UNIT I – LINEAR STRUCTURES
PART A
1. Define Data Structures
Data Structures is defined as the way of organizing all data items that consider not only the elements stored but also stores the relationship between the elements.
2. Define primary data structures
Primary data structures are the basic data structures that directly operate upon the machine instructions. All the basic constants (integers, floating-point numbers, character constants, string constants) and pointers are considered as primary data structures.
3. Define static data structures
A data structure formed when the number of data items are known in advance is referred as static data structure or fixed size data structure.
4. List some of the static data structures in C
Some of the static data structures in C are arrays, pointers, structures etc.
5. Define dynamic data structures
A data structure formed when the number of data items are not known in advance is known as dynamic data structure or variable size data structure.
6. List some of the dynamic data structures in C
Some of the dynamic data structures in C are linked lists, stacks, queues, trees etc.
7. Define linear data structures
Linear data structures are data structures having a linear relationship between its adjacent elements. Eg) Linked lists
8. Define non-linear data structures
Non-linear data structures are data structures that don’t have a linear relationship between its adjacent elements but have a hierarchical relationship between the elements. Eg) Trees and Graphs
9. Define Linked Lists
Linked list consists of a series of structures, which are not necessarily adjacent in memory. Each structure contains the element and a pointer to a structure containing its successor. We call this the Pointer. The last cell’s pointer points to NULL.
10. State the different types of linked lists
The different types of linked list include singly linked list, doubly linked list and circular linked list.
11. List the basic operations carried out in a linked list
The basic operations carried out in a linked list include:
1.Creation of a list
2.Insertion of a node
3.Deletion of a node
4.Modification of a node
5.Traversal of the list
12. List out the advantages of using a linked list
• It is not necessary to specify the number of elements in a linked list during its declaration
• Linked list can grow and shrink in size depending upon the insertion and deletion that occurs in the list
• Insertions and deletions at any place in a list can be handled easily and efficiently
• A linked list does not waste any memory space
13. List out the disadvantages of using a linked list
• Searching a particular element in a list is difficult and time consuming
• A linked list will use more storage space than an array to store the same number of elements
14. List out the applications of a linked list
Some of the important applications of linked lists are manipulation of polynomials, sparse matrices, stacks and queues.
15. State the difference between arrays and linked lists
Arrays
Linked Lists
Size of an array is fixed
Size of a list is variable
It is necessary to specify the number of elements during declaration
It is not necessary to specify the number of elements during declaration
Insertions and deletions are somewhat difficult
Insertions and deletions are carried out easily
It occupies less memory than a linked list for the same number of elements
It occupies more memory
16. Define a stack
Stack is an ordered collection of elements in which insertions and deletions are
restricted to one end. The end from which elements are added and/or removed is referred
to as top of the stack. Stacks are also referred as piles, push-down lists and last-in-first-
out (LIFO) lists.
17. List out the basic operations that can be performed on a stack
The basic operations that can be performed on a stack are
• Push operation
• Pop operation
• Peek operation
• Empty check
• Fully occupied check
18. State the different ways of representing expressions
The different ways of representing expressions are
• Infix Notation
• Prefix Notation
• Postfix Notation
19. State the advantages of using infix notations
• It is the mathematical way of representing the expression
• It is easier to see visually which operation is done from first to last
20. State the advantages of using postfix notations
• Need not worry about the rules of precedence
• Need not worry about the rules for right to left associativity
• Need not need parenthesis to override the above rules
21. State the rules to be followed during infix to postfix conversions
• Fully parenthesize the expression starting from left to right. During parenthesizing, the operators having higher precedence are first parenthesized
• Move the operators one by one to their right, such that each operator replaces their corresponding right parenthesis
• The part of the expression, which has been converted into postfix is to be treated as single operand
22. State the rules to be followed during infix to prefix conversions
• Fully parenthesize the expression starting from left to right. During parenthesizing, the operators having higher precedence are first parenthesized
• Move the operators one by one to their left, such that each operator replaces their corresponding left parenthesis
• The part of the expression, which has been converted into prefix is to be treated as single operand
• Once the expression is converted into prefix form, remove all parenthesis
23. State the difference between stacks and linked lists
The difference between stacks and linked lists is that insertions and deletions may occur anywhere in a linked list, but only at the top of the stack
24. Mention the advantages of representing stacks using linked lists than arrays
• It is not necessary to specify the number of elements to be stored in a stack during its declaration, since memory is allocated dynamically at run time when an element is added to the stack
• Insertions and deletions can be handled easily and efficiently
• Linked list representation of stacks can grow and shrink in size without wasting memory space, depending upon the insertion and deletion that occurs in the list
• Multiple stacks can be represented efficiently using a chain for each stack
25. Define a queue
Queue is an ordered collection of elements in which insertions are restricted to one end called the rear end and deletions are restricted to other end called the front end. Queues are also referred as First-In-First-Out (FIFO) Lists.
