Automated Enterprise Architecture Model Mining
Peter Hillmann, Erik Heiland, Andreas Karcher

TL;DR
This paper presents an automated approach for mining enterprise architecture models from organizational metadata, reducing manual effort and enabling agile decision-making across business and technical layers.
Contribution
It introduces a novel system that automatically generates enterprise architecture models using network and log data, supporting multiple modeling languages and holistic analysis.
Findings
Successfully applied to a small company's infrastructure
Automates model generation from network and log data
Supports multiple enterprise architecture languages
Abstract
Metadata are like the steam engine of the 21st century, driving businesses and offer multiple enhancements. Nevertheless, many companies are unaware that these data can be used efficiently to improve their own operation. This is where the Enterprise Architecture Framework comes in. It empowers an organisation to get a clear view of their business, application, technical and physical layer. This modelling approach is an established method for organizations to take a deeper look into their structure and processes. The development of such models requires a great deal of effort, is carried out manually by interviewing stakeholders and requires continuous maintenance. Our new approach enables the automated mining of Enterprise Architecture models. The system uses common technologies to collect the metadata based on network traffic, log files and other information in an organisation. Based on…
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Taxonomy
TopicsInformation Technology Governance and Strategy · Service-Oriented Architecture and Web Services · Software System Performance and Reliability
