Performance Analysis of Elastic Search Technique in Identification and Removalof Duplicate Data
Subhani Shaik1,Nallamothu Naga Malleswara Rao2
1Nallamothu Naga Malleswara Rao, Professor, Department of IT, RVR & JC College of Engineering, Chowdavaram, Guntur, A.P,India.
2Subhani Shaik, Research scholar, Department of CSE, Acharya Nagarjuna University, NagarjunaNagar,Guntur, A.P, India
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2401-2405 | Volume-8 Issue-10, August 2019 | Retrieval Number: H6579068819/2019©BEIESP | DOI: 10.35940/ijitee.H6579.0881019
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Elastic search is a way to organize the data and make it easily accessible. It is a server based search on Lucene. It is a highly scalable, distributed and full-text search engine. Elastic search is developed in Java. It is published as open source under the terms of the Apache License. Elastic search is the most popular enterprise search engine. Elastic search includes all advances in speed, security, scalability, and hardware efficiency. Elastic search is a tool for querying written words. It can perform some other smart tasks, but its principal is returning text similar to a given query and statistical analyses of a quantity of text. Elasticsearch is a standalone database server, which is written in Java and using HTTP/JSON protocol,it’s takes data and optimized the data according to language based searches and stores it in a sophisticated format. Elastic search is very convenient, supporting clustering and leader selection out of the box. Whether it’s searching a database of trade products by description, finding similar text in a body of crawled web pages. In this manuscript elastic search capability of copied data identification and its removing techniques performance are analyzed.
Keywords: Elastic search, duplicate data identification, data removing techniques, performance analysis.
Scope of the Article: Big Data Analytics