Class 11 Accountancy Chapter 14 STRUCTURING DATABASE FOR ACCOUNTING
NCERT Notes for Class 11 Accountancy Chapter 14 STRUCTURING DATABASE FOR ACCOUNTING, (Accountancy) exam are Students are taught thru NCERT books in some of state board and CBSE Schools. As the chapter involves an end, there is an exercise provided to assist students prepare for evaluation. Students need to clear up those exercises very well because the questions with inside the very last asked from those.
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NCERT Notes for Class 11 Accountancy Chapter 14 STRUCTURING DATABASE FOR ACCOUNTING
Class 11 Accountancy Chapter 14 STRUCTURING DATABASE FOR ACCOUNTING
Data processing cycle
- Data processing involves the activities of collecting, storing, relating, interpreting and computing data so as to get meaningful information for decision making.
- Data processing is a sequence of action that is taken to transform the data into decision useful information.
The major components or steps of data processing are
1- Source document
The first step is to collect available data from transactions in order to prepare the respective voucher, which records the accounting data in a systematic manner
2- Input data
The data contained in voucher are to be stored in a computer’s storage device. The data is entered using pre designed data entry form, designed using software and which look like a physical voucher.
suitable database is required to store the records.
4- Data manipulation
The data in the database are manipulated to generate final reports.
5- Output data
The accounting reports such as ledger, trial balances etc. are obtained in a predesigned format by accessing the transformed data.
- data model defines how the logical structure of a database is modified.
- It defines how data is connected to each other and how they are processed and stored inside in the system.
- A data model is a collection of conceptual tools for describing data, data relationship and data constraints.
There are three important class of data model.
1- Conceptual data model
- conceptual data model identifies the highest level relationship between the different entities.
- It includes the main concept and the main relationship among them.
2- Physical data model
- Physical data model represents how the model will be built in the database.
- A physical database model shows all table structure, including column name, column data type, column constraints primary key etc.
3- Logical data model.
A. logical data model describes the data in as much as detail as possible. It includes a Hierarchical model
In this model data and relationship are represented as collection of trees using records and links.
B- Network data model
This model represents data by collection of records and relationship among data by links.
C- Relational Data model
- This model represents data and relationship among data by collection of tables each of which has a number of columns with unique names.
- In a relational data model the table has rows and columns.
- Formally a row is called tuple and column header is called an attribute.
- And the table as such is called a relation.
- The data type describing the type of values that can appear in each column is called a domain.
- A domain is a set of indivisible value.
Designing of database for accounting
- The database is used for storing data.
- Data base is a collection of inter related data organised in a specific manner and a database system is basically a computer based record keeping system,
- The process of designing database for accounting begins with an accounting reality expressed using elements of a conceptual data model.
The elements include
It refers to the real world situation for which database is to be designed. In accounting it is an accounting reality that is to be expressed with complete description.
2- ER design
ER design refers to the blue print or graphical representation of accounting reality tin which ER model concepts are used.
3- Relational data model
Relational data model refers to data model through which ER design is transformed in to interrelated data table.
Normalisation is the process of refining a database design by which the possibilities of duplicate data items are removed
The outcome of normalisation is described as refinement.
Entity Relationship Model / ER model
ER model is a conceptual data model. The model is used to express a reality for which an accounting database is designed. The major elements of ER model are entities, attributes, identifiers and relationship, which are expressed with symbols.
An entity is a thing in the real world. It may be an object with a physical existence or an object with a conceptual existence. eg. Employee, customer etc. entity represented as rectangle shape
Entities having its own attribute as primary key are called strong entities. Entity student has student ID as primary key.
- Entity types which do not have identifier or primary key of their own are called weak entity.
- These entities derive their primary key from the combination of its attributes and primary key from its mapping entity.
- Consider class and section as entity.
- The section has section id , name, class id as its attributes.
- But section id cannot be a primary key.
- Weak entity represented as double lined rectangle shape
An entity type is defined as a collection of entities having common attributes.
The value of attribute of an entity belonging to entity type is known as entity instance.
Entity set is a collection of all entity instances of a particular entity type. An entity set may contain entities with attribute sharing similar values. E.g. a student set
Attributes are some properties of interest that further describe the entity. E.g. Name, ID, Salary etc.
Type of attributes
1- Simple attribute
Attribute that cannot be divided in to sub parts are called simple or atomic attribute. Employee number and age of a person are simple attribute.
Composite attribute can be divided in to smaller sub parts. E.g. Name attribute we can divide it in to first name, middle name and last name.
3- Derived attribute
Derived attributes are the attributes that do not exist in the physical data base, but their values are derived from other attributes present in the database. e.g. The entity students age would be considered a derived attribute., since it could be calculated using the student’s date of birth.
