Using the TUM Campus iOS App (iTCA) students and employees of the Technical University of Munich (TUM) have access to all day-to-day information concerning the university. This includes a broad range of data about lectures as well as venues and events of the TUM. Today, the app’s wide range of data consists of various types of data presented in different views across the app. While there is a searching option in some views included, this is limited to one type and one view at a time.
Since all the data processed by the iTCA is so diverse, this thesis introduces a Global In-App Search (GIAS), which allows searching for any data type easily. Combining the different types into a single view simplifies information access without the need to navigate across different views. The GIAS relies on machine learning to identify the most likely type of data, using a custom algorithm that compares individual strings. Therefore various metrics were introduced and analyzed to find the best results for a given search query. Additionally, the app architecture was improved by refactoring the API implementations. The refactoring of the API enhances maintainability, increases implementation efficiency and helps to add new features faster such as the GIAS.
For future work, the iTCA is expected to work offline reducing data consumption for new features such as the GIAS. As a result, possible persistence storage frameworks that enable local storage of data on the user’s device were analyzed. Furthermore, the local data storage could accelerate the loading times and improve the user experience.