INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY


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1 INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK A REVIEW ON THE USAGE OF OLD AND NEW DATA STRUCTURE ARRAYS, LINKED LIST, STACK, QUEUES, TREES, GRAPHS, BIG DATA MISS. SONAL R. KADU, MR ZEESHAN I. KHAN, MISS RAVNEET T. BAGGA Assistant Professor, Dept. of Information Technology, IBSS College of Engineering, Amravati, India. Accepted Date: 05/03/2015; Published Date: 01/05/2015 Abstract: The study of Data Structure is needed for better organization of memory and it helps the programmer to make out the best usage of memory at the time of insertion, deletion and searching operation. The paper helps us to study about various data structure used in programming as well as the new data structure to maintain large amount of Audio and Video Files that we called today as a Big Data. The paper also signify the pros and cons of all data structure as well as its ability, so that one can use the best from it according to its need (project). Keywords: Array, Linked List, Stack, Queues, Tree, Graph, Big Data Corresponding Author: MISS. SONAL R. KADU Access Online On: How to Cite This Article: PAPERQR CODE 1826
2 INTRODUCTION Data Structure is the organization of memory or it gives an idea to a programmer, how to use a memory at the time of developing a project according to its requirement. Data structures are defined and give an enormous technique to use a memory at the time of insertion and retrieval of data. Why Data Structure In day to day life, you always need a place to dump your personal objects in your home so that you can retrieve the same when you find it easily otherwise it will create a problem which will take your large amount of time in finding it, and this process is nothing but a home organizer. Similarly placing the data in the memory is nothing but a memory organizer and it is called as Data Structure which will helps you to put the data so that insertion and retrieval will be done efficiently. III. Traditional Types A. Array Array is, Simple Sequential Homogenous Data Type Physically Logically Linear Index variable is needed to traverse the list. Linear Easy Search Physically Linear Data Storage 1827
3 Fixed Length Data Structure Insertion & Deletion Overhead Same Data Type Storage Used only when consecutive memory available Internal Fragmentation Possible B. Linked List Linked List is, Collection of Nodes Logically Linear Physically may or may not be Linear Heterogeneous Data Type Variable Length Storage Addresses are needed to traverse the list Supports all Data Types in single shed No need of consecutive memory for implementation Variable Length Storage No Insertion & Deletion Overhead No Internal Fragmentation Possible 1828
4 Use of Pointers No direct access to any node (proper traversing is needed ) External Fragmentation Possible Time consumption is high in any algorithm Efficient algorithms like Binary Search etc. are not applicable C. Stack Stack is, Linear First in Last out / Last in First out basis. Stack Top is needed to access the List. Priority based algorithms uses stack as a Data Structure Used for solving Mathematical Equations Best Data Structure for Recursion function. Applications in Theory of Computation, Compilers etc. No direct access to any element (proper traversing is needed through stack top ) Only two operations are possible, Push and POP. 1829
5 D. Queue Queue is, Linear First in First out / Last in Last out basis. Rear & Front is needed for Insertion and Deletion. Priority based algorithms uses Queue as a Data Structure Applications in different algorithms like Depth First Search & Breadth First Search Traversing algorithms etc. No direct access to any element (proper traversing is needed through from front to rear variable) Only two operations are possible, Enqueue (Insert) and Dequeue (Delete). E. Trees Tree is, Non Linear Data Structure 1830
6 It requires nodes to store the elements. Root, Left sub tree and Right sub tree are needed for traversing. No need of consecutive memory for implementation. To maintain Hierarchical relationship in between the data, tree data structure is used. Non Linear Data structure For traversing a tree data strucutre, algorithms like Preorder, Postorder & In order are needed. Complexity rises due to the use of Pointers. Limited connectivity with nodes ( only parents to child i.e sibling) E. Graphs Graph is, Non Linear Data Structure. It requires nodes to store the elements. Collection of nodes with maximum connectivity. No need of consecutive memory for implementation. Maximum connectivity is possible. 1831
7 Best Data structure for the project having large databases Complex Data Structure For traversing graph data structure, algorithms like DFS & BFS are needed. Complicated for small databases. IV. What is Big Data? The data is too big to fit in main memory. We need data structures on the data. Words like index or metadata suggest that there are underlying data structures. Big data requires exceptional technologies to efficiently process large quantities of data within tolerable elapsed times. Due to Big Data, it becomes difficult to organist the data with help of traditional Data Structures. So research is continuing for managing the BIG DATA by using suitable technologies include A/B testing, crowdsourcing, data fusion & integration, algorithms, machine learning, cloud based infrastructure and the Internet, etc. [1] 1832
8 V. BIG DATA Measurement Volume Variety Velocity Variability Veracity Complexity [3] VI. REFERENCES 1. Michael A. Bender, Stony Brook & Tokutek Bradley C. Kuszmaul for Data Structure and Algorithms for Big Databases. 2. Data Structure Wikipedia. 3. Big Data Wikipedia. 1833
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