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The application of big data using MongoDB: Case study with SCeLE Fasilkom UI forum data

Authors: Argianto Rahartomo; Rizal Fathoni Aji; Yova Ruldeviyani;

The application of big data using MongoDB: Case study with SCeLE Fasilkom UI forum data

Abstract

Big Data is a condition in which data size in a database is very large so it is difficult to be managed. An e-Learning application, like SCeLE Fasilkom UI (scele.cs.ui.ac.id), also has a very large data. SCeLE has hundreds of forum data, and each forum has at least 4000 threads of discussion. In addition, one thread can have at least dozens or hundreds posts. Therefore, it may further experience data growth problem, which will be difficult to be handled by RDBMS, such as MySQL that is currently used. In order to solve this problem, a research been conducted to apply Big Data in SCeLE Fasilkom UI, which implementation is aimed to increase SCeLE's data management performance. The implementation of Big Data in the research used MongoDB as the system's DBMS. The research result showed that MongoDB obtain better results than MySQL in SCeLE Fasilkom UI forum data case in terms of speed.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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