What is “data structures”?
A data structure is a way of organizing data that considers not
only the items stored, but also their relationship to each other.
Advance knowledge about the relationship between data items
allows designing of efficient algorithms for the manipulation of
List out the areas in which data structures are
applied extensively?
• Compiler design
• Operating system
• Database management system
Define a linear and non linear data structure.
• Linear data structure: a linear data structure traverses the data elements
sequentially, in which only one data element can directly be reached. Ex:
arrays, stack, queue
• Non-linear data structure: every data item is attached to several other
data items in a way that is specific for reflecting relationships. The data
items are not arranged in a sequential structure. Ex: trees, graphs
State the difference between arrays and linked
Size of an array is fixed
It is necessary to specify the
number of elements during
Linked Lists
Size of a list varies
It is not necessary to specify the
number of elements during
Insertions and deletions make the Insertions and deletions
elements to change the positions carried out easily
It occupies less memory than a It occupies more memory
linked list for the same number of
Are linked lists considered linear or non-linear
data structure?
• It actually depends on where you intend to apply linked lists. If
you based it on storage, a linked list is considered non-linear.
On the other hand, if you based it on access strategies, then a
linked list is considered linear.
What is the data structure used to perform
• Stack, because of its LIFO (last in first out) property it remembers its 'caller' so
knows whom to return when the function has to return. Recursion makes use of
system stack for storing the return addresses of the function calls.
• Every recursive function has its equivalent iterative (non-recursive) function.
Even when such equivalent iterative procedures are written, explicit stack is to
be used.
Describe stack operation.
• Push – pushes (inserts) the element in the stack. The location is specified
by the pointer.
• Pop – pulls (removes) the element out of the stack. The location is
specified by the pointer
• Peek: - returns the top element on the stack but does not remove it from
the stack
The stack pointer points to the recent element inserted.
What are the different applications of stack?
• Function calls.
• Reversal of a string.
• Checking validity of an expression containing nested parenthesis.
• Conversion of infix expression to postfix.
• Evaluation of postfix expression.
Describe queue operation.
Queue is a data structure that follows first in first out strategy.
• Enqueue – inserts the element in the queue at the end.
• Dequeue – removes the element out of the queue from the front
• Size – returns the size of the queue
• Front – returns the first element of the queue.
• Isempty – to find if the queue is empty.
Discuss how to implement queue using stack.
• Have two stacks S1 and S2
• For enqueue, perform push on S1
• For dequeue, if S2 is not empty, pop top element from S2.
If S2 is empty, then pop all elements currently in S1 and push them
to S2, the last element popped from S1 is the element to be
What are priority queues?
A priority queue is essentially a list of items in which each item has
associated with it a priority. Items are inserted into a priority queue in any,
arbitrary order. However, items are withdrawn from a priority queue in order
Applications of queue
• Serving requests of a single shared resource (printer, disk, cpu), transferring data asynchronously
(data not necessarily received at same rate as sent) between two processes (io buffers), Pipes,
file io, sockets.
• Call center phone systems will use a queue to hold people in line until a service representative is
• Buffers on mp3 players and portable cd players, ipod playlist. Playlist for jukebox - add songs to
the end, play from the front of the list.
• When programming a real-time system that can be interrupted (eg., By a mouse click or wireless
connection), it is necessary to attend to the interrupts immediately, before proceeding with the
current activity. If the interrupts should be handled in the same order they arrive, then a FIFO
queue is the appropriate data structure.
Define a deque
• Deque (double-ended queue) is another form of a queue in which
insertions and deletions are made at both the front and rear ends of the
queue. There are two variations of a deque, namely, input restricted
deque and output restricted deque.
• The input restricted deque allows insertion at one end (it can be either
front or rear) only.
• The output restricted deque allows deletion at one end (it can be either
front or rear) only.
Describe tree data structures
• A recursive data structure, which consists of nodes, connected with edges. The
following statements are true for trees:
Each node can have 0 or more direct descendants (children).
Each node has at most one parent. There is only one special node without
parent – the root (if the tree is not empty).
All nodes are reachable from the root – there is a path from the root to
each node in the tree.
• A node is a tree and this node can have zero or more children, which are also trees.
Describe binary tree
A binary tree consists of
• A node (called the root node) and
• Left and right sub-trees.
Both the sub-trees are themselves binary trees.
Define a binary search tree
A binary search tree is a special binary tree, which is either empty or it should
satisfy the following characteristics:
• Every node has a value and no two nodes should have the same value i.E) the
values in the binary search tree are distinct
• The values in any left sub-tree is less than the value of its parent node
• The values in any right sub-tree is greater than the value of its parent node
• The left and right sub-trees of each node are again binary search trees
What are the different binary tree traversal
• Preorder traversal
• Inorder traversal
• Postorder traversal
What is the order of traversing nodes in in-order
• Traverse the left sub-tree
• Process the root node
• Traverse the right sub-tree
What is the order of traversing nodes in preorder traversal?
• Process the root node
• Traverse the left sub-tree
• Traverse the right sub-tree
What is the order of traversing nodes in postorder traversal?
• Traverse the left sub-tree
• Traverse the right sub-tree
• Process the root node
Give the various traversals of the following tree
•Pre-Order: A-B-D-C-E-F
•In-Order: B-D-A-E-C-F
•Post-Order: D-B-E-F-C-A
What is an AVL tree?
An avl tree is a binary tree in which the difference between the
height of the right and left subtrees (or the root node) is never
more than one.
Binary heap
A binary heap is a complete binary tree which satisfies the heap ordering property. The ordering can be one of two
• The min-heap property: the value of each node is greater than or equal to the value of its parent, with the minimumvalue element at the root.
