1.1 introduction to Data Structures.ppt

Introduction to Data Structures

Data and information
• Data means value or set of values . Data items refers to a single unit of values.
• Data is represented in the from of text/numbers/figures/ tables/graphs/pictures etc.
A computer Language is incomplete without data.
• Information is derived from data, such as:
per capita income, Average Population of the states , price index ,etc.
Data
Method to
Process data
I
N
F
O
R
M
A
T
I
O
N

Example of data processed in information
• In a month we everyday get the newspaper then at the end we can calculate the
bill from the data of the cost of newspaper we daily receive.
• Data about some persons in a group:
• Distance between the two stations:
A 100 km B
Information Details Data
Name Satish
Gender Male
Age 32
Phone 987680987

Examples of data into information
• From the score of an individual cricket player we can draw following information:
• Total Runs Scored
• Highest score
• Average
• Strike rate
• No. of Sixes
• No. of fours
• This information helps the planners/analysts/to make decisions.
• Info. In Computer: A computer is an electronic device that manipulates the
information presented in the form of data.
• Data: Data is nothing but a collection of numbers, alphabets and symbols
combined to represent information.
• Entity: An entity possesses some attributes and some values can be allocated to it.
E.g. student entity in a college.

Data Structures
• Data Structure: Organization of data in
computers’ memory so it can be accessed easily
to solve the problem
• Algorithm: Outline, the essence of a
computational procedure, step-by-step
instructions
• Program: an implementation of an algorithm in
some programming language

Classification of Data Structures
• Linear: The values are arranged in a linear fashion. E.g. Arrays, Linked Lists, Stacks,
Queues etc.
• Non-Linear: The values are not arranged in an order. E.g. Tree, Graph, Table etc.
• Homogeneous: Here all the values stored are of same type e.g. arrays
• Non- Homogeneous: Here all the values stored are of different type e.g. structures and
classes.
• Dynamic:A dynamic data structure is one that can grow or shrink as needed to contain
the data you want stored. That is, you can allocate new storage when it's needed and
discard that storage when you're done with it. E.g. pointers, or references
• Static:They're essentially fixed-size and often use much space E.g. Array

Classification of Data Structures
Data Structures
Primitive
Data Structures
Non-Primitive
Data Structures
Linear
Data Structures
Non-Linear
Data Structures
• Integer
• Real
• Character
• Boolean • Array
• Stack
• Queue
• Linked List
• Tree
• Graph

Types of Data Structures
• Arrays:
1. Linear data Structure
2. All elements stored at contiguous memory locations
3. Every element is identified by an index (Logical Address)
4. At the same time every element is identified by a physical address (Physical
Address)
5. We can have 2 D and 3D arrays also but stored in memory in a linear way.

• Stack:
– Only the top item can be accessed
– Can only extract one item at a time
• A stack is a data structure with the property that only the top element of the stack
is accessible
• The stack’s storage policy is Last-In, First-Out
• Only the top element of a stack is visible, therefore the number of operations
performed by a stack are few
• Need the ability to
– Inspect the top element
– Retrieve the top element
– Push a new element on the stack
– Test for an empty stack

• Queue:
• Stores a set of elements in a particular order
• Stack principle: FIRST IN FIRST OUT
• = FIFO
• It means: the first element inserted is the first one to be removed
• Real life examples
– Waiting in line
– Waiting on hold for tech support
• Applications related to Computer Science
– Threads
– Job scheduling (e.g. Round-Robin algorithm for CPU allocation)

Linked Lists
• A linked list is a series of connected nodes
• Each node contains at least
– A piece of data (any type)
– Pointer to the next node in the list
• Head: pointer to the first node
• The last node points to NULL
A 
Head
B C
A
data pointer
node

• Linked List:
1. Linear data structure.
2. Primarily used when the elements are to stored at non-contiguous locations.
1. Linked list is able to grow in size as needed.
2. Does not require the shifting of items during insertions and deletions.