26. Define a priority queue
Priority queue is a collection of elements, each containing a key referred as the priority for that element. Elements can be inserted in any order (i.e., of alternating priority), but are arranged in order of their priority value in the queue. The elements are deleted from the queue in the order of their priority (i.e., the elements with the highest priority is deleted first). The elements with the same priority are given equal importance and processed accordingly.
27. State the difference between queues and linked lists
The difference between queues and linked lists is that insertions and deletions may occur anywhere in the linked list, but in queues insertions can be made only in the rear end and deletions can be made only in the front end.
28. 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.
29. Why you need a data structure?
A data structure helps you to understand the relationship of one data element with the other and organize it within the memory. Sometimes the organization might be simple and can be very clearly visioned. Eg) List of names of months in a year –Linear Data Structure, List of historical places in the world- Non-Linear Data Structure. A data structure helps you to analyze the data, store it and organize it in a logical and mathematical manner.
30. Difference between Abstract Data Type, Data Type and Data Structure
• An Abstract data type is the specification of the data type which specifies the logical and mathematical model of the data type.
• A data type is the implementation of an abstract data type.
• Data structure refers to the collection of computer variables that are connected in some specific manner.
i.e) Data type has its root in the abstract data type and a data structure comprises a set
of computer variables of same or different data types
31. Define data type and what are the types of data type?
Data type refers to the kinds of data that variables may hold in the programming language. Eg) int, float, char, double – C
The following are the types of data type:
• Built in data type- int, float, char, double which are defined by programming language itself
• User defined data type- Using the set of built in data types user can define their own data type
Eg) typedef struct student
{int roll;
char name;
}S;
S s1 ;
Where S is a tag for user defined data type which defines the structure
student and s1 is a variable of data type S.
32. Define an Abstract Data Type (ADT)
An abstract data type is a set of operations. ADTs are mathematical abstractions; nowhere in an ADT’s definition is there any mention of how the set of operations is implemented. Objects such as lists, sets and graphs, along with their operations can be viewed as abstract data types.
33. What are the advantages of modularity?
• It is much easier to debug small routines than large routines
• It is easier for several people to work on a modular program simultaneously
• A well-written modular program places certain dependencies in only one routine, making changes easier
34. State the difference between primitive and non-primitive data types
Primitive data types are the fundamental data types. Eg) int, float, double, charNon-primitive data types are user defined data types. Eg) Structure, Union and enumerated data types
35. State the difference between persistent and ephemeral data structure
Persistent data structures are the data structures which retain their previous state and modifications can be done by performing certain operations on it. Eg) Stack Ephemeral data structures are the data structures which cannot retain its previous state. Eg) Queues
36. What are the objectives of studying data structures?
• To identify and create useful mathematical entities and operations to determine what classes of problems can be solved using these entities and operations
• To determine the representation of these abstract entities and to implement the abstract operations on these concrete representation
37. What are the types of queues?
• Linear Queues – The queue has two ends, the front end and the rear end. The rear end is where we insert elements and front end is where we delete elements. We can traverse in a linear queue in only one direction ie) from front to rear.
• Circular Queues – Another form of linear queue in which the last position is connected to the first position of the list. The circular queue is similar to linear queue has two ends, the front end and the rear end. The rear end is where we insert elements and front end is where we delete elements. We can traverse in a circular queue in only one direction ie) from front to rear.
• Double-Ended-Queue – Another form of queue in which insertions and deletions are made at both the front and rear ends of the queue.
38. List the applications of stacks
• Towers of Hanoi
• Reversing a string
• Balanced parenthesis
• Recursion using stack
• Evaluation of arithmetic expressions
39. List the applications of queues
• Jobs submitted to printer
• Real life line
• Calls to large companies
• Access to limited resources in Universities
• Accessing files from file server
40. Why we need cursor implementation of linked lists?
Many languages such as BASIC and FORTRAN do not support pointers. If linked lists are required and pointers are not available, then an alternative implementation must be used known as cursor implementation.
