Kdd Ontology

As data mining applications became more popular, organizations providing KDD (Knowledge Discovery in Database) services have accumulated a growing number of stored documents and processes of their past projects. Moreover, developing KDD projects usually demands several tools, programming languages and methodologies, as well several descriptions of data generated during the development of such projects. In fact, one the major practical problems regarding KDD is how to provide interoperability among different platforms. Another important practical problem with KDD is the lack of platforms capable of supporting the reuse of knowledge acquired from past projects. This work proposes an ontology to the KDD projects and at using these metadata for knowledge reuse and evaluating new data mining projects (meta-data mining).

Example

In Construction...

See also

In Construction...

Data Manipulation

• • Feature extraction

General References

• Bartlmae, K. and Riemenschneider, M., Case based reasoning for knowledge management in kdd projects. in In Proceedings of the 3rd International Conference on Practical Aspects of Knowledge Management (PAKM 2000), (Basel, Switzerland., 2000).
• Birk, A., Surmann, D. and Althoff, K., Applications of Knowledge Acquisition in Experimental Software Engineering. in ACM INTERNET COMPUTING, (Alemanha. Fraunhofer, 2003), Center for Experimental Software Engenering.
• Conover, W.J. Practical Nonparametric Statistics. John Wiley & Sons, New York, 1999.
• Cunha, R. DMEasy: Metodologia para Desenvolvimento de Solucoes Centro de Informatica, UFPE, Recife, 2005.
• Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. The KDD process for extracting useful knowledge from volumes of data. Commun. ACM, 39 (11). 27--34.
• Fayyad, U.M., Piatetsky-Shapiro, G. and Smyth, P. From data mining to knowledge discovery: an overview. Advances in knowledge discovery and data mining. 1--34.

Gu, C.-s. Mining data mining process: Meta-mining Imperial College of Science Technology and Medicine, University of London, London, 2002.

• Haseltine, E., User-centered design for KDD. in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, (Seattle, WA, USA, 2004), ACM Press, 1-1.
• Hofmann, M. and Tierney, B., The involvement of human resources in large scale data mining projects. in Proceedings of the 1st international symposium on Information and communication technologies, (Dublin, Ireland, 2003), Trinity College Dublin, 103--109.
• Hosmer, D.W. and Lemeshow, S. Applied logistic regression. Wiley, New York, 2000.
• Institute, P.M. A Guide to the Project Management Body of Knowledge: PMBOK Guide 2000 Edition. The Project Management *Institute Inc., Newtown Square, Pennsylvania USA, 2000.
• Major, J.A. and Mangano, J.J. Selecting among Rules Induced from a Hurricane Database. Journal of Intelligent Information Systems, vol. 4. 39-52.
• Neaga, I. Framework for Distributed Knowledge Discovery Systems Embedded in Extended Enterprise Manufacturing Engineering, Loughborough University, Loughborough, United Kingdom, 2003.
• NeuroTech Ltda. www.neurotech.com.br.
• Schneider, K., von Hunnius, J.-P. and Basili, V.R. Experience in Implementing a Learning Software Organization. IEEE Softw., 19 (3). 46--49.
• Vaduva, A., Kietz, U. and cker, R., M4: a metamodel for data preprocessing. in Proceedings of the 4th ACM international workshop on Data warehousing and OLAP, (Atlanta, Georgia, USA, 2001), ACM Press, 85-92.
• Wirth, R. and Hipp, J. CRISP-DM: Towards a standard process model for data mining In Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining (PADD00), 2000.
• Witten, I.H. and Frank, E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, CA, 2005.