CERTIFICATE

IMPACT FACTOR 2021

Subject Area

  • Life Sciences / Biology
  • Architecture / Building Management
  • Asian Studies
  • Business & Management
  • Chemistry
  • Computer Science
  • Economics & Finance
  • Engineering / Acoustics
  • Environmental Science
  • Agricultural Sciences
  • Pharmaceutical Sciences
  • General Sciences
  • Materials Science
  • Mathematics
  • Medicine
  • Nanotechnology & Nanoscience
  • Nonlinear Science
  • Chaos & Dynamical Systems
  • Physics
  • Social Sciences & Humanities

Why Us? >>

  • Open Access
  • Peer Reviewed
  • Rapid Publication
  • Life time hosting
  • Free promotion service
  • Free indexing service
  • More citations
  • Search engine friendly

Critical analysis of existing Big Data analytics frame works

Author: 
Belesti Melesse Asress and Dr. Tibebe Beshah
Subject Area: 
Physical Sciences and Engineering
Abstract: 

Recent technological advancements have led to a flood of data from various domains over the past few decades. Big Data incorporates large volume of structured, semi-structured, and unstructured data, which is beyond the processing capabilities of traditional databases. In addition to its huge volume, Big Data is commonly unstructured and requires more real-time analysis. On the other hand, the processing and analysis of Big Data plays a central role in decision making, forecasting, business analysis, product development, customer experience, and loyalty. Hence, organizations dealing with Big Data and analytics need to manage the challenges and opportunities related to datasets they have. The IT industry has responded by providing Big Data tools and technologies as well as approaches. However, many of the existing approaches and technologies experience noted limitations. In this paper, attempt has been made to examine the distinctive features of Big Data along the lines of the 3Vs (variety, volume, and velocity) using literature review and provide an understanding of the Big Data processing approaches. Furthermore, Various Big Data analytics frameworks that deal with Big Data analysis workloads were also investigated and analyzed against set of criteria. Finally, analysis and discussions of existing Big Data analytics frameworks along with a way forward approach is presented.

PDF file: 

CALL FOR PAPERS

 

ONLINE PAYPAL PAYMENT

IJMCE RECOMMENDATION

Advantages of IJCR

  • Rapid Publishing
  • Professional publishing practices
  • Indexing in leading database
  • High level of citation
  • High Qualitiy reader base
  • High level author suport

Plagiarism Detection

IJCR is following an instant policy on rejection those received papers with plagiarism rate of more than 20%. So, All of authors and contributors must check their papers before submission to making assurance of following our anti-plagiarism policies.

 

EDITORIAL BOARD

Dr. Swamy KRM
India
Dr. Abdul Hannan A.M.S
Saudi Arabia.
Luai Farhan Zghair
Iraq
Hasan Ali Abed Al-Zu’bi
Jordanian
Fredrick OJIJA
Tanzanian
Firuza M. Tursunkhodjaeva
Uzbekistan
Faraz Ahmed Farooqi
Saudi Arabia
Eric Randy Reyes Politud
Philippines
Elsadig Gasoom FadelAlla Elbashir
Sudan
Eapen, Asha Sarah
United State
Dr.Arun Kumar A
India
Dr. Zafar Iqbal
Pakistan
Dr. SHAHERA S.PATEL
India
Dr. Ruchika Khanna
India
Dr. Recep TAS
Turkey
Dr. Rasha Ali Eldeeb
Egypt
Dr. Pralhad Kanhaiyalal Rahangdale
India
DR. PATRICK D. CERNA
Philippines
Dr. Nicolas Padilla- Raygoza
Mexico
Dr. Mustafa Y. G. Younis
Libiya
Dr. Muhammad shoaib Ahmedani
Saudi Arabia
DR. MUHAMMAD ISMAIL MOHMAND
United State
DR. MAHESH SHIVAJI CHAVAN
India
DR. M. ARUNA
India
Dr. Lim Gee Nee
Malaysia
Dr. Jatinder Pal Singh Chawla
India
DR. IRAM BOKHARI
Pakistan
Dr. FARHAT NAZ RAHMAN
Pakistan
Dr. Devendra kumar Gupta
India
Dr. ASHWANI KUMAR DUBEY
India
Dr. Ali Seidi
Iran
Dr. Achmad Choerudin
Indonesia
Dr Ashok Kumar Verma
India
Thi Mong Diep NGUYEN
France
Dr. Muhammad Akram
Pakistan
Dr. Imran Azad
Oman
Dr. Meenakshi Malik
India
Aseel Hadi Hamzah
Iraq
Anam Bhatti
Malaysia
Md. Amir Hossain
Bangladesh
Ahmet İPEKÇİ
Turkey
Mirzadi Gohari
Iran