referencing any number of dimension tables”. The star schema is considered an important special case of the snowflake schema.