• The max-heap property: the value of each node is less than or equal to the value of its parent, with the maximumvalue element at the root.
What is a graph?
• A graph G = (V,E) is composed of:
V: set of vertices
E: set of edges connecting the vertices in V
• An edge e = (u,v) is a pair of vertices
• Example:
V= {a,b,c,d,e}
E= {(a,b),(a,c),(a,d),(b,e),(c,d),(c,e), (d,e)}
What are the representations of a graph?
Adjacency matrix
Adjacency lists
How can you represent a graph using adjacency
Let g=(v,e) be a graph with n vertices.
The adjacency matrix of g is a two-dimensional n by n array, say adj_mat
If the edge (vi, vj) is in e(g), adj_mat[i][j]=1
If there is no such edge in e(g), adj_mat[i][j]=0
The adjacency matrix for an undirected graph is symmetric; the adjacency
matrix for a digraph need not be symmetric
Adjacency list representation of a graph
• An array of linked lists is used. Size of the array is equal to number
of vertices. Let the array be array[]. An entry array[i] represents the
linked list of vertices adjacent to the i th vertex. This representation
can also be used to represent a weighted graph. The weights of
edges can be stored in nodes of linked lists.
What are the ways to traverse all the nodes in the
• Depth first search
• Once a possible path is found, continue the search until the end of the
• Breadth first search
• Start several paths at a time, and advance in each one step at a time
Give an example of dfs for graph
Give an example of bfs for graph
Topological sorting
• An ordering of the vertices in a directed acyclic graph, such that: if there
is path from u to v, then u appears before v in the ordering.
One possible sorting: V1, V2, V4, V3, V5
Another sorting: V1, V2, V4, V5, V3
Applications of graph
• Computer networks
• Electronic circuits
What is a spanning tree?
• A spanning tree of a graph g(v,e) is a free tree (i.e., A tree with
no root) with | v | - 1 edges that connects all the vertices of the
What is a minimum spanning tree
A minimum spanning tree for g is a spanning tree of g that has
the least total cost.
Example: the graph
Has 16 spanning trees. Some are:
The graph has two minimum-cost spanning
trees, each with a cost of 6:
Give some applications of minimum-cost
spanning trees
• Building cable networks that join n locations with minimum
• Building a road network that joins n cities with minimum cost.
• Obtaining an independent set of circuit equations for an
electrical network.
What are the algorithms available to find mst
• Prim’s algorithm
• Kruskal’s algorithm
What is the idea of prim’s algorithm to find MST?
• Prim’s algorithm finds a minimum cost spanning tree by selecting
edges from the graph one-by-one as follows:
• It starts with a tree, t, consisting of the starting vertex, x.
• Then, it adds the shortest edge emanating from x that connects t to
the rest of the graph.
• It repeats the process and stops when the tree covers all vertices
What is the idea of kruskal’s algorithm to find
• A forest is a graph whose connected components are trees.
Starting from a spanning forest with no edges, repeatedly add
edges of minimum weight (never creating a cycle) until the
forest becomes a tree.
Sequential search
• A sequential search of a list/array begins at the beginning of the list/array and
continues until the item is found or the entire list/array has been searched
• Sequential search (array implementation) uses n+1 comparisons for an
unsuccessful search (always).
• Time complexity will be in the order of n in average
Binary Search
• Binary search algorithm assumes that the items in the array being searched
are sorted
• The algorithm begins at the middle of the array in a binary search
• If the item for which we are searching is less than the item in the middle, we
know that the item won’t be in the second half of the array
• Once again we examine the “middle” element
• The process continues with each comparison cutting in half the portion of the
array where the item might be
• Time complexity will be in the order of log N in average.
Hash function
• determines position of key in the array.
• assume table (array) size is n
• function f(x) maps any key x to an int between 0 and n−1
• for example, assume that n=15, that key x is a non-negative integer between
0 and max_int, and hash function f(x) = x % 15.
How can collision be handled in hashing?
• Open hashing (closed addressing) - separate chaining
• Closed hashing(open addressing)
Separate chaining: disadvantages
• Parts of the array might never be used.
• As chains get longer, search time increases to o(n) in the worst
• Constructing new chain nodes is relatively expensive (still
constant time, but the constant is high).
Brute force
• A straightforward approach, usually based directly on the
problem’s statement and definitions of the concepts involved
• Examples:
• Computing an (a > 0, n a nonnegative integer)
• Computing n!
• Multiplying two matrices
• Searching for a key of a given value in a list
Divide-and-conquer technique
a problem of size n
subproblem 1
of size n/2
subproblem 2
of size n/2
a solution to
subproblem 1
a solution to
subproblem 2
a solution to
the original problem
sorting: mergesort and
binary tree traversals
Decrease and conquer
1. Reduce problem instance to smaller instance of the same problem
2. Solve smaller instance
3. Extend solution of smaller instance to obtain solution to original instance
• Decrease by a constant: insertion sort
• Decrease by a constant factor: binary search
• Decreaase by variable: euclid’s algorithm gcd(m,n) = gcd(n, m mod n)
Transform and conquer
This group of techniques solves a problem by a transformation.
• Instance simplification:
• Presorting
• Representation change:
• Heap sort
• Problem reduction:
• Mathematical results
Greedy technique
Constructs a solution to an optimization problem piece by piece through a
sequence of choices that are:
Feasible, i.E. Satisfying the constraints
• Locally optimal (with respect to some neighborhood definition)
• Greedy (in terms of some measure), and irrevocable
• Example: minimum spanning tree, single source shortest path, huffman
coding, simple scheduling problems

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