OPTILYZER: Hawk-Eye Technology for Data Integration & Performance Optimization

Data Comparison and Performance Optimization are key challenges we face when we talk about humongous volume of data. OPTILYZER, a user-friendly interface is proposed to overcome these challenges. The 4V's which are ruling in market due to which Big Data has come into existence that are Volume,...

Full description

Saved in:
Bibliographic Details
Published in:2020 IEEE International Conference for Innovation in Technology (INOCON) pp. 1 - 5
Main Authors: Rathod, Pawan Manoj, Kushwaha, Vartika, Sengar, Rashi Singh, Alexander, Rhea
Format: Conference Proceeding
Language:English
Published: IEEE 06.11.2020
Subjects:
Amazon Web Service (AWS)
Business
Data integration
Distributed databases
Hadoop Distributed File System (HDFS)
Java Database Connectivity (JDBC)
OPTILYZER
Optimization
Query processing
XML
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Data Comparison and Performance Optimization are key challenges we face when we talk about humongous volume of data. OPTILYZER, a user-friendly interface is proposed to overcome these challenges. The 4V's which are ruling in market due to which Big Data has come into existence that are Volume, Velocity, Variety and Veracity. Data Analysts use data for activities like forecasting or deep learning and in order to process these data various tools are available which helps to achieve this task with minimum efforts, but problem arises with legacy data. Legacy data piles up the space and hinders the performance of the current process. This system provides an efficient way to archive these data so as to reduce the space and time complexity of the current process also helps to resolve the challenges faced in data comparison between various heterogeneous platforms like Hive, MySQL and Redshift which have different structure and syntax for processing the data.
AbstractList Data Comparison and Performance Optimization are key challenges we face when we talk about humongous volume of data. OPTILYZER, a user-friendly interface is proposed to overcome these challenges. The 4V's which are ruling in market due to which Big Data has come into existence that are Volume, Velocity, Variety and Veracity. Data Analysts use data for activities like forecasting or deep learning and in order to process these data various tools are available which helps to achieve this task with minimum efforts, but problem arises with legacy data. Legacy data piles up the space and hinders the performance of the current process. This system provides an efficient way to archive these data so as to reduce the space and time complexity of the current process also helps to resolve the challenges faced in data comparison between various heterogeneous platforms like Hive, MySQL and Redshift which have different structure and syntax for processing the data.
Author Rathod, Pawan Manoj
Sengar, Rashi Singh
Kushwaha, Vartika
Alexander, Rhea
Author_xml – sequence: 1
  givenname: Pawan Manoj
  surname: Rathod
  fullname: Rathod, Pawan Manoj
  email: var95kush@gmail.com
  organization: St. Francis Institue of Technology,Computers Department,Mumbai,India
– sequence: 2
  givenname: Vartika
  surname: Kushwaha
  fullname: Kushwaha, Vartika
  email: pawanrathod519@gmail.com
  organization: Data Analyst, Technoboltz,Mumbai,India
– sequence: 3
  givenname: Rashi Singh
  surname: Sengar
  fullname: Sengar, Rashi Singh
  email: rashisingh41@gmail.com
  organization: Data Analyst, Technoboltz,Mumbai,India