PART – B
1. What is a Stack? Explain with example?
• Definition of Stack
• Operations of Stack: PUSH and POP
• Example
2. Write the algorithm for converting infix expression to postfix expression?
• Definition of Expression
• Types of expression
• Algorithm for infix to postfix expression
• Example
3. What is a Queue? Explain its operation with example?
• Definition of Queue
• Operations of Queue: insert and remove
• Example
4. Explain the applications of stack?
• Evaluating arithmetic expression
• Balancing the symbols
• Function calls
5. Write an algorithm for inserting and deleting an element from doubly linked list?
Explain linear linked implementation of Stack and Queue?
• Introduction to Doubly linked list
• Operations: insertion and deletion with algorithm
• Linked list implementation of Stack
• Linked list implementation of Queue
6.What is an Abstract Data type? And explain
· Definition of ADT
· Example : Rational ADT
· Pseudo code of Rational ADT
7.Define Structure. Explain in detail.
· Definition of Structure
· Various forms of declaration of structure
· Implementation of structure
8. What is Union? Explain in detail
· Definition of union
· Example
· Implementation of union
9. Define recursion. Explain with it Fibonacci series
· Definition of recursion
· Fibonacci sequence definition
· Example
10. Explain allocation of storage variable and scope variables.
· Allocation of variables
· Two types of variables automatic and external variables
· Example
UNIT II – TREE STRUCTURES
PART A
1. Define a tree
A tree is a collection of nodes. The collection can be empty; otherwise, a tree consists of a distinguished node r, called the root, and zero or more nonempty (sub) trees T1, T2,…,Tk, each of whose roots are connected by a directed edge from r.
2. Define root
This is the unique node in the tree to which further sub-trees are attached.
Here, A is the root.
Oval: A
3. Define degree of the node
The total number of sub-trees attached to that node is called the degree of the
Oval: AOval: Bnode.
For node A, the degree is 2 and for B and C, the degree is 0.
4. Define leaves
These are the terminal nodes of the tree. The nodes with degree 0 are always the
Oval: BOval: AOval: Cleaves.
Here, B and C are leaf nodes.
5. Define depth and height of a node
For any node ni, the depth of ni is the length of the unique path from the root to ni.
The height of ni is the length of the longest path from ni to a leaf.
6. Define depth and height of a tree
The depth of the tree is the depth of the deepest leaf. The height of the tree is
equal to the height of the root. Always depth of the tree is equal to height of th
7. Define a binary tree
A binary tree is a finite set of nodes which is either empty or consists of a root and
two disjoint binary trees called the left sub-tree and right sub-tree.
8. Define a path in a tree
A path in a tree is a sequence of distinct nodes in which successive nodes are
connected by edges in the tree.
9. Define terminal nodes in a tree
A node that has no children is called a terminal node. It is also referred to as leaf
node.
10. Define non-terminal nodes in a tree
All intermediate nodes that traverse the given tree from its root node to the
terminal nodes are referred as non-terminal nodes.
11. Define a full binary tree
A full binary tree is a tree in which all the leaves are on the same level and every
non-leaf node has exactly two children.
12. Define a complete binary tree
A complete binary tree is a tree in which every non-leaf node has exactly two
children not necessarily to be on the same level.
13. Define a right-skewed binary tree
A right-skewed binary tree is a tree, which has only right child nodes.
14. State the properties of a binary tree
• The maximum number of nodes on level n of a binary tree is 2n-1, where n≥1.
• The maximum number of nodes in a binary tree of height n is 2n-1, where n≥1.
• For any non-empty tree, nl=nd+1 where nl is the number of leaf nodes and nd is the
number of nodes of degree 2.
15. What is meant by binary tree traversal?
Traversing a binary tree means moving through all the nodes in the binary tree,
visiting each node in the tree only once.
