A data model inference algorithm for schemaless process modeling

Abstract

Mobile devices have become ubiquitous not only in the consumer domain but also support the digitalization of business operations though business apps. Many frameworks for programming cross-platform apps have been proposed, but only few modeling approaches exist that focus on platform-agnostic representations of mobile apps. In addition, app development activities are almost exclusively performed by software developers, while domain experts are rarely involved in the actual app creation beyond requirements engineering phases. This work concentrates on a model-driven approach to app development that is also comprehensible to non-technical users. With the help of a graphical domain-specific language, data model, view representation, business logic, and user interactions are modeled in a common model from a process perspective. To enable such an approach from a technical point of view, an inference mechanism is presented that merges multiple partial data models into a global specification. Through model transformations, native business apps can then be generated for multiple platforms without manual programming.