4- Single valued
The single valued attribute can be only have one value.
E.g. in the entity student height and weight are single valued attribute.
5- Multivalued attribute
Multivalued attributes may contain more than one value.
e.g. In the entity student, student address, students qualification are multivalued attributes
6- Null value
Absence of a data item is represented by a special value called null value. The cases where null value used are
- When a particular attribute does not apply to an entity.
- Value of an attribute is unknown, although it exists.
- Unknown because it does not exist.
7. Complex attributes
A complex attribute that is both composite and multivalued
8. Identifier or key attribute
Identifier or key attribute is an attribute that uniquely identifies individual instances of an entity type. E.g. Roll number as attribute of entity type students has unique value through which a student instance can be identified.
The association among entities are called relationship. Relationship links the various components in an ER diagram together.
i. Types of Relationship
If any entity from different entity types is related to one another in a specific manner, they create a relationship type. Relationship type may be one to one, one to many, or many to many.
ii. Degree of relationship
- The number of participating entities in a relationship defines the degree of the relationship.
The most common relationship degrees are
Binary- when two entities are associated to form a relation, it is known as binary relationship.
Ternary- The relationship between three entity types.
iii. Role names
The role name signifies the role that a participating entity from the entity type plays in each relationship instance, and helps to explain what the relationship means.
iv. Structural constraints.
The reality may impose certain constraints or restrictions which may limit the possible combination of entities participating in a given relationship set.
a) Cardinality ratios
Cardinality ratios for binary relationship specify the number of relationship instances that an entity can participate in. The possible cardinality ratios are one to one (1:1), one to many (1: N), many to one (N:1) and many to many (N: M)
Participation constraint specifies the presence of an entity when it is related to another entity in a relationship type. It is also called the minimum cardinality constraint.
There are two types of Participation constraint:
- Total participation
- Partial participation
9. Weak entity type
Entity types which do not have identifier or primary key of their own are called weak entity. These entities derive their primary key from the combination of its attributes and primary key from its mapping entity.
It refers to a set of techniques that are used to design a database. These technique use certain concepts
- Database schema is a set of formulas or rules called integrity constraints imposed on a database.
- It is the logical view of entire database.
- It defines how the data is organised and how the relation among them are associated.
- A database schema defines its entities and the relationship among them.
Constraints of database schema
Database considers are the restrictions on the content of the database or on the database operations. Integrity constraints are used to ensure accuracy and consistency of data in a relational database.
1- Domain constraints
A domain constraint specifies that value of an attribute must confirm to the data type associated with the domain.
2- Key constraints
To specify entities and relationship a super key is assigned to each set. A super key is a set of one or more attribute which collectively allows us to identify uniquely an entity.
E.g. Adm no. Reg. no. etc
The key which identify all record of a table uniquely and cannot be repeated is called primary key. It cannot be null
Those keys not selected as primary key are secondary key.
- key that can act as a primary key is called candidate key.
- It meets all the requirement of a primary key.
When a primary key of one relation is also available as an attribute in another relation that attribute is called foreign key.
3. Entity integrity constraints
The entity integrity constraints state that primary key cannot be null. There must be a proper value in the primary key because it is used to identify individual tuple in a relation.
4. Referential integrity constraints
The referential integrity constraints are specified between two tables and it is used to maintain the consistency among rows between the two tables. E.g.
- Cannot delete a record from a primary table if matching records exist in a related table
- Cannot change primary key value in the primary table if that record has related table
- Cannot enter a value in the foreign key field
- Cannot enter a null value in the foreign key
Operation and constraint violation
There are two categories of operation in relational model. Updating and retrieval. The tree types of updates are
- Insert- to add a new tuple in a table
- Delete to remove a tuple from a relation
- Modify- modify existing values
Designing of relational database schema
A relational data schema is the tables, columns and relationship that make up a relational database.
1- Create a relation for every strong entity
For each strong entity a separate relation that includes all the simple attribute of that entity is created. Choose one of the key attribute of such entity as the primary key for this relation.
2- Create a separate relation for each weak entity type
For every weak entity type a separate relation is created by including its attribute.
3- Identify entity type participating in 1:N relationship
1: N means one to many. If we have two tables and each raw of a table one may be referenced by any number of rows in second table. Second table can only reference one raw in first table
4- Identify entity type participating in binary M: N relationship
M: N means many to many. Each raw in table A can reference many rows in table B and each row in table B can reference many row in table A
Interacting with database
Structured query language is the interacting language between user and the database. The user will be using queries to access the information from the database. SQL is a special purpose programming language designed for managing data held in DBMS.