Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions
Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions
Developing a reusable workflow engine
Journal of Systems Architecture: the EUROMICRO Journal
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Introduction to the special issue on semantic integration
ACM SIGMOD Record
AI Magazine - Special issue on semantic integration
Queue - Semi-structured Data
A Platform for Service-Oriented Integration of Software Engineering Environments
Proceedings of the 2009 conference on New Trends in Software Methodologies, Tools and Techniques: Proceedings of the Eighth SoMeT_09
Semantic Integration of Heterogeneous Data Sources for Monitoring Frequent-Release Software Projects
CISIS '10 Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems
Integrating Production Automation Expert Knowledge Across Engineering Stakeholder Domains
CISIS '10 Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems
Foundations for Event-Based Process Analysis in Heterogeneous Software Engineering Environments
SEAA '10 Proceedings of the 2010 36th EUROMICRO Conference on Software Engineering and Advanced Applications
Model-driven interoperability: MDI 2010
MODELS'10 Proceedings of the 2010 international conference on Models in software engineering
Workflow validation framework in distributed engineering environments
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems
Proceedings of the Eleventh ACM International Conference on Embedded Software
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In automation systems engineering, signals are considered as common concepts for linking information across different engineering disciplines, such as mechanical, electrical, and software engineering. Signal engineering is facing tough challenges in managing the interoperability of heterogeneous data tools and models of each individual engineering discipline, e.g., to make signal handling consistent, to integrate signals from heterogeneous data models/tools, and to manage the versions of signal changes across engineering disciplines. Currently, signal changes across engineering disciplines are primarily managed manually which is costly and error-prone. The main contribution of this paper is the signal change management process model as an input for semantic integration of engineering tools and models to support (semi) automated signal change management. Major result was that the process model discovery approach well supports the discovery of semantic integration requirements across heterogeneous engineering tools and models more efficient compared to the manual signal change management.