Data Modeling

OBJECTIVES

A data model may be a conceptual representation of the info structures that are required by a database. the info structures include the info objects, the associations between data objects, and therefore the rules which govern operations on the objects. because the name implies, the info model focuses on what data is required and the way it should be organized instead of what operations are going to be performed on the info. To use a standard analogy, the info model is like an architect’s building plans.

a knowledge model is independent of hardware or software constraints. instead of attempt to represent the info as a database would see it, the info model focuses on representing the info because the user sees it within the “real world”. It is a bridge between the concepts that structure real-world events and processes and therefore the physical representation of these concepts during a database. Data modeling is the process of making a knowledge model by applying formal data model descriptions using data modeling techniques. In simpler words, data modeling is the process of deciding the way to store digitized information during a logically structured electronic database. After reading this lesson, you’ll be able to:

• understand the concept of knowledge modeling and their role in conceptual database design;
• understand the varied data models;
• become conversant in electronic database technology; and
• grasp the steps involved in designing databases.

INTRODUCTION

One fundamental characteristic of the database approach is that it provides some level of knowledge abstraction by hiding details of knowledge storage that aren’t needed by most database users. a knowledge model — a set of concepts which will be wont to describe the structure of a database — provides the required means to realize this abstraction. By the structure of a database, we mean the info types, relationships, and constraints that ought to hold on the info. Most data models also include a group of basic operations for specifying retrievals and updates on the database.

Data models are methods by which data is structured to represent the important world and therefore the manner during which the info is accessed. These models provide other ways of picturing the relationships and function frameworks for mapping the conceptual schema of a database. Data models support data and computer systems by providing the definition and format of knowledge. If are often “> this is often done consistently across systems then compatibility of knowledge can be achieved. If equivalent data structures are wont to store and access data then different applications can share data.

A data model comprises of:
• a knowledge structure
• a group of integrity constraints
• operations related to the info structure

Data Modeling 

An effective data model completely and accurately represents the info requirements of the top users. it’s simple enough to be understood by the top user yet detailed enough to be employed by a database designer to create the database. The model eliminates redundant data, it’s independent of any hardware and software constraints, and may be adapted to changing requirements with a minimum of effort. Data modeling may be a bottom-up process. A basic model, representing entities and relationships, is developed first. Then detail is added to the model by including information about attributes and business rules.

Data modeling

is additionally a way for detailing organizations’ requirements for a database. it’s sometimes called database modeling because a knowledge model is eventually implemented during a database. it’s a way wont to define and analyze data requirements needed to support the varied processes of a corporation. the info requirements are recorded as a conceptual data model with associated data definitions. Data modeling defines not just data elements, but their structures and relationships between them.

Data modeling techniques and methodologies are wont to model data in a standard, consistent, predictable manner so as to manage it as a resource. the utilization of knowledge modeling standards is strongly recommended for all projects requiring a typical means of defining and analyzing data within a corporation. Data modeling is completed to:

• to manage data as a resource;
• for the mixing of data systems
• for designing databases/data warehouses

Data modeling could also be performed during various sorts of projects and in multiple phases of projects. Data models are progressive; there’s no such thing because of the final data model for a business or application. Instead, a knowledge model should be considered a living document that will change in response to a changing business. the info models should ideally be stored during a repository in order that they will be retrieved, expanded, and edited over time. Whitten (2004) determined two sorts of data modeling.

In summary, a knowledge model may be a plan for building a database. To be effective, it must be simple enough to speak to the top using the info structure required by the database yet detailed enough for the database design to use to make the body.

• Strategic data modeling: this is often a neighborhood of the creation of an information systems strategy, which defines an overall vision and architecture for information systems. Information engineering may be a methodology that embraces this approach

• Data modeling during analysis: In systems analysis, logical data models are created as a part of the event of the latest databases.

COMPONENTS OF a knowledge MODEL
The data model gets its inputs from the design and analysis stage. Here the modeler, alongside analysts, collects information about the wants of the database by reviewing existing documentation and interviewing end-users. the info model has two outputs. the primary is an entity-relationship diagram that represents the info structures during a pictorial form. Because the diagram is definitely learned, it’s a valuable tool to speak the model to the end-user. The second component may be a data document.

This document describes in detail the info objects, relationships, and rules required by the database. The dictionary provides the detail required by the database developer to construct the physical database.

IMPORTANCE of knowledge MODELING
Data modeling is perhaps the foremost labor-intensive and time consuming a part of the event process. The goal of the info model is to form sure that all data objects required by the database are completely and accurately represented. Because the info model uses easily understood notations and tongue, it is often reviewed and verified as correct by the end-users. the info model is additionally detailed enough to be employed by the database developers to use as a “blueprint” for building the physical database.

the knowledge contained within the data model is going to be wont to define the relational tables, primary and foreign keys, stored procedures, and triggers. A poorly designed database would require longer within the long-term. Without careful planning, you’ll create a database that omits data required to make critical reports, produces results that are incorrect or inconsistent, and is unable to accommodate changes within the user’s requirements.
DATA MODELING PROCESS
Data Modeling is the initiative within the process of database design. This step is usually considered as a high-level and abstract design phase (conceptual design). The aim of this phase is to:

• Describe what data is contained within the database (e.g. entities: students, lecturers, courses, subjects, etc.)
• Describe the relationships between data items (e.g. Students are supervised by Lecturers; Lecturers teach Courses )
• Describe the constraints on data (e.g. Student Number has exactly 8 digits; a topic has 4 or 6 unit of credits only)

The data items, the relationships, and constraints are all expressed using the concepts provided by the high-level data model. Because these concepts don’t include the implementation details, the results of the info modeling process are a (semi) formal representation of the database structure. This result’s quite easy to know so it’s used as regards confirming that each one of the user’s requirements is met.

