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

  • Anca Maria Ivanescu
  • Philipp Kranen
  • Manfred Smieschek
  • Philip Driessen
  • Thomas Seidl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7239)

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.

Keywords

Fuel Injection Piecewise Linear Function Dynamic Time Warping Input Setting Single Time Step 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Anca Maria Ivanescu
    • 1
  • Philipp Kranen
    • 1
  • Manfred Smieschek
    • 1
  • Philip Driessen
    • 1
  • Thomas Seidl
    • 1
  1. 1.Data Management and Data Exploration GroupRWTH Aachen UniversityGermany

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