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A data analytic module to extend grafana functionality

Gan, Kian Yong (2019) A data analytic module to extend grafana functionality. Final Year Project, UTAR.

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    In this data driven era and the concept of industry 4.0, they will need the versatile platforms to visualize and analyse their event data to explore, interpret and understand the information hide in the data. A versatile data analytic platform can help to improve and assist the various kind of analytics. Those analytics including the descriptive, predictive and the advance prescriptive analytics. The Data analysis is an important process that make data become more valuable and gain more insights about the data. Various tools and systems used to analyse the huge volume of complex data to gain the insight from the raw data which is meaningless. In this project, we compare the strengths and weaknesses of the solutions, approaches and the popular visualization tools done by others. The candidate including the Amazon (Amazon Kinesis and Amazon Quick Sights), Microsoft Azure (Time Series Insights), IBM (Watson),InfluxDB, Elastic search, Open TSDB, Grafana and Kibana. We say that Grafana outrank the rest in terms of the dashboard features, the pricing, functionality and flexibility. Elastic search have a powerful search engine, it has a better query throughput which is suitable to handle large amount of data. We solve the limitation of Grafana that cannot perform custom query and visualize data from the custom query. We propose a solution to enhance the data analytic platform and improving the system extension functionality by develop a programme that sit between the data source (Elastic search) and the visualization tool Grafana. The new or advance analytics done by the data scientists can be added into the system through this program in the future easily.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: Q Science > Q Science (General)
    Divisions: Faculty of Information and Communication Technology > Bachelor of Computer Science (Honours)
    Depositing User: ML Main Library
    Date Deposited: 20 Aug 2019 12:18
    Last Modified: 20 Aug 2019 12:18
    URI: http://eprints.utar.edu.my/id/eprint/3490

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