Logical Data Modeling Assignment PDF

Running Head: LOGICAL DATA MODELING 0
Logical Data Modeling
Individual assignment
Student Name

Logical Data Modeling 1
Table of Contents
Task 1(a):.....................................................................................................................................2
Task 1(b):.....................................................................................................................................3
Task 2...........................................................................................................................................4
Task 3...........................................................................................................................................5
References........................................................................................................................................8

Logical Data Modeling 2
Task 1(a):
Database is a collection of data in record format. They are used for managing all the data at a
single place. Database is separated in four parts, which are:
 Hierarchical databases.
 Network databases.
 Relational databases.
 Object-oriented databases
Hierarchical database is structure as a tree. It uses parent child-relationship for managing all the
records. It store information as a table format. It follows relational model for arranging the data,
such as levels. RDM mobile and Information Management System of IBM are based on this
approach.
Network database is used for large databases. In this database, different connections can be made
for more efficiency. It is also follow the hierarchical database and maintain a parent-child
relationship in between different records. All the records are interconnected and it is having fast
access of records. It follows network model for managing records and it maintain a many-to-
many relationship in the data. Integrated Data Store (IDS) is an example of Network database.
Relational databases maintain a relational relationship between the data. It uses key fields for
making connection between different tables for accessing the data, such as primary key, foreign
key. It is more reliable than hierarchical and network databases, as it is reduce redundancy
between data. MySQL, PostgreSQL SQL Server, MySQL, Oracle are examples of relational
databases.
Object oriented databases are following object-oriented model for storing data and accessing data
from the databases. It uses object oriented programming language concept for making it more
consistent.
Flat files are also used for data storage, such as text file, binary files in C and C++ programming
languages.

Logical Data Modeling 3
Task 1(b):
Database systems are well documented and maintained data in a manner format as relational
databases are used mature technologies. Database standard are well defined and they are
accepted at all the processes. Databases are helping of data warehouses for collection of data. All
the databases are following the ACID properties, which are Atomicity, Consistency, Isolation,
and Durability (Demba, 2013).
Relational database is having a better data structure. It is fast in accessing the network. Language
of relational databases is simple to understand for a developer. Speed of queries launces and
getting results from databases is fast.
Relational database management system is not good in case of unstructured data, because of
schema and type constraints. So, it is not used in IoT events loads. These databases are scalable
and fault tolerant. These are used in different distributed systems, such as IoT. MySQL databases
are also having disadvantages because of range of formats and constraints of each database.
Hierarchical database is providing fast access of data as it defined relationship in advance
between parent and child nodes, but it has a disadvantage that there is no direct access between
the child nodes.
A big disadvantage of hierarchical database is one parent per child. It is complex in structure and
navigation. It is not supported many to many relationships.
An object-oriented database is used two elements, first is piece of data, and second is method, for
handing data. However, it is more expensive to develop and cost effective as compared to other
databases.

Logical Data Modeling 4
Task 2
Logical Data Modeling
Conceptual Data Model
(CDM)
Logical Data Model (LDM) Physical Data Model (PDM)
It have so many constructs It includes entities, attributes,
and their relationship
It includes keys, validation
rules, tables, row, columns,
domains, triggers, and
constraints for data access.
It used non-technical names
for better understanding for
managers.
It uses business names for the
different fields
It uses defined names for
tables and columns.
It uses architectural
description for non-technical
terms.
It is not dependent on
technology.
It provides fast data access
using primary keys, and
indices.
It is not proper for
normalization
It can be normalized up to
fourth normalization form.
It normalized sometimes as
needed. It is usually not de-
normalized.
Logical data modeling is used for maintaining tailored based database systems, which are used
entities, attributes and relationship for completing a business function. It is also used domain
model objects for managing relationship between data. Logical data modeling is use for

Logical Data Modeling 5
converting logical model into physical data model. In the figure, a logical data model is
maintained. It is showing a relationship between, time, product, sales, and store. It is a data
model for a specific problem domain. It is used as a data structures, such as XML tags, relational
tables, and object oriented classes. It is not following semantic model for managing the data,
such as conceptual data model ( Howe, 2001).

Logical Data Modeling 6
Entity relationship diagram is showing relationship between the different attributes of an entity, which is
used in the project. Few steps are required for creating an ERD diagram, which are:
1. Each entity is mention in the boxes, such as tickets, passenger.
2. Make connectivity in model by the help of drawing lines.
3. Specifically defines attributes of each entity in the model
4. Review ERD with the requirements of business and technical stakeholders
5. Repeat all the steps until a proper representation is not done
Above diagram is entity relationship diagram for travelling management system. Each entity is
defined in the box and their attributes are connected with them.

Logical Data Modeling 7
Task 3
As a database develops, I thought it was the best way to develop a project's logical data model
before physical implementation. It provides a lot of understanding of the data storage concept. It
includes all entities and attributes with the help of a relationship. Normalization is occurs at this
level. According to logical data model normalization is easy for a developer.
Logical Data Modeling:
A data model is a concept, which is used for describing the analysis and design part of the
project using different approaches, such as flow charts, and entity relationship diagrams. It is an
important process for better understanding about the project before staring coding parts. It is just
like blue prints of a building (Jacob, 2017).
Source: (Jacob, 2017)
It is a continue process until target not meet. It is uses for understanding about the requirements
at each step.
There are three types of model for understanding requirements.

Logical Data Modeling 8
Source: (Jacob, 2017)
It is a well-documented model for analyzing the error and make changes before coding part has
been written. These models are used for viewing the same data in different format and identify
changes based on them. It is used for the information gathering from requirements. It is very
useful model for understanding the requirements. It is an extension of the conceptual model. It
describes data in more details. It is a normalized model, in which primary keys and foreign keys
are linking with the different entities.

Logical Data Modeling 9
References
Howe, D., 2001. Data Analysis for Database Design. 3 ed. Oxford: Butterworth-Heinemann.
Auer, D. & Kroenke, D., 2010. Database Concept. 5 ed. London: Prentice Hall.
Chmura, . A. & Heumann, J. M., 2007. Logical Data Modeling: What it is and how to Do it. 5
ed. New York: Springer Science & Business Media..
Demba, M., 2013. Algorithm for relational database normalization up to 3NF. International
Journal of Database Management Systems, 5(3), p. 39.
Srikant, S., 2006. Logical data modeling: A key to successful enterprise data warehouse
implementations. Information Management, 16(9), p. 13.
Teorey, T. J., Lightstone, S. S., Nadeau, T. & Jagadish, H. V., 2011. Database modeling and
design: logical design. 3rd ed. Oxford: Elsevier.