A data model is a collection of concepts and rules which are to be used by the database designers. It is the conceptual method of structuring data in the database. It provides mechanism to structure data for the entities to be modeled. It also allows entities to update and retrieve data from the database. These are used to ensure the accuracy of data. Data modeling techniques and tools captures and translates the complex system designs which will be easily understood by the users.
Data model is a collection of mathematically well defined concepts that helps the enterprise to consider the state and dynamic properties of data application. It consists of three properties-
(a) Static properties such as objects, relations, attributes
(b) Integrity rules over objects and operation
(c) Dynamic properties such as rules defining new database states.
Roles of data model
The aim of data model is to support the development of information system by providing the definition and format of data. Data models describe structured data for storage in data management systems like relational database for storage in database management system. Data models do not describe the unstructured data like word processing documents, email messages, pictures, audio, video, etc. Data models for different systems are different. The result of this is that complex interfaces are required between system that share data.
Classification of data model perspectives
The data model perspectives are divided into three categories.
(a) Conceptual schema:– Conceptual schema describes the semantics of the domain. This consists of entity classes, significance in the domain and relationship between pairs of entity classes. The use of conceptual schema become a powerful communication tool with business users. This is also called a subject area model or high level data model.
(b) Logical schema:– Logical schema describes the semantics, which are represented by a particular data manipulation technology. Logical schema consists of description of tables and columns, object oriented classes, XML tags.
(c) Physical schema:- Physical schema describes the physical means by which data are stored like partitions, CPUs , tablespace, etc.
Types of data model
Data models can be divided into following types.
(a) Flat model:- a flat model consists of single, two-dimensional array of data element. In this model, all members of a given column are assumed to be similar values whereas all members of a row are assumed to be related to each other.
(b) Hierarchical model:- In hierarchical model, data is organised into a tree like structure. A single upward link describe the nesting, and a sort field to keep the records in a particular order in each same level list.
(c) Network model:- In this model, the data are stored using two constructs, records and sets. Record defines the fields whereas sets defines one to many relationship between records.
(d) Relational model:- Relational model consists of the first order predicate logic. It is used to describe the database as a collection of inter related data and constraints on the possible values.
Advantages of hierarchical model
The advantages of hierarchical model are listed below.
(a) As the database is based on the hierarchical structure, the relationship between various layers is logically simple.
(b) Design of a hierarchical model is simple.
(c) As all data are held in a common database, data sharing becomes practical.
(d) Hierarchical model offers data security that is provided by DBMS.
(e) The DBMS creates environment in which data independence can be maintained.
(f) The hierarchical data model is very efficient when the database contains a large volume of data in one-to-many relationships. When the users require large numbers of transactions using data whose relationships are fixed over time.
Disadvantages of hierarchical model
There are certain disadvantages of hierarchical model. These are as follows.
(a) The hierarchical database is complex to implement.
(b) A hierarchical database lacks flexibility.
(c) If any changes are made to the database structure of the hierarchical database, then it is required to make the necessary changes in all the application programs that access the database. Thus, maintaining the database and the applications can become very difficult.
(d) In a hierarchical database system the benefits of data independence is limited by structural dependence.
(e) Many-to-many relationships are difficult to implement in a hierarchical data model.
(f) Use of hierarchical model requires extensive programming activities, and therefore, it has been called as a system created by programmers for programmers. Modern data processing environment does not accept such concepts.
Advantages of Network data model
The advantages of network data model are as follows:
(a) Network model is simple and easy to design.
(b) The network model facilitates one-to-many and many-to-many relationships
(c) It has superior data access.
(d) Network model provides data integrity and does not allow a member to exist without an owner.
(e) The network data model provides sufficient data independence.
Disadvantages in Network data model
The disadvantages of network data model are as follows:
(a) Network model provides a navigational access mechanism to the data in which the data are accessed one record at a time.
(b) Absence of structural independence.
(c) The network data model is not a design for user-friendly system.
(d) It is highly skill oriented system.
Advantages of Relational data model
There are certain advantages of relational data model.
(a) A relational data model is more simpler than other data models.
(b) It allows the designers to concentrate on the logical view of the database instead of physical data storage.
(c) The relational database model does not depend on the navigational data access system.
(d) The relational data model provides both structural independence and data independence.
(e) The relational database model provides very powerful, flexible, and easy-to-use query facilities.
Disadvantages of Relational data model.
The disadvantages of relational data model are as follows:
(a) The relational data models need more powerful computing hardware and data storage devices to perform RDBMS assigned task.
(b) The easy-to-design feature of relational database results into untrained people generating queries and reports without much understanding cause to bad design.
DBMS vs RDBMS
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