FEEDING BIG DATA FOR ENHANCED SEARCH ENGINE APPLICATIONS

Alagappa Institute of Skill Development & Computer Centre,Alagappa University, Karaikudi, India.15 -16 February 2017. IT Skills Show & International Conference on Advancements In Computing Resources (SSICACR-2017)

Format: Volume 5, Issue 1, No 27, 2017

Copyright: All Rights Reserved ©2017

Year of Publication: 2017

Author: L.Rasikannan,Dr.P.Alli

Reference:IJCS-286

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Abstract

Big Data has its own popularity since it surrounds huge, complicated, growing data sets with many, autonomous sources. They are analyzed computationally to reveal patterns, trends and associations, more specifically relating to human behavior and interactions. Many of the IT investments turn towards Big Data for their better survival. Search Engine plays a predominant role in presenting information to all those who need it, but still struggles to satisfy the drastic changes in user’s requirements. So the search engine should be refined and fine tuned in all scopes, it is required to depend on some other emerging technologies to expedite the necessary issue in Search Engines. Search Engines request the robust services of Big Data for improving its essential functions like web crawling, indexing and summarization of data from the large repositories irrespective of the domain. The repository, in nature, may be dissimilar, independent sometimes and seems to be irrelevant with complex, evolving relationships, and keeps growing. Now a day we are brilliant enough to create quintillion websites for various applications. Maintaining and exploring the huge websites seems to be tedious. The ultimate challenge for Big Data applications is to explore the huge volumes of data and extract suitable information or knowledge for future actions. One of the toughest processes of search engine is to uncover the entire information or knowledge existing in the universe. User expects A to Z from the engine, but storing and extracting all observed data is nearly infeasible.

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Keywords

Autonomous sources, dimension, complex data, data mining.

This work is licensed under a Creative Commons Attribution 3.0 Unported License.   

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