16. What are the different binary tree traversal techniques?
1. Preorder traversal
2.Inorder traversal
3.Postorder traversal
4.Levelorder traversal
17. What are the tasks performed while traversing a binary tree?
• Visiting a node
• Traverse the left sub-tree
• Traverse the right sub-tree
18. What are the tasks performed during preorder traversal?
• Process the root node
• Traverse the left sub-tree
• Traverse the right sub-tree
19. What are the tasks performed during inorder traversal?
• Traverse the left sub-tree
• Process the root node
• Traverse the right sub-tree
19. What are the tasks performed during postorder traversal?
• Traverse the left sub-tree
• Traverse the right sub-tree
• Process the root node
20. State the merits of linear representation of binary trees.
• Storage method is easy and can be easily implemented in arrays
• When the location of a parent/child node is known, other one can be determined
easily
• It requires static memory allocation so it is easily implemented in all
programming language
21. State the demerit of linear representation of binary trees.
Insertions and deletions in a node take an excessive amount of processing time
due to data movement up and down the array.
22. State the merit of linked representation of binary trees.
Insertions and deletions in a node involve no data movement except the
rearrangement of pointers, hence less processing time.
23. State the demerits of linked representation of binary trees.
• Given a node structure, it is difficult to determine its parent node
• Memory spaces are wasted for storing null pointers for the nodes, which have one
or no sub-trees
• It requires dynamic memory allocation, which is not possible in some
programming language
24. 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
25. What do you mean by general trees?
General tree is a tree with nodes having any number of children.
26. Define ancestor and descendant
If there is a path from node n1 to n2, then n1 is the ancestor of n2 and n2 is the
descendant of n1.
27. Why it is said that searching a node in a binary search tree is efficient than that of a
simple binary tree?
In binary search tree, the nodes are arranged in such a way that the left node is having less data value than root node value and the right nodes are having larger value than that of root. Because of this while searching any node the value of the target node will be compared with the parent node and accordingly either left sub branch or right sub branch will be searched. So, one has to compare only particular branches. Thus searching becomes efficient.
28. What is the use of threaded binary tree?
In threaded binary tree, the NULL pointers are replaced by some addresses. The left pointer of the node points to its predecessor and the right pointer of the node points to its successor.
29. What is an expression tree?
An expression tree is a tree which is build from infix or prefix or postfix expression. Generally, in such a tree, the leaves are operands and other nodes are operators.
30. Define right-in threaded tree
Right-in threaded binary tree is defined as one in which threads replace NULL
pointers in nodes with empty right sub-trees.
31. Define left-in threaded tree
Left-in threaded binary tree is defined as one in which each NULL pointers is
altered to contain a thread to that node’s inorder predecessor.
PART – B
1. What is a binary search tree? Explain with example?
• Definition of binary search tree
• Operations of binary search tree: Insertion and Deletion
• Example
2. Explain binary tree traversals?
• Definition of tree traversal
• Types of traversals
• Example
3. What is a threaded binary tree? Explain its operation with example?
• Definition of threaded binary tree
• Operations of threaded binary tree: insertion
• Example
4. Explain the expression trees?
• Procedure to construct expression tree
• Example
5. Write the procedure to convert general tree to binary tree.
• Steps
• Example
6. Explain the various operations performed on a stack.
· Definition of stack
· Operations performed on stack
· Example
7. Write an algorithm to convert infix expression to postfix expression.
· Definition of expressions
· Types of expression
· Algorithm to convert to postfix expression
8. Explain how the "switch" statement is used in the programs instead of multiple "if else" statements with suitable example program.
· Definition of switch and if else statements
· Example
9. Explain how the following "infix" expression is evaluated with the help of Stack :
5 * (6 + 2) - 12 / 4
· Explanation
10. Write an algorithm for inserting and deleting element in an doubly linked list. explain linear linked implementation of stack and queue
· Introduction to doubly linked list
· Algorithm for inserting and deleting
· Linked list implementation of stack
· Linked list implementation of queue
UNIT III – BALANCED TREES
PART – A
1. Define AVL Tree.
An empty tree is height balanced. If T is a non-empty binary tree with TL and
TR as its left and right subtrees, then T is height balanced if
i) TL and TR are height balanced and
ii)│hL - hR│≤ 1 Where hL and hR are the heights of TL and TR respectively.
2. What do you mean by balanced trees?
Balanced trees have the structure of binary trees and obey binary search tree properties. Apart from these properties, they have some special constraints, which differ from one data structure to another. However, these constraints are aimed only at reducing the height of the tree, because this factor determines the time complexity.
Eg: AVL trees, Splay trees.
