Development of a Microservice-based Approach for Workflow-supported Data Collection

Since smart devices like smartphones are increasingly present in daily life, there is great potential using them as a source for data collection. Other work shows for example that by utilizing smartphones combined with Mobile Crowdsensing (MCS) and Ecological Momentary Assessments (EMA) for data collection in a mHealth context, valuable data can be gathered which can provide new insights for the treatment of chronic disorders. Furthermore, using the gathered data, feedback can be generated for patients suffering from these disorders to help them get better control of their medical condition.

To enable these data collection procedures, a scalable and reliable backend is needed to handle a varying amount of users in different use cases. Therefore, the usage of a microservice architecture with a central orchestration system providing a RESTful interface has been suggested in previous work.

This thesis proposes such a distributed, microservice-based approach for data collection using BPMN workflows to model control and data flow between these services, so that they can be orchestrated by the open-source workflow engine Zeebe and monitored using respective workflow monitoring tools. With this approach, generic data collection procedures in various contexts gathering questionnaire and sensor data can be supported by providing a RESTful interface, enabling both frontend components to allow for participation on these kind of data collection platforms as well as other tools to retrieve gathered data and perform analysis functions on it, thus potentially revealing new insights in the respective domain.

A subset of the features considered in the proposed concept are subsequently implemented and discussed, using an existing frontend implementation as a guideline for the implemented features.