C4.5: programs for machine learning
C4.5: programs for machine learning
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Planning for verification, validation, and accreditation of modeling and simulation applications
Proceedings of the 32nd conference on Winter simulation
Simulation: The Practice of Model Development and Use
Simulation: The Practice of Model Development and Use
Proceedings of the 35th conference on Winter simulation: driving innovation
Automated analysis of simulation output data
WSC '05 Proceedings of the 37th conference on Winter simulation
Managing Information Quality: Increasing the Value of Information in Knowledge-intensive Products and Processes
Advances in analytics: integrating dynamic data mining with simulation optimization
IBM Journal of Research and Development - Business optimization
A new procedure model for verification and validation in production and logistics simulation
Proceedings of the 40th Conference on Winter Simulation
A methodology for input data management in discrete event simulation projects
Proceedings of the 40th Conference on Winter Simulation
Principles of human-computer collaboration for knowledge discovery in science
Artificial Intelligence
RuleGrowth: mining sequential rules common to several sequences by pattern-growth
Proceedings of the 2011 ACM Symposium on Applied Computing
Winter Simulation Conference
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Discrete-event simulation has been established as an important methodology in various domains. In particular in the automotive industry, simulation is used to plan, control, and monitor processes including the flow of material and information. Procedure models help to perform simulation studies in a structured way and tools for data preparation or statistical analysis provide assistance in some phases of simulation studies. However, there is no comprehensive data assistance following all phases of such procedure models. In this article, a new approach combining assistance functionalities for input and output data analysis is presented. The developed tool -- EDASim -- focuses on supporting the user in selection, validation, and preparation of input data as well as to assist the analysis of output data. The proposed methods have been implemented and initial evaluations of the concepts have led to promising feedback from practitioners.