Welcome to the InfoSci Platform
Reference Hub1
Data Analytic Models That Redress the Limitations of MapReduce

Data Analytic Models That Redress the Limitations of MapReduce

Uttama Garg
Copyright: © 2021 |Volume: 16 |Issue: 6 |Pages: 15
ISSN: 1548-1093|EISSN: 1548-1107|EISBN13: 9781799867425|DOI: 10.4018/IJWLTT.20211101.oa7
Cite Article Cite Article

MLA

Garg, Uttama. "Data Analytic Models That Redress the Limitations of MapReduce." IJWLTT vol.16, no.6 2021: pp.1-15. http://doi.org/10.4018/IJWLTT.20211101.oa7

APA

Garg, U. (2021). Data Analytic Models That Redress the Limitations of MapReduce. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 16(6), 1-15. http://doi.org/10.4018/IJWLTT.20211101.oa7

Chicago

Garg, Uttama. "Data Analytic Models That Redress the Limitations of MapReduce," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT) 16, no.6: 1-15. http://doi.org/10.4018/IJWLTT.20211101.oa7

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The amount of data in today’s world is increasing exponentially. Effectively analyzing Big Data is a very complex task. The MapReduce programming model created by Google in 2004 revolutionized the big-data comput-ing market. Nowadays the model is being used by many for scientific and research analysis as well as for commercial purposes. The MapReduce model however is quite a low-level progamming model and has many limitations. Active research is being undertaken to make models that overcome/remove these limitations. In this paper we have studied some popular data analytic models that redress some of the limitations of MapReduce; namely ASTERIX and Pregel (Giraph) We discuss these models briefly and through the discussion highlight how these models are able to overcome MapReduce’s limitations.