Abstract
Several business intelligence concepts show that it is possible to build a data warehouse based on heterogeneous quantitative and qualitative data. But the danger of flooding the management with information still remains. Business information should be made available according to the personal need of a manager by a self-defined push mechanism. The concept of active data warehousing is going to be expanded in this way, first. Messages about new relevant information should be created for quantitative and qualitative data as well. Due to this, there is a need for an enterprise specific ontology. This ontology works as an intermediate between current information and user profiles. Just interesting information are passed to the user. The second approach clusters qualitative data in favour of visualization. The usage of increasing cell structures provides a self organizing map. Decision makers will get the opportunity to search in document volumes, which are clustered.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Abramowicz, W.; Kalczyński, P.; Weçel, K.: Filtering the Web to Feed Data Warehouses. Springer, London et al. 2002.
Bishop, C. M. (1995): Neural Networks for Pattern Recognition, Oxford, 1995.
Codd, E.; Codd, S.; Salley, C: Providing OLAP (On-line Analytical Processing) to User-Analysts. An IT Mandate. White Paper. Arbor Software Corporation. 1993.
Colomb, R. M. (2002): Information Retrieval — The Architecture of Cyberspace, London, 2002
Elmasri, R./ Navathe, S. B. (2003): Fundamentals of database systems, 4 edition, München, 2003.
Ellingsworth, M.; Sullivan, D.: Text Mining Improves Business Intelligence and Predictive Modeling in Insurance. In: DM Review 13 (2003) 7, S. 42–44.
Felden, C: Konzept zum Aufbau eines Marktdateninformationssystems für den Energiehandel auf der Basis interner und externer Daten. DUV, Wiesbaden 2002.
Fensel, D.: Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce. Springer, Berlin, Heidelberg 2001.
Foltz, P.; Kintsch, W.; Landauer, T.: The Measurement of Textual Coherence with Latent Semantic Analysis. Discourse Process. Http://www.knowledgetechnologies.com/papers/dp2.foltz.pdf, 1998, Abruf am 2003-12-15.
Fritzke, B.: Wachsende Zellstrukturen. Ein selbstorganisierendes Neuronales Netzwerkmodell. Arbeitsberichte des Instituts für mathematische Maschinen und Datenverarbeitung (Informatik), 25. Bd., Nr. 9. Erlangen 1992.
Fritzke, B.: Vektorbasierte Neuronale Netze. Habilitationsschrift. Shaker, Aachen 1998.
Hotho, A.; Staab, St.; Stumme, G.: Wordnet improves Text Document Clustering. In: Proceedings of the Semantic Web Workshop at SIGIR-2003, 26th Annual International ACM SIGIR Conference, July 28–August 1, 2003, Toronto, Canada.
Kamphusmann, T.: Text-Mining. Eine praktische Marktübersicht. Symposium, Düsseldorf 2002.
Kohonen, T.: Overture. In (Seiffert, U.; Jain, L. Hrsg.): Self-Organizing Neural Networks. Recent Advances and Applications. Studies in Fuzziness and Soft Computing, Nr. 78. Physica, Heidelberg et al. 2002; S. 1–12.
Kohonen, T.; Kaski, S.; Lagus, K.; Salojärvi, J.; Honkela, J.; Paatero, V.; Saarela, A.: Self organizing of a massive document collection. In: IEEE Transactions on Neural Networks, Vol. 11, No. 3, Mai 2000, S. 574–606.
Porter, M.: Wettbewerbsstrategie (Competitive Strategy). Methoden zur Analyse von Branchen und Konkurrenten. 10. Aufl. Campus, Frankfurt et al., 1999.
Pullwitt, D.: Integrating Contextual Information to Enhance SOM-based Text Document Clustering. Graduiertenkolleg Wissenspräsentation, Institut für Informatik, Universität Leipzig. Http://www.informatik.uni-leipzig.der/~pullwitt/papers/nnsi.pdf, 2001, Abruf am 2003-12-13.
Sebastiani, F.: Machine Learning in Automated Text Categorization. In: ACM Computing Surveys, Vol. 34, No. 1, März 2002, S. 1–47.
Sullivan, D.: Document Warehousing and Text Mining. Techniques for Improving Business Operations, Marketing, and Sales. Wiley, New York et al. 2001.
Welge, M.; Al-Laham, A.: Strategisches Management. Grundlagen-Prozess-Implementierung. 3. Aufl. Gabler, Wiesbaden 2001.
Zavrel, J.: Neural Information Retrieval. An Experimental Study of Clustering and Browsing of Document Collections with Neural Networks. Http://ilk.kub.nl/~zavrel/zavrel.scriptie.ps.Z, 1995, Abruf am 2003-12-13.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Felden, C., Chamoni, P. (2005). User-Oriented Filtering of Qualitative Data. In: Fleuren, H., den Hertog, D., Kort, P. (eds) Operations Research Proceedings 2004. Operations Research Proceedings, vol 2004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27679-3_54
Download citation
DOI: https://doi.org/10.1007/3-540-27679-3_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24274-1
Online ISBN: 978-3-540-27679-1
eBook Packages: Business and EconomicsBusiness and Management (R0)