Automation of Enterprise Architecture Discovery based on Event Mining from API Gateway logs: State of the Art

Enterprise Architecture (EA) is defined as a coherent set of principles, methods, and models used to design an organizational structure, containing business processes, information systems (IS), IT infrastructure, and other artefacts aiming the alignment of business, IT, and other organizational dimensions with the strategic objectives of a company. One of the most critical in Enterprise Architecture Management (EAM) is creating EA models representing different viewpoints for managing various company concerns on its IT landscape. At the same time, the speed of changes pressures EAM to automate modeling activities. In this context, architects need adequate tools to discover the current state of EA, enabling analyzing improvement opportunities and support architectural decisions making in a fast and agile way with more precision about the real conditions. EA Mining is the use of data mining techniques to automate the creation or update of EA models with data collected from different data sources. This work presents an exploratory review of the literature to gather the state of art on EA mining models from applications logs pursuing to automate the architecture modeling. Through this literature review, we identified the main aspects, techniques, and challenges of EA modeling automation.