2. Data Dictionary
• A data dictionary defines each term encountered during the analysis and
design of a new system. Data dictionary is the place where we keep the
details of the contents of data flows, data stores & processes.
• Data dictionary is an analysis tool, that primarily records the information
content of data.
• Without a data dictionary the development of large systems becomes
difficult. The data dictionary is an effective solution to the problem of
complicated nature. The main purpose of a data dictionary is to provide a
source of reference in which the analyst , the user, the designer can look up
& find out it’s content and any other relevant information.
• Examples of Data dictionary –
• Student Record = Enrolment Number +
• Name +
• Address +
• Sex +
• Date of Birth +
• Subject +
3. Levels of Data Dictionary
We can define the data dictionary in three
different levels.
• Data Elements
• Data Structure
• Data flows and Data Stores
4. Data Elements
• Data elements are pieces of data, which
need not be broken further. Data elements
can describe files, data flows, or
processes. Often a data element is self
defining such as Student name, enrolment
no.
5. Data structure
• Data structure comprises of data elements. It is defined as collection of data elements. For example let us consider the following "Student Information Record".
• Student Information
• Enrolment Number
• Student name
• First name
• Second name
• Last name
• Sex
• Student Address
• Address 1
• Address 2
• Address 3
• Pin code
• Subject Details
• Subject1
• Subject2
• Subject3
• Subject4
• Subject5
• Subject6
• Background details
• Family background
• Father’s education
• Mother’s education
• Family Income
• Urban/rural
• House hold items
• TV (Y?N)
• Radio (Y?N)
• Cycle(Y?N)
• Here "STUDENT INFORMATION" is a data structure, made up of data elements student name, enrolment number. "Subject details" is a data structure made up of six
data elements subject1 to subject6.
•
6. Data flows & Data stores
• Data flows are paths along which data
travels & Data stores are places where
data is stored until needed. So we can say
that Data flows are Data structures in
motion & Data
8. Normalization
• Normalization is a process by which the
contents of the data store is simplified by
removing redundant data elements. It is
done by removing repeating groups and
reorganizing the contents of data store.
9. • Let us consider the following data structure:
• Name
• Personnel-No.
• Address
• Salary-Slip-History*
• Date-of-change
• Job-title
• Salary
10. There are three types of normalized relations, called in increasing
order of simplicity; first normal form, second normal form, and
third normal form
• First Normal Form :
– Any normalized relation is automatically 1st
normal form. Relations in first normal form
may suffer from two kinds of complexity.
– If the primary key is concatenated (chained
together), some of the non-key domains
may depend on only part of the key, not the
whole key.
– Some of the non-key domains may be
interrelated.
11. • We can create a normalized relation:
• Book-order (Customer-Name, Order-Date,
ISBN, Author, Title, Quantity, Price, Order-
Total)
12. Second Normal Form
• normalized relation is in 2nd normal form if all
the non-key domains are fully functionally
dependent on the primary key "Book Order". To
get "Book Order" into 2nd formal form, we must
get rid of the partial functional dependence. This
can be done by taking out the domains which
describe the book and putting them in a
separate relation:-
• Order (Customer-Name, Order-Date, ISBN,
Quantity Order-Total)
• Book (ISBN), Title, Author, Price)
13. Third Normal Form
• A normalized relation is in 3rd normal form
if :
• All the non-key domains are "fully" functionally
dependent on the primary key.
• No non-key domain is functionally dependent on
any other non-key domains.
16. • Moreover, it is very difficult to arrange and
organize the large amount of data into
meaningful interpretation of the whole.
• System Analysis and Design makes use of
the various tools for representing and
facilitating comprehension of the complex
processes and procedure involved.
• We present some details about
– Flowcharts,
– data flow diagram (DFD),
– Decision Tables and
– Decision Trees.
17. OBJECTIVES
• Draw flowchart
• Represent any physical system through
DFD
• Prepare decision table
• Display decision tree
18. FLOWCHARTS
• The pictorial representation of the programs
or the algorithm is known as flowcharts. It is
nothing but a diagrammatic representation
of the various steps involved in designing a
system. Some of the boxes which are used
in flowcharts are:
19.
20. Flowcharts are of three types:
• System flowcharts
• Run flowcharts
• Program flowcharts
21. System Flowcharts
• System flowchart describes the data flow for a data
processing system. It provides a logical diagram of how
the system operates.
• It represents the flow of documents, the operations
performed in data processing system. It also reflects the
relationship between inputs, processing and outputs.
Following are the features of system flowcharts:
– the sources from which data is generated and device used for
this purpose
– various processing steps involved
– the intermediate and final output prepared and the devices used
for their storage
