Performance Analysis in Bigdata

Kaur, Pankaj Deep; Kaur, Amneet; Kaur, Sandeep

doi:10.5815/ijitcs.2015.11.07

Cited by 8 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Essentially, Big Data is not only a large volume of data, but there are also other features which differentiate it from the of massive data and very large data. There are several explanations of Big Data, but three types of definitions play a significant role in forming how Big Data is viewed: attributive definition (in there includes IDC, IBM, Gartner, Microsoft researcher's definition), comparative definition (in there includes McKinsey definition) and architectural definition (in there includes The National Institute of Standards and Technology (NIST) definition) [5,6].…”

Section: What Is Big Data?mentioning

confidence: 99%

About Big Data Measurement Methodologies and Indicators

Hajirahimova¹,

Aliyeva²

2017

IJMECS

View full text Add to dashboard Cite

Abstract-The digitization of nearly all media and the increasing migration of social and economic activities to the İnternet, the development of social networking technologies, the İnternet of Things and cloud computing caused rapid increase in the volume of data and the formation of Big Data paradigm. Big Data involves technologies and tools for collecting, processing, analyzing and extracting useful knowledge from structured and unstructured data of large volumes generated at high speed by different sources. Increasing the volume, speed, diversity and value of Big Data began to play an important role in the creation of social relationships, competitive advantage and innovative fields. The development of the information society, the formation of digital economy, and the application of Big Data technologies in different spheres of human activity required the quantitative and qualitative assessment of Big Data. In this article some approaches relate to the definition of Big Data have been reviewed. Methodological approaches and indicators for measuring Big Data have been researched. At the end, the indicators have been proposed for the measurement of factors that affected the growth and development of Big Data.

show abstract

Section: What Is Big Data?mentioning

confidence: 99%

About Big Data Measurement Methodologies and Indicators

Hajirahimova¹,

Aliyeva²

2017

IJMECS

View full text Add to dashboard Cite

show abstract

“…In order to support timely and effect ive decision making, Big Data analytics relies on large volu mes of data that requires clusters for data storage. However, since relational databases are not designed for clusters, and exhibit performance issues with regard to Big Data analytics, businesses are considering the need for the NoSQL movement [10].…”

Section: Introductionmentioning

confidence: 99%

SQL Versus NoSQL Movement with Big Data Analytics

Venkatraman¹,

Fahd²,

Kaspi³

et al. 2016

IJITCS

View full text Add to dashboard Cite

Abstract-Two main revolutions in data management have occurred recently, namely Big Data analytics and NoSQL databases. Even though they have evolved with different purposes, their independent developments complement each other and their convergence would benefit businesses tremendously in making real-t ime decisions using volumes of co mplex data sets that could be both structured and unstructured. While on one hand many software solutions have emerged in supporting Big Data analytics, on the other, many NoSQL database packages have arrived in the market. However, they lack an independent benchmarking and co mparat ive evaluation. The aim of this paper is to provide an understanding of their contexts and an in-depth study to compare the features of four main NoSQL data models that have evolved. The performance comparison of traditional SQL with No SQL databases for Big Data analytics shows that NoSQL database poses to be a better option for business situations that require simplicity, adaptability, high performance analytics and distributed scalability of large data. This paper concludes that the NoSQL movement should be leveraged for Big Data analytics and would coexist with relational (SQL) databases.

show abstract