Author: Pei Hsien Chuah

Abstract: Microservice-based systems consist of loosely coupled, independent distributed services, communicating over the network. The integration of the frontend in microservices is a central problem. In contrast to the back end services, one independent service is often used for the front end of an application. This contradicts the principle of microservice-based systems, since a central, monolithic service is responsible for the entire frontend. This thesis examines how front end user interfaces can be integrated into microservices. The existing front end architectures in a microservice context are presented and analyzed with regard to advantages, disadvantages and challenges. A structured literature analysis is carried out to collect data for theory formation. In addition, expert interviews are conducted as part of a case study with industrial partners in order to get an overview of the methods used in practice. Subsequently, in the scope of an action research study, one integration options for user interfaces in microservices is applied for a specific application. The results of the action research are evaluated and compared to the contents of the structured literature analysis. The objective of the thesis is to show the implications of the comparison with a guideline. Practitioners can use this comparison to select a suitable front end integration solution for their application. Scientists can build on this work to develop further integration solutions, refine existing ones, and extend them.

Keywords: Microservices, micro front ends, microservice UIs, JValue

PDF: Master Thesis

Reference: Pascal Vahldiek. Guidelines für die Integration von User Interfaces in Microservice-basierten Systemen. Master Thesis. Friedrich-Alexander-Universität Erlangen-Nürnberg: 2022.

Abstract: Open data has been known for having data quality issues that require complex data cleansing and data transformation in order to be usable for data analysis, data visualization, training machine learning algorithms, and other data science activities. Open Data Service (ODS) is a software project that aims at creating an interface for reliable and safe consumption of open data. It does so by providing the necessary tooling and infrastructure needed for collaboration on eliminating open data usability obstacles. ODS underwent several cycles of development to better serve its purposes, which include functioning as an extract, transform, load (ETL) tool to consume open data from different sources and adapt it to different needs. In this work we evaluate and analyze ODS performance in that regard. Specifically, as part of a data pipeline supporting a real-world data science application.

PDF: Master Thesis

Reference: Shady Hegazy. Study and Analysis of the Performance of JValue Open Data Service as Part of a Data Pipeline Supporting An Online Learning Model. Master Thesis. Friedrich-Alexander-Universität Erlangen-Nürnberg: 2022.