The third step is Database Design. During this step, we’d have two sub-steps called Database Logical Design which define a database during a data model of a selected DBMS, and Database Physical Design which defines the interior database storage structure, file organization, or indexing techniques. The last two steps shown are Database Implementation and Operations/Interfaces Building. These specialize in creating an instance of the schema and implementing operations and user interfaces.

In the database design phases, data is represented employing a certain data model. the info Model may be a collection of conceptual concepts or notations for describing data, data relationships, data semantics, and data constraints. Most data models also include a group of basic operations for manipulating data within the database.

The actual database design is that the process of manufacturing an in-depth data model of a database. This logical data model contains all the needed logical and physical design choices and physical storage parameters needed to get a design during a Data Definition Language, which may then be wont to create a database. a totally attributed data model contains detailed attributes for every entity. The term database design is often wont to describe many various parts of the planning of an overall database system.

Principally, and most correctly, it is often thought of because the logical design of the bottom data structures wont to store the info. within the relational model, these are the tables and views. In an object database, the entities and relationships map on to object classes and named relationships. However, the term database design could even be wont to apply to the general process of designing, not just the bottom data structures, but also the forms and queries used as a part of the general database application within the management System or DBMS

Data Modeling Process

Figure 8.1 illustrates the way data models are developed and used today. A conceptual data model is developed supported the info requirements for the appliance that’s being developed, perhaps within the context of an activity model. the info model will normally contain entity types, attributes, relationships, integrity rules, and therefore the definitions of these objects. this is often then used because of the start point for interface or database design.

Entity-Relationship Model
There are several notations for data modeling. the particular model is usually called the “Entity-relationship model” because it depicts data in terms of the entities and relationships described within the data. The Entity-relationship model (ERM) developed by Chen in 1976-77, is a superb tool within the database design process. It provides a graphic representation of entities, attributes, and relationships. it’s an abstract conceptual representation of structured data.

The E-R model describes the conceptual schema and is taken into account as a blueprint of the database under design. After finalization, an E-R diagram (as the entity-relationship model is usually called) is mapped into one among the chosen database models, and therefore the system-dependent procedure of database creation is started. Entity-relationship modeling may be a relational schema database modeling method, utilized in software engineering to supply a kind of conceptual data model (or semantic data model) of a system, often an electronic database, and its requirements during a top-down fashion.

These models are getting used within the first stage of data system design during the wants analysis to explain information needs or the sort of data that’s to be stored during a database. the info modeling technique is often wont to describe any ontology (i.e. a summary and classifications of used terms and their relationships) for a particular universe of discourse i.e. area of interest.

• it maps well to the relational model. The constructs utilized in the ER model can easily be transformed into relational tables.

• it’s simple and straightforward to know with a minimum of coaching. Therefore, the model is often employed by the database designer to speak the planning to the top user.

• Additionally, the model is often used as a design plan by the database developer to implement a knowledge model during a specific management software.

Basic Constructs of E-R Modeling
The ER model views the important world as a construct of entities and associations between entities.

Special Entity Types
Associative entities (also referred to as intersection entities) are entities wont to associate two or more entities so as to reconcile a many-to-many relationship. Subtypes entities are utilized in generalization hierarchies to represent a subset of instances of their parent entity, called the supertype, but which have attributes or relationships that apply only to the subset.

Steps in Building the info Model
While the ER model lists and defines the constructs required to create a knowledge model, there’s no standard process for doing so. Some methodologies, like IDEFIX, specify a bottom-up development process where the model is made piecemeal. Typically, the entities and relationships are modeled first, followed by key attributes, then the model is finished by adding non-key attributes. Other experts argue that in practice, employing a phased approach is impractical because it requires too many meetings with the end-users. The sequence used for this document is

 Identification of knowledge objects and relationships

 Refining the ER diagram

 Add key attributes to the diagram

Adding non-key attributes

 Diagramming Generalization Hierarchies

 Validating the model through normalization

GLOSSARY
Dependency: A dependency refers to the connection amongst attributes elonging to the relation or different relations.

E-R Diagram: Entity-Relationship Diagram. A diagram that shows associations (relationships) between entities.

Foreign Key: A column in one table that’s the first key during a second table. It doesn’t get to be a key within the first table.

Normalization: the method of making a well-behaved set of tables to efficiently store data, minimize redundancy, and ensure data integrity.

Primary Key: A column or a group of columns that identify a specific row during a table. Relation: A relation maybe a table.

Relationship: An association between two or more entities.

REFERENCES
• Date, C.J. (1989). Introduction to Database Systems. New Delhi: Narosa publisher.

• Ramaz Elmasri, Shaukan B. Navathe (2000). Fundamentals of Database Systems: Pearson Education Asia.

• Jelena Mamčenko (2004). Lecture Notes on INFORMATION RESOURCES Part I Introduction to Data Modeling and MS-Access. Retrieved from http://gama.vtu.lt/biblioteka/Information_Resources/i_part_of_information_res ources.pdf

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