PA-Miner: process analysis using retrieval, modeling, and prediction

Authors:
Anca Maria Ivanescu;Philipp Kranen;Manfred Smieschek;Philip Driessen;Thomas Seidl
Affiliations:
Data Management and Data Exploration Group, RWTH Aachen University, Germany;Data Management and Data Exploration Group, RWTH Aachen University, Germany;Data Management and Data Exploration Group, RWTH Aachen University, Germany;Data Management and Data Exploration Group, RWTH Aachen University, Germany;Data Management and Data Exploration Group, RWTH Aachen University, Germany
Venue:
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Year:
2012

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Abstract

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.