Terminology for a Tree
• Tree: A collection of data whose entries have a hierarchical organization
• Node: An entry in a tree
• Root node: The node at the top
• Terminal or leaf node: A node at the bottom
• Parent: The node immediately above a specified node
• Child: A node immediately below a specified node
• Ancestor: Parent, parent of parent, etc.
• Descendent: Child, child of child, etc.
• Siblings: Nodes sharing a common parent
• Binary tree: A tree in which every node has at most two children
• Depth: The number of nodes in longest path from root to leaf

Graph
• A graph is a pair (V, E), where
– V is a set of nodes, called vertices
– E is a collection of pairs of vertices, called edges
– Vertices and edges are positions and store elements
• Example:
– A vertex represents an airport and stores the three-letter airport code
– An edge represents a flight route between two airports and stores the
mileage of the route
15
Graphs
ORD
PVD
MIA
DFW
SFO
LAX
LGA
HNL

Edge Types
• Directed edge
– ordered pair of vertices (u,v)
– first vertex u is the origin
– second vertex v is the destination
– e.g., a flight
• Undirected edge
– unordered pair of vertices (u,v)
– e.g., a flight route
• Directed graph
– all the edges are directed
– e.g., route network
• Undirected graph
– all the edges are undirected
– e.g., flight network
16
Graphs
ORD PVD
flight
AA 1206
ORD PVD
849
miles

Applications
• Electronic circuits
– Printed circuit board
– Integrated circuit
• Transportation networks
– Highway network
– Flight network
• Computer networks
– Local area network
– Internet
– Web
• Databases
– Entity-relationship diagram
17
Graphs
John
David
Paul
brown.edu
cox.net
cs.brown.edu
att.net
qwest.net
math.brown.edu
cslab1b
cslab1a

Data Structure Operations
Data Structures are processed by using certain operations.
1. Traversing: Accessing each record exactly once so that
certain items in the record may be processed.
2. Searching: Finding the location of the record with a given
key value, or finding the location of all the records that satisfy
one or more conditions.
3. Inserting: Adding a new record to the structure.
4. Deleting: Removing a record from the structure.

Special Data Structure-
Operations
• Sorting: Arranging the records in some logical order
(Alphabetical or numerical order).
• Merging: Combining the records in two different sorted
files into a single sorted file.

Algorithm
• Finite step by step list of well-defined instructions
for solving a particular problem.
• Algorithm is providing a way to access the data
from the data structure in most convenient way.
• Algorithm is programming independent.
• It is written in combination of english language with
little syntax conditions.

Algorithm
• A clearly specified set of instructions to solve a problem.
• Characteristics:
– Input: Zero or more quantities are externally supplied
– Definiteness: Each instruction is clear and unambiguous
– Finiteness: The algorithm terminates in a finite number of steps.
– Effectiveness: Each instruction must be primitive and feasible
– Output: At least one quantity is produced

Algorithmic Notations:
Formal presentation of the Algorithm consists of two parts:
1. A paragraph that tells the purpose of an algorithm. Identifies the variables which occur in the algorithm
and lists the input data.
2. The list of the steps that is to be executed.
Some conventions used in Algorithms:
 Identifying Number (Algorithm 2: )
 Steps, Control and Exit ( All statements are numbered, Go to and Exit)
 Comments (The comments are to be given to understand the meaning)
 Variable Names (capital letters are used for variable names)
 Assignment Statement (Max:=DATA[1])
 Input and output (Read: variable names, Write: Messages and/or variable names)
 Procedure

Basic Notations
Control Structures:
1. Sequential Logic or Sequential Flow
2. Selection Logic or Conditional Flow
• If (condition) , then:
-----
[Endif]
• If (condition) , then:
-----
Else :
-------
[ Endif]

• Multiple If (condition), then : -----
Elseif : -----
Else :
------
[Endif]
3. Iteration Logic (Repetitive Flow)
•
Repeat step ---- for K=I to N:
[Module] [End of Loop]

• While loop
Repeat tep --- while K<= N:
(logic)
[end of while loop]

C++ Program of sum of two numbers
#include<iostream>
Using namespace std;
Main()
{
int a,b,sum;
cout<<“enter a and b”;
cin>> a>>b;
sum=a+b;
cout<<“sum of a and b is “;
cout<<sum;
}
Convert it
into
algorithm

Sum of N numbers
#include<iostream>
Using namespace std;
Main()
{
int sum=0;
For(int i=0;i<n;i++)
{
cin>>i;
sum=sum +i;
cout<<“sum of n numbers is “;
cout<<sum;
}
Convert it into algorithm

Practice Algorithms
• Write an Algorithm (WAA) to find largest of three
numbers.
• WAA to find the sum of first 10 natural numbers
using for loop.
• WAA to find the sum of first 10 natural numbers
using while loop.

1.1 introduction to Data Structures.ppt

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