Big Data Analytics on Cloud: challenges, techniques and technologies
Page: 37-43
2020
- 123Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage123
- Downloads86
- Abstract Views37
Artifact Description
These days it is known that Big Data Analytics is taking a huge attention from researchers and also from business. We all are witness of the data growth that every institution, company or even individuals store in order to use them in the future. There is a big potential to extract useful data from this Big Data that is stored usually in Cloud because sometimes there is not enough local space to store big amounts of data. There is a huge number of sectors where Big Data can be helpful including economic and business activities, public administration, national security, scientific researches in many areas, etc. This data in order to be used must get processed, usually by using Big Data Analytics Techniques. It is for sure that the future of business and technology will be relied on Big Data Analytics. This paper aims to show how big data is analyzed especially when it is deployed on cloud as well as the challenges, techniques and technologies that are used and can be used, in order to analyze Big Data on Cloud. We discuss and implement different methodologies of Big Data Analytics on Cloud.
Bibliographic Details
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