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dc.contributor.author |
FODIL et MOKRAN, Youssouf Islam et Abde lrr ahim |
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dc.date.accessioned |
2020-12-06T14:29:59Z |
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dc.date.available |
2020-12-06T14:29:59Z |
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dc.date.issued |
2020 |
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dc.identifier.uri |
http://dspace.univ-msila.dz:8080//xmlui/handle/123456789/21706 |
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dc.description.abstract |
The development of social media creates a multitude of new Real-Time Analytics (RTA) application possibilities. However, already the topic of big data forced the use of analytical solutions and among them, there are some decent near real-time solutions but using them in the social media analytics domain will reveal some other flaws especially in the context of the real-time analytics where the social media analytics native tools can’t be challenged in general and in real-time analytics specifically.
This work aims to address this gap by exposing a decent tool (Technology stack) excels at handling real-time social media analytics by providing abilities like fast processing of data, real-time data aggregation, ingestion, interactive, and effective data exploration, and visualization.
We describe our experiments for the architecture in Twitter use case and evaluate the functional and non-functional tests such as real-time update performance and time taken for data flow among components. All components were able to handle their functionalities properly. |
en_US |
dc.language.iso |
en |
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dc.publisher |
FACULTY: Mathematics and Computer Science DEPARTMENT: Computer Science - OPTION: SI-GL |
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dc.subject |
Real-time data : Analytics Apache Druid |
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dc.title |
Real-time data Analytics Apache Druid |
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dc.type |
Thesis |
en_US |
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