Intelligent Incident Management : Ticketing Software API Integration for Efficient Visualization and Statistical Insights
Gimenes Alvarenga Ferrari, Melissa (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2024050224834
https://urn.fi/URN:NBN:fi-fe2024050224834
Tiivistelmä
This thesis focuses on creating an application based on the TOPdesk ticketing system, aiming to produce a more efficient and user-friendly software. By incorporating advanced filtering options, statistical insights, and intuitive design elements, the webpages created seek to simplify the ticket management process.
Implemented through the utilization of tools and technologies such as Visual Studio Code, Node.js, and SQLite, the project offers a comprehensive exploration of the iterative development process. HTML, CSS, and JavaScript facilitate the creation of an intuitive and visually appealing interface, while backend development ensures integration with the TOPdesk API for data retrieval. The resulting webpage enables users to navigate ticket categories efficiently, access detailed ticket information, and gain insights into ticket resolution metrics.
The thesis achieved its main goal of creating a filtered version of the TOPdesk ticketing system and revealing statistics of incident-solving processes.
Implemented through the utilization of tools and technologies such as Visual Studio Code, Node.js, and SQLite, the project offers a comprehensive exploration of the iterative development process. HTML, CSS, and JavaScript facilitate the creation of an intuitive and visually appealing interface, while backend development ensures integration with the TOPdesk API for data retrieval. The resulting webpage enables users to navigate ticket categories efficiently, access detailed ticket information, and gain insights into ticket resolution metrics.
The thesis achieved its main goal of creating a filtered version of the TOPdesk ticketing system and revealing statistics of incident-solving processes.