3. What are the categories of AVL rotations?
Let A be the nearest ancestor of the newly inserted nod which has the balancing factor ±2. Then the rotations can be classified into the following four categories:
Left-Left: The newly inserted node is in the left subtree of the left child of A.
Right-Right: The newly inserted node is in the right subtree of the right child of
A.
Left-Right: The newly inserted node is in the right subtree of the left child of A.
Right-Left: The newly inserted node is in the left subtree of the right child of A.
4. What do you mean by balance factor of a node in AVL tree?
The height of left subtree minus height of right subtree is called balance factor of a node in AVL tree.The balance factor may be either 0 or +1 or -1.The height of an empty tree is -1.
5. Define splay tree.
A splay tree is a binary search tree in which restructuring is done using a scheme called splay. The splay is a heuristic method which moves a given vertex v to the root of the splay tree using a sequence of rotations.
6. What is the idea behind splaying?
Splaying reduces the total accessing time if the most frequently accessed node is moved towards the root. It does not require to maintain any information regarding the height or balance factor and hence saves space and simplifies the code to some extent.
7. List the types of rotations available in Splay tree.
Let us assume that the splay is performed at vertex v, whose parent and
grandparent are p and g respectively. Then, the three rotations are named as:
Zig: If p is the root and v is the left child of p, then left-left rotation at p would
suffice. This case always terminates the splay as v reaches the root after this
rotation.
Zig-Zig: If p is not the root, p is the left child and v is also a left child, then a left-
left rotation at g followed by a left-left rotation at p, brings v as an ancestor of g
as well as p.
Zig-Zag: If p is not the root, p is the left child and v is a right child, perform a
left-right rotation at g and bring v as an ancestor of p as well as g.
8. Define Heap.
A heap is defined to be a complete binary tree with the property that the value of each node is atleast as small as the value of its child nodes, if they exist. The root node of the heap has the smallest value in the tree.
9. What is the minimum number of nodes in an AVL tree of height h?
The minimum number of nodes S(h), in an AVL tree of height h is given
by S(h)=S(h-1)+S(h-2)+1. For h=0, S(h)=1.
10. Define B-tree of order M.
A B-tree of order M is a tree that is not binary with the following structural
properties:
• The root is either a leaf or has between 2 and M children.
• All non-leaf nodes (except the root) have between ┌M/2┐ and M children.
• All leaves are at the same depth.
11. What do you mean by 2-3 tree?
A B-tree of order 3 is called 2-3 tree. A B-tree of order 3 is a tree that is not
binary with the following structural properties:
• The root is either a leaf or has between 2 and 3 children.
• All non-leaf nodes (except the root) have between 2 and 3 children.
• All leaves are at the same depth.
12. What do you mean by 2-3-4 tree?
A B-tree of order 4 is called 2-3-4 tree. A B-tree of order 4 is a tree that is not
binary with the following structural properties:
• The root is either a leaf or has between 2 and 4 children.
• All non-leaf nodes (except the root) have between 2 and 4 children.
• All leaves are at the same depth.
13. What are the applications of B-tree?
• Database implementation
• Indexing on non primary key fields
14. What is the need for Priority queue?
In a multiuser environment, the operating system scheduler must decide which of the several processes to run only for a fixed period of time. One algorithm uses queue. Jobs are initially placed at the end of the queue. The scheduler will repeatedly take the first job on the queue, run it until either it finishes or its time limit is up and place it at the end of the queue if it does not finish. This strategy is not appropriate, because very short jobs will soon to take a long time because of the wait involved in the run.
Generally, it is important that short jobs finish as fast as possible, so these jobs should have precedence over jobs that have already been running. Further more, some jobs that are not short are still very important and should have precedence. This particular application seems to require a special kind of queue, known as priority queue. Priority queue is also called as Heap or Binary Heap.
15. What are the properties of binary heap?
i) Structure Property
ii) Heap Order Property
16. What do you mean by structure property in a heap?
A heap is a binary tree that is completely filled with the possible exception at the bottom level, which is filled from left to right. Such a tree is known as a complete binary tree.
17. What do you mean by heap order property?
In a heap, for every node X, the key in the parent of X is smaller than (or
equal to) the key in X, with the exception of the root (which has no parent).
