Classification of Data Structure: Primitive Vs Non Primitive, Static Vs Daynamic

Data Structures & Algorithms
Dr. Devyani Jadhav
Course Instructor
Department of AIML
Sanjivani Rural Education Society’s
Sanjivani University, Kopargaon 423601
School of Engineering & Technology
Department of Artificial Intelligence & Machine Learning

Dr. Devyani Jadhav, Dept of AIML, Sanjivani University 2
Data Structure
Data Structure
Types of Data Structure
 Primitive and Non-primitive
 Static and Dynamic
 Linear and Non-linear
 Persistent and ephemeral data structures
Data Structures & Algorithm

Learning Outcome
 Students will be to understand the Data Structure.
 Students will be able to distinguish the Static and Dynamic data
struture, Primitive and Non-primitive
Reference Book

Data Structure
 Data structure is a way of storing and organizing data efficiently such
that the required operations on them can be performed efficiently to
time and memory.
 Types:

Data Structure: Primitive Vs Non-
Primitive
Data Structure
Primitive
Non Primitive
Stack Queue
Linear Data
Structure
Non Linear Data
Structure
Linked List
Array
real character
Boolean
integer

Primitive
Primitive Data Type Non-Primitive Data Type
Predefined types of data not defined by the programming language
supported by the programming
language
Created by the programmer.
integer, character, and string are all
primitive data types
They are sometimes called "reference
variables," or "object references," since they
reference a memory location, which stores the
data
Programmers can use these data types
when creating variables in their
programs

Primitive
Primitive Data Type Non-Primitive Data Type
The basic data types that are
available in most of the
programming languages
The data types that are derived from
primary data types
used to represent single values. used to store group of values.
Example:
Integer, string, Boolean, Floating
Example:
Arrays, Structure, Union
linked list, Stacks, Queue etc

Data Structure: 2. Static and
Dynamic
 Static Data Structure
a. Memory is allotted at compile
time
b. Cannot change size, once
created.
c. Provides more easier access to
elements through Index
d. Element can access directly
e. Not Flexible
f. Example: Array
 Dynamic Data Structure
a. Memory is allotted at Run time
b. size can be updated during run
time
c. Not provide easier access
d. Not access element directly
e. More Flexible
f. Example: Linked List

Dynamic
Array Linked List
Contains a collection of similar type data
elements
considered as non-primitive data structure
contains a collection of unordered linked
elements known as nodes.
Direct Access to the elements through
Index.
Start from the First Node / head and
Traverse the list through until you get to the
element.
Accessing an element in an array is fast, Linked list takes linear time, so it is quite a bit
slower
Operations like insertion and deletion in
arrays consume a lot of time
Performance of insertion and deletion operations
in Linked lists is fast.

Dynamic
Array Linked List
Arrays are of fixed size. Linked lists are dynamic and flexible and can
expand and contract its size.
Memory is assigned during compile time Allocated during execution or runtime.
Elements are stored consecutively in arrays Elements are stored randomly in Linked lists.
The requirement of memory is less due to
actual data being stored within the index in
the array
It need for more memory in Linked Lists due to
storage of additional next and previous
referencing elements.
Memory utilization is inefficient in the
array.
Memory utilization is efficient in the linked list.

Data Structure: 3. Linear and Non-
Linear
Data Structure
Linear Non-Linear
Linked List
Stack
Array
Queue
Tree Graph

Data Structure: 3. Linear and Non-
Linear

Data Structure: Linear
 Linear: the data elements construct a sequence of a linear list.
 Every element have one successor and one predecessor
 adjacently attached to each other and in a specified order.
 Consumes linear memory space:
 the data elements are required to store in a sequential manner in the
memory.
 The traversal of data elements done sequentially
 Insertion or deletion are done sequentially
 The data element are visited sequentially where only a single element
can be directly reached

Data Structure: Linear
 Array: An array is a group of a definite number of homogeneous
elements stored consecutive memory locations.
 Each element is at fixed distance apart.
 Linked List: A group of a definite number of homogeneous elements
not stored consecutively.

Linear Data Structure
Stack: ordered collection of the elements like an array but
 Stack follows LIFO (Last in first out) order
Queue: ordered collection of the elements like an array but
 Queue employ FIFO (First in first out) to insert and delete the elements.
D
C
B
A
E
D
C
B
A
top

Non-Linear Data Structure
 Graph:
 A graph data structure
organizes and stores the data
elements in a hierarchical
relationship
 All elements are connected to
each other
 Tree:
 A tree data structure organizes
and stores the data elements in a
hierarchical relationship
 Parent to child relation ship
a b
c
d e

Non-Linear Data Structure
 It does not arrange the data consecutively rather it is arranged in
sorted order.
 Every element have more than one successor and predecessor
 element exhibiting the hierarchical relationship which involves the
relationship between the child, parent, and grandparent.
 The traversal of data elements and insertion or deletion are not done
sequentially.
 It utilizes the memory efficiently.
 It does not require the memory declaration in advance.

Linear vs Non-Linear Data
Structure
Basis for
comparison
Linear data structure Non-linear data structure
the data is organized in a linear order in
which elements are linked one after the
other
data elements are not stored in a
sequential manner rather the
elements are hierarchically related
Basic Every item is related to its previous and
next item.
very item is attached with many other
items.
Data is arranged in linear sequence. Data is not arranged in sequence.
Traversing of the
data
All data elements can be read in one time
(single run).
Traversing of data elements in one go
is not possible
Ease of
implementation
Easy to Implement Complex

Basis for
comparison
Linear data structure Non-linear data structure
Levels involved Single Level Multilple level
A single level of elements is
incorporated in the linear data
structure
non-linear data structure
involves multiple levels
Examples Array, queue, stack, linked list, Tree and graph.

Data Structure: 4. Persistent vs
Ephemeral
 A persistent data structure:
 always preserves the previous version of itself when it is modified.
 Any operations on such data structure creates two version of data
structure.
 Retain their previous state and modifications can be done by
performing some operations.
 Considered as ‘immutable’ as updates are not in-place.

Summary
• Classification of Data Structure
• Primitive Vs Non-Primitive
• Static Vs Dynamic
• Linear Vs Non-Linear
• Persistent vs Ephemeral

Thank you!!
Happy
Learning!!

Classification of Data Structure: Primitive Vs Non Primitive, Static Vs Daynamic

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