Abstract
The “Machining Intelligence Network” (MachInNet) project tackles the challenges to “unearthing” manufacturing knowledge from NC codes (numerical control codes), tool layouts and other manufacturing documents, and to making it accessible for daily use, e.g., for feature-based NC planning. A new approach using data mining algorithms and semantic search technologies makes it possible to reverse engineer data from different sources and make it available for explicit use with the help of a semantic, Internet-based knowledge network. The business rationale of MachInNet is to help SMEs (small and medium-sized enterprises) to manage a large variety of technologies, to avoid redundant engineering efforts, and to accelerate industrial engineering of mechanical parts.
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
Similar content being viewed by others
References
M. Ester, H.P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD’96), Menlo Park, ed. by E. Simoudis, J. Han, U. Fayyad (AAAI, 1996), pp. 226–231
J. Han, M. Kamber, Data Mining – Concepts and Techniques, 2 edn. (Morgan Kaufmann, San Francisco, 2006)
G. Marczinski, N.P. Steinmann, Von Data Mining und semantischer Suche – Neue Wege zum Aufbau von Feature-Bibliotheken und Technologiedatenbanken. Zeitschrift für wirtschaftlichen Fabrikbetrieb (ZwF) 107(1–2), 82–86 (2012)
Further Reading
C. Beecks, I. Assent, T. Seidl, Content-based multimedia retrieval in the presence of unknown user preferences, in Advances in Multimedia Modeling. Volume 6523 of Lecture Notes in Computer Science (Springer, Berlin/Heidelberg/New York, 2011a), pp. 140–150
C. Beecks, J. Lokoc, T. Seidl, T. Skopal, Indexing the signature quadratic form distance for efficient content-based multimedia retrieval, in Proceedings of the 1st ACM International Conference on Multimedia Retrieval (ICMR’11), Trento (ACM, Berlin/Heidelberg/New York, 2011b)
C. Beecks, S. Wiedenfeld, T. Seidl, Improving the efficiency of content-based multimedia exploration, in 20th International Conference on Pattern Recognition (ICPR’10), Istanbul (IEEE, Washington, DC, 2010), pp. 3163–3166. http://dblp.uni-trier.de/db/conf/icpr/icpr2010.html#BeecksWS10
S. Günnemann, I. Färber, E. Müller, I. Assent, T. Seidl, External evaluation measures for subspace clustering, in Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM’11), Glasgow (ACM, New York, 2011), pp. 1363–1372
P. Kranen, S. Günnemann, S. Fries, T. Seidl, MC-Tree: improving bayesian anytime classification, in Scientific and Statistical Database Management. Volume 6187 of Lecture Notes in Computer Science (Springer, Berlin/Heidelberg/New York, 2010), pp. 252–269
Y. Rubner, C. Tomasi, L. Guibas, A metric for distributions with applications to image databases, in 6th International Conference on Computer Vision (ICCV’98), Bombay (IEEE, Washington, DC, 1998), pp. 59–66
J. Sivic, A. Zisserman, Video Google: a text retrieval approach to object matching in videos, in Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV’03), Nice, vol. 2 (IEEE, Washington, DC, 2003), pp. 1470–1477
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Färber, I., Fries, S., Marczinski, G., Seidl, T., Steinmann, NP. (2014). Machining Intelligence Network: Data Mining and Semantic Search in Manufacturing Industry. In: Wahlster, W., Grallert, HJ., Wess, S., Friedrich, H., Widenka, T. (eds) Towards the Internet of Services: The THESEUS Research Program. Cognitive Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-06755-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-06755-1_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06754-4
Online ISBN: 978-3-319-06755-1
eBook Packages: Computer ScienceComputer Science (R0)