18. What are the applications of priority queues?
· The selection problem
· Event simulation
19. What do you mean by the term “Percolate up”?
To insert an element, we have to create a hole in the next available heap location. Inserting an element in the hole would sometimes violate the heap order property, so we have to slide down the parent into the hole. This strategy is continued until the correct location for the new element is found. This general strategy is known as a percolate up; the new element is percolated up the heap until the correct location is found.
20. What do you mean by the term “Percolate down”?
When the minimum element is removed, a hole is created at the root. Since the heap now becomes one smaller, it follows that the last element X in the heap must move somewhere in the heap. If X can be placed in the hole, then we are done.. This is unlikely, so we slide the smaller of the hole’s children into the hole, thus pushing the hole down one level. We repeat this step until X can be placed in the hole. Thus, our action is to place X in its correct spot along a path from the root containing minimum children. This general strategy is known as percolate down.
PART- B
1. What is a Binary heap? Explain binary heap?
• Definition of Binary heap
• Properties of binary heap
• Example
2. Explain Splay tree in detail
• Definition
• Creation
• Types of rotation
3. Explain B-tree representation?
• Node representation of B-tree
• Implicit array representation of B-tree
• Implementation of various operations
4. What is a Priority Queue? What are its types? Explain.
• Definition of Priority queue
• Types: Ascending and Descending priority queue
• Implementation of priority queue
5. Explain AVL tree in detail
· Definition
· Creation
· Types of rotation
· Deletion
6. What is a binary tree? Explain binary tree traversal in “c”
· Definition of binary tree
· Traversals
· Inorder traversal
· Preorder traversal
· Postorder traversal
7. Construct a binary tree to satisfy the following orders:
· Inorder : D B F E A G C L J H K
· Postorder : D F E B G L J K H C A
8. Explain representing lists as binary trees. Write an algorithm to find kth element and deleting it.
· Representing list as binary tree
· Finding kth element
· Deleting an element
9. Explain threaded binary tree with examples
· Define threaded binary tree
· Types Right-in threaded binary tree Left-in threaded tree.
10. Explain binary tree representation
·
UNIT IV – HASHING AND SET
PART – A
1. Define Hashing.
Hashing is the transformation of string of characters into a usually shorter fixed length value or key that represents the original string. Hashing is used to index and retrieve items in a database because it is faster to find the item using the short hashed key than to find it using the original value.
2. What do you mean by hash table?
The hash table data structure is merely an array of some fixed size, containing the keys. A key is a string with an associated value. Each key is mapped into some number in the range 0 to tablesize-1 and placed in the appropriate cell.
3. What do you mean by hash function?
A hash function is a key to address transformation which acts upon a given key to compute the relative position of the key in an array. The choice of hash function should be simple and it must distribute the data evenly. A simple hash function is hash_key=key mod tablesize.
4. Write the importance of hashing.
• Maps key with the corresponding value using hash function.
• Hash tables support the efficient addition of new entries and the time spent on searching for the required data is independent of the number of items stored.
5. What do you mean by collision in hashing?
When an element is inserted, it hashes to the same value as an already
inserted element, and then it produces collision.
6. What are the collision resolution methods?
• Separate chaining or External hashing
• Open addressing or Closed hashing
7. What do you mean by separate chaining?
Separate chaining is a collision resolution technique to keep the list of all elements that hash to the same value. This is called separate chaining because each hash table element is a separate chain (linked list). Each linked list contains all the elements whose keys hash to the same index.
8. Write the advantage of separate chaining.
• More number of elements can be inserted as it uses linked lists.
9. Write the disadvantages of separate chaining.
• The elements are evenly distributed. Some elements may have more
elements and some may not have anything.
• It requires pointers. This leads to slow the algorithm down a bit because of
the time required to allocate new cells, and also essentially requires the
implementation of a second data structure.
10. What do you mean by open addressing?
Open addressing is a collision resolving strategy in which, if collision occurs alternative cells are tried until an empty cell is found. The cells h0(x), h1(x), h2(x),…. are tried in succession, where hi(x)=(Hash(x)+F(i))mod Tablesize with F(0)=0. The function F is the collision resolution strategy.
11. What are the types of collision resolution strategies in open addressing?
• Linear probing
• Quadratic probing
• Double hashing
12. What do you mean by Probing?
Probing is the process of getting next available hash table array cell.