– sequence: 4
  givenname: Rhea
  surname: Alexander
  fullname: Alexander, Rhea
  email: rhealexx1993@gmail.com
  organization: Data Analyst, Technoboltz,Mumbai,India
BookMark eNotj81Og0AURsdEF9r2CdzMyh04P0xnxp1BtCQEmgYXumkucKkTy9AgicGnl2hXZ3GSL-e7IZe-90gI5SzknNn7NC_iIldMSRsKJlhohTVSqguystpwLQy3OpLimuTFtkyzt_dk90A38P0ZJBPSEusP3x_7w0TbfqBPMAJN_YiHAUbXe3pHtzjMpgNfIy1Oo-vcz59akqsWjl-4OnNBXp-TMt4EWfGSxo9Z4Dg3Y9ACq0RlamUlzpzTGs51I9e6iaqorqSMImaMbtcajAa0skFgFhWrrDK1kQty-7_rEHF_GlwHw7Q_v5S_YbtMIQ
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/INOCON50539.2020.9298335
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Business
EISBN 9781728197432
9781728197449
1728197449
1728197430
EndPage 5
ExternalDocumentID 9298335
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i118t-fa0b2b8c593e2b8833d117d367d4b4cb33440887f67a87ae93dea09e50b958c83
IEDL.DBID RIE
IngestDate Thu Jun 29 18:38:13 EDT 2023
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i118t-fa0b2b8c593e2b8833d117d367d4b4cb33440887f67a87ae93dea09e50b958c83
PageCount 5
ParticipantIDs ieee_primary_9298335
PublicationCentury 2000
PublicationDate 2020-Nov.-6
PublicationDateYYYYMMDD 2020-11-06
PublicationDate_xml – month: 11
  year: 2020
  text: 2020-Nov.-6
  day: 06
PublicationDecade 2020
PublicationTitle 2020 IEEE International Conference for Innovation in Technology (INOCON)
PublicationTitleAbbrev INOCON
PublicationYear 2020
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.7393403
Snippet Data Comparison and Performance Optimization are key challenges we face when we talk about humongous volume of data. OPTILYZER, a user-friendly interface is...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Amazon Web Service (AWS)
Business
Data integration
Distributed databases
Hadoop Distributed File System (HDFS)
Java Database Connectivity (JDBC)
OPTILYZER
Optimization
Query processing
XML
Title OPTILYZER: Hawk-Eye Technology for Data Integration & Performance Optimization
URI https://ieeexplore.ieee.org/document/9298335
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFH9sQ8STH5v4TQ7iyWxt0-bD69xYYbRFpkwvI2lSGOIms1P87027bkPw4iUJISThBd5L8n6_9wCuHUO4zzMXZ25GcOE6wzxVARbaYynlGeElNudpyKKIj8ciqcHthgtjjCnBZ6ZdNEtfvp6ny-KrrGNNecERqkOdMbriaq3BOY7ohFHcjSNr0UsCiue0q-G_8qaUZqO__78FD6C15d-hZGNZDqFmZkewu8aoNyGKk1E4fH7pPdyhgfx6xb1vg7af5MheRNG9zCUKq2AQVvjoBiVbkgCKrap4qziYLXjs90bdAa4SI-CpfQ_kOJOO8hRPA0GMre0WtesyTSjTvvJTRUiRR5qzjDLJmTSCaCMdYQJHiYCnnBxDYzafmRNAlGrFpWNLQ33Xt5qb2okl9TIh3UDzU2gWYpm8r2JfTCqJnP3dfQ57heRLrh69gEa-WJpL2Ek_8-nH4qo8sB_-opeC
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED_mFPXJj038Ng_ik9napk0TX3VjxdoWmTJ9GWmTwhA3mZ3if29Suw3BF1-SEMgHd3CX3N3vDuDcUoS5LLdxbucEG9cZZlnqYS4dP6MsJ6yMzXkM_ShigwFPanC5wMIopcrgM9Uyw9KXLyfZzJjK2lqVG4zQCqyaylkVWmsenmPxdhDF13GkdXoJQXGsVrXgV-WUUnF0t_535DY0lwg8lCx0yw7U1HgX1udR6g2I4qQfhE_Pnfsr1BOfL7jzpdDSTI70UxTdiEKgoEoHocmPLlCyhAmgWAuL1wqF2YSHbqd_3cNVaQQ80j-CAufCSp2UZR4nSvf6itK2fUmoL93UzVJCTCVp5ufUF8wXihOphMWVZ6XcYxkje1AfT8ZqHxClMmXC0q2iru1q2U31xoI6ORe2J9kBNAxZhm8_2S-GFUUO_54-g41e_y4chkF0ewSbhgslco8eQ72YztQJrGUfxeh9eloy7xsTjZrL
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2020+IEEE+International+Conference+for+Innovation+in+Technology+%28INOCON%29&rft.atitle=OPTILYZER%3A+Hawk-Eye+Technology+for+Data+Integration+%26+Performance+Optimization&rft.au=Rathod%2C+Pawan+Manoj&rft.au=Kushwaha%2C+Vartika&rft.au=Sengar%2C+Rashi+Singh&rft.au=Alexander%2C+Rhea&rft.date=2020-11-06&rft.pub=IEEE&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FINOCON50539.2020.9298335&rft.externalDocID=9298335