PA-Miner: Process Analysis using Retrieval, Modeling, and Prediction

Handling experimental measurements is an essential part of research and development in a multitude of disciplines, since these contain information about the underlying process. Besides an efficient and effective way of exploring multiple results, researchers strive to discover correlations within the measured data. Moreover, model-based prediction of expected measurements can be highly beneficial for designing further experiments. In this demonstrator we present PA-Miner, a framework which incorporates advanced database techniques to allow for efficient retrieval, modeling and prediction of measurement data. We showcase the components of our framework using the fuel injection process as an example application and discuss the benefits of the framework for researchers and practitioners.

Authors: Zimmer (née Ivanescu) A., Kranen P., Smieschek M., Driessen P., Seidl T.
Published in: The 17th International Conference on Database Systems for Advanced Applications (DASFAA 2012), Busan, South Korea
Language: EN
Year: 2012


Conference: DASFAA
Url:DASFAA 2012
Type: Conference papers (peer reviewed)
Research topic: Data Analysis and Knowledge Extraction