13. What do you mean by linear probing?
Linear probing is an open addressing collision resolution strategy in which F is a linear function of i, F(i)=i. This amounts to trying sequentially in search of an empty cell. If the table is big enough, a free cell can always be found, but the time to do so can get quite large.
14. What do you mean by primary clustering?
In linear probing collision resolution strategy, even if the table is relatively
empty, blocks of occupied cells start forming. This effect is known as primary
clustering means that any key hashes into the cluster will require several attempts
to resolve the collision and then it will add to the cluster.
15. What do you mean by quadratic probing?
Quadratic probing is an open addressing collision resolution strategy in which F(i)=i2. There is no guarantee of finding an empty cell once the table gets half full if the table size is not prime. This is because at most half of the table can be used as alternative locations to resolve collisions.
16. What do you mean by secondary clustering?
Although quadratic probing eliminates primary clustering, elements that hash to the same position will probe the same alternative cells. This is known as secondary clustering.
17. What do you mean by double hashing?
Double hashing is an open addressing collision resolution strategy in which F(i)=i.hash2(X). This formula says that we apply a second hash function to X and probe at a distance hash2(X), 2hash2(X),….,and so on. A function such as hash2(X)=R-(XmodR), with R a prime smaller than Tablesize.
18. What do you mean by rehashing?
If the table gets too full, the running time for the operations will start taking too long and inserts might fail for open addressing with quadratic resolution. A solution to this is to build another table that is about twice as big with the associated new hash function and scan down the entire original hash table, computing the new hash value for each element and inserting it in the new table. This entire operation is called rehashing.
19. What is the need for extendible hashing?
If either open addressing hashing or separate chaining hashing is used, the major problem is that collisions could cause several blocks to be examined during a Find, even for a well-distributed hash table. Extendible hashing allows a find to be performed in two disk accesses. Insertions also require few disk accesses.
20. List the limitations of linear probing.
• Time taken for finding the next available cell is large.
• In linear probing, we come across a problem known as clustering.
21. Mention one advantage and disadvantage of using quadratic probing.
Advantage: The problem of primary clustering is eliminated.
Disadvantage: There is no guarantee of finding an unoccupied cell once the table
is nearly half full.
22. Define a Relation.
A relation R is defined on a set S if for every pair of elements (a,b), a,b ε S,
aRb is either true or false. If aRb is true, then we say that a is related to b.
23. Define an equivalence relation.
An equivalence relation is a relation R that satisfies three properties:
1. (Reflexive) aRa, for all a ε S.
2. (Symmetric) aRb if and only if bRa.
3. (Transitive) aRb and bRc implies that aRc.
24. List the applications of set ADT.
• Maintaining a set of connected components of a graph
• Maintain list of duplicate copies of web pages
• Constructing a minimum spanning tree for a graph
25. What do you mean by disjoint set ADT?
A collection of non-empty disjoint sets S=S1,S2,….,Sk i.e) each Si is a non- empty set that has no element in common with any other Sj. In mathematical notation this is: Si∩Sj=Ф. Each set is identified by a unique element called its representative.
26. Define a set.
A set S is an unordered collection of elements from a universe. An element cannot appear more than once in S. The cardinality of S is the number of elements in S. An empty set is a set whose cardinality is zero. A singleton set is a set whose cardinality is one.
27. List the abstract operations in the set.
Let S and T be sets and e be an element.
• SINGLETON(e) returns {e}
• UNION(S,T) returns S Ụ T
• INTERSECTION(S,T) returns S ∩ T
• FIND returns the name of the set containing a given element
28. What do you mean by union-by-weight?
Keep track of the weight ie)size of each tree and always append the smaller tree to
the larger one when performing UNION.
29. What is the need for path compression?
Path compression is performed during a Find operation. Suppose if we want to perform Find(X), then the effect of path compression is that every node on the path from X to the root has its parent changed to the root.
PART – B
1. Explain hashing with example.
• Hashing techniques
• Example
2. Explain collision resolution strategies?
• Separate chaining
• Open addressing
• Examples
3. Explain extendible hashing?
• Need for extendible hashing
• Procedure
• Example
4. Explain smart union algorithm?
• Smart union algorithm
• Example
5. Explain path compression?
• Definition
• algorithm and example
UNIT V –GRAPHS
PART – A
1. Define Graph.
A graph G consist of a nonempty set V which is a set of nodes of the graph, a set E which is the set of edges of the graph, and a mapping from the set for edge E to a set of pairs of elements of V. It can also be represented as G=(V, E).
