Information represents value for everyone, however acquiring it is more and more challenging nowadays, since there are vast amount of data wherever we are. The analytical systems are trying to help us and they provide the data of the sources to the users in a form that is easy to interpret. In my thesis, such an analytical system's planning and implementation are presented through a news reader service.
This document presents the options for gathering data from different sources of news. It gives an overview of modern technologies which can be used to implement such a system, like the serverless infrastructure, crawler frameworks, GraphQL query language, and React.js based applications. Moreover, it outlines the requirements and future architecture, and then it provides the details of the implementation process. Conclusively, it shows the final version of the cross-platform application and the website of the administration platform.
The overall system covers the fields from gathering the data until presenting them to the end-users, as a result, it can be a solid foundation for a machine learning based recommendation engine.