Rule cubes for causal investigations |
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| Authors: | Axel Blumenstock Franz Schweiggert Markus Müller Carsten Lanquillon |
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| Affiliation: | (1) Department of Applied Information Processing, University of Ulm, Ulm, Germany;(2) Laboratory for Semantic Information Technology, University of Bamberg, Bamberg, Germany;(3) Department of Knowledge and Language Engineering, University of Magdeburg, Magdeburg, Germany |
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| Abstract: | With the complexity of modern vehicles tremendously increasing, quality engineers play a key role within today’s automotive industry. Field data analysis supports corrective actions in development, production and after sales support. We decompose the requirements and show that association rules, being a popular approach to generating explanative models, still exhibit shortcomings. Interactive rule cubes, which have been proposed recently, are a promising alternative. We extend this work by introducing a way of intuitively visualizing and meaningfully ranking them. Moreover, we present methods to interactively factorize a problem and validate hypotheses by ranking patterns based on expectations, and by browsing a cube-based network of related influences. All this is currently in use as an interactive tool for warranty data analysis in the automotive industry. A real-world case study shows how engineers successfully use it in identifying root causes of quality issues. |
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