2. Define adjacent nodes.
Any two nodes which are connected by an edge in a graph are called adjacent nodes. For example, if an edge x ε E is associated with a pair of nodes (u,v) where u, v ε V, then we say that the edge x connects the nodes u and v.
3. What is a directed graph?
A graph in which every edge is directed is called a directed graph.
4. What is a undirected graph?
A graph in which every edge is undirected is called a directed graph.
5. What is a loop?
An edge of a graph which connects to itself is called a loop or sling.
6. What is a simple graph?
A simple graph is a graph, which has not more than one edge between a pair of
nodes than such a graph is called a simple graph.
7. What is a weighted graph?
A graph in which weights are assigned to every edge is called a weighted graph.
8. Define outdegree of a graph?
In a directed graph, for any node v, the number of edges which have v as their
initial node is called the out degree of the node v.
9. Define indegree of a graph?
In a directed graph, for any node v, the number of edges which have v as their
terminal node is called the indegree of the node v.
10. Define path in a graph?
The path in a graph is the route taken to reach terminal node from a starting node.
11. What is a simple path?
A path in a diagram in which the edges are distinct is called a simple path. It is
also called as edge simple.
12. What is a cycle or a circuit?
A path which originates and ends in the same node is called a cycle or circuit.
13. What is an acyclic graph?
A simple diagram which does not have any cycles is called an acyclic graph.
14. What is meant by strongly connected in a graph?
An undirected graph is connected, if there is a path from every vertex to every
other vertex. A directed graph with this property is called strongly connected.
15. When is a graph said to be weakly connected?
When a directed graph is not strongly connected but the underlying graph is
connected, then the graph is said to be weakly connected.
16. Name the different ways of representing a graph?
a. Adjacency matrix
b. Adjacency list
17. What is an undirected acyclic graph?
When every edge in an acyclic graph is undirected, it is called an undirected
acyclic graph. It is also called as undirected forest.
18. What are the two traversal strategies used in traversing a graph?
a. Breadth first search
b. Depth first search
19. What is a minimum spanning tree?
A minimum spanning tree of an undirected graph G is a tree formed from graph
edges that connects all the vertices of G at the lowest total cost.
20. Name two algorithms two find minimum spanning tree
Kruskal’s algorithm
Prim’s algorithm
21. Define graph traversals.
Traversing a graph is an efficient way to visit each vertex and edge exactly once.
22. List the two important key points of depth first search.
i) If path exists from one node to another node, walk across the edge – exploring
the edge.
ii) If path does not exist from one specific node to any other node, return to the
previous node where we have been before – backtracking.
23. What do you mean by breadth first search (BFS)?
BFS performs simultaneous explorations starting from a common point and
spreading out independently.
24. Differentiate BFS and DFS.
No.
DFS
BFS
1
Backtracking is possible from a
dead end
Backtracking is not possible
2
Vertices from which exploration is
incomplete are processed in a
LIFO order
The vertices to be explored are organized as a
FIFO queue
3
Search is done in one particular
direction
The vertices in the same level are maintained
parallely
25. What do you mean by tree edge?
If w is undiscovered at the time vw is explored, then vw is called a tree edge and
v becomes the parent of w.
26. What do you mean by back edge?
If w is the ancestor of v, then vw is called a back edge.
27. Define biconnectivity.
A connected graph G is said to be biconnected, if it remains connected after removal of any one vertex and the edges that are incident upon that vertex. A connected graph is biconnected, if it has no articulation points.
28. What do you mean by articulation point?
If a graph is not biconnected, the vertices whose removal would disconnect the
graph are known as articulation points.
29. What do you mean by shortest path?
A path having minimum weight between two vertices is known as shortest path,
in which weight is always a positive number.
PART – B
1. Explain shortest path algorithm with example.
· Shortest path algorithm
· Example
2. Explain depth first and breadth first traversal?
· Depth first traversal
· Breadth first traversal
· Examples
3. Explain spanning and minimum spanning tree?
· Spanning tree
· Minimum spanning tree
4. Explain kruskal’s and prim’s algorithm?
· Kruskal’s algorithm
· Prim’s algorithm
5. Explain topological sorting?
· Definition
· algorithm and example
· implementation
No comments:
Post a Comment