DocumentCode :
3165245
Title :
Rule Cubes for Causal Investigations
Author :
Blumenstock, Axel ; Schweiggert, Franz ; Müller, Markus
Author_Institution :
Univ. of Ulm, Ulm
fYear :
2007
fDate :
28-31 Oct. 2007
Firstpage :
53
Lastpage :
62
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 ex- planative models, still exhibit shortcomings. Recently proposed interactive rule cubes 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.
Keywords :
automobile industry; computational complexity; data analysis; interactive systems; automotive industry; causal investigations; cube-based network; field data analysis; interactive tool; ranking patterns; vehicles complexity; warranty data analysis; Automotive engineering; Data analysis; Data engineering; Data mining; Information processing; Information technology; Mining industry; Production; Vehicles; Warranties;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on
Conference_Location :
Omaha, NE
ISSN :
1550-4786
Print_ISBN :
978-0-7695-3018-5
Type :
conf
DOI :
10.1109/ICDM.2007.29
Filename :
4470229
Link To Document :
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