Abstract
In this paper we present an interactive, web-based data mining application that supports quality analysis in the automotive industry. Our tool is designed to help automotive engineers in their task of identifying the root cause of quality issues. Knowing what exactly caused a problem and identifying vehicles that are most likely to be affected by the issue, helps in planning and implementing effective service actions. We show how data mining can be applied in the given application domain, point out the key role of interactivity and propose an appropriate software architecture.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blumenstock, A., Hipp, J., Kempe, S., Lanquillon, C., Wirth, R.: Interactivity closes the gap. In: Proceedings of the KDD 2006 Workshop on Data Mining for Business Applications, Philadelphia (2006)
Blumenstock, A., Schweiggert, F., Mueller, M.: Rule cubes for causal investigation. In: Proceedings of the Seventh IEEE International Conference on Data Mining, Philadelphia (2007)
Buschmann, F., Meunier, R., Rohnert, H., Sommerlad, P., Stal, M.: Pattern-Oriented Software Architecture: A System of Patterns, Model-View-Controller, pp. 125–143. John Wiley & Sons, Chichester (1996)
Elomaa, T., Rousu, J.: General and efficient multisplitting of numerical attributes. Machine Learning 36, 201–244 (1999)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Cambridge (1996)
Fowler, M.: Passive View (2006), http://www.martinfowler.com/eaaDev/PassiveScreen.html
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Jaroszewicz, S., Simovici, D.A.: Interestingness of frequent itemsets using bayesian networks as background knowledge. In: KDD 2004: Proceedings of the 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 178–186. ACM Press, New York (2004)
Krasner, G.E., Pope, S.T.: A cookbook for using the model-view controller user interface paradigm in smalltalk-80. J. Object Oriented Program. 1(3), 26–49 (1988)
Potel, M.: MVP: Model-view-presenter, the taligent programming model for c++ and java (1996), http://www.wildcrest.com/Potel/Portfolio/mvp.pdf
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Scheffer, T., Wrobel, S.: Finding the most interesting patterns in a database quickly by using sequential sampling. Journal of Machine Learning Research 3, 833–862 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fritzsche, S., Mueller, M., Lanquillon, C. (2009). Interactive Quality Analysis in the Automotive Industry: Concept and Design of an Interactive, Web-Based Data Mining Application. In: Filipe, J., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2009. Lecture Notes in Business Information Processing, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01347-8_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-01347-8_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01346-1
Online ISBN: 978-3-642-01347-8
eBook Packages: Computer ScienceComputer Science (R0)