ABSTRACT
Focusing on massive high-dimensional data reduction, this paper established the ontology model of data reduction, to improve the related theoretical methods of data reduction, and to propose an ontology-based data reduction system architecture. Data reduction workflow model, workflow pattern mining, and workflow optimization and data reduction experiment design based on knowledge base are studied to achieve the accumulation, sharing and reuse of data reduction knowledge and enhance the intelligence level of data reduction process as well as the credibility of reduction results. A mechanism of meta-reduction system framework and its technology is put forward so as to improve the availability, flexibility and applicability of data reduction system.
- Sowmya R, Suneetha K R. Data Mining with Big Data{C}// International Conference on Intelligent Systems and Control. IEEE, 2017:246--250.Google Scholar
- Yang Q, Piantanida P, Gündüz D. The multi-layer information bottleneck problem{J}. arXiv preprint arXiv:1711.05102, 2017.Google Scholar
- Belli S, Contursi A, Davies R I. Flame: A Flexible Data Reduction Pipeline for Near-Infrared and Optical Spectroscopy{J}. arXiv preprint arXiv:1710.05924, 2017.Google Scholar
- Gruber T R. A tramlation approach tO portable ontology specificatiom{R}. {s. I}: Knowledge System Laboratory. 1993.Google Scholar
- Gil J M, Montes J F A, Alba E, et al. Optimizing ontology alignments by using genetic algorithms{J}. 2018.Google Scholar
- Balakirsky S, Schlenoff C, Fiorini S R, et al. Towards a robot task ontology standard{C}//ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017: V003T04A049-V003T04A049.Google Scholar
- Melanie Hilario, Phong Nguyen, Huyen Do, Adam Woznica, Alexandros Kalousis. Ontology- Based Meta-Mining of KnowledgeDiscovery Workflows{J}. http://www.dmo- foundry.org/ Ontology-Based Meta-Mining of Knowledge workflow.pdf.Google Scholar
- C. Giraud-Carrier, R. Vilalta, and P. Brazdil.Introduction to the special issue onmeta-learning. Machine Learning, 54:187--193, 2004. Google ScholarDigital Library
- M. L. Anderson and T. Oates.A review of recent research in metareasoning andmetalearning. AI Magazine, 28(1):7--16, 2007.Google Scholar
- P. Brazdil, C. Giraud-Carrier, C. Soares, and R. Vilalta, editors. Meta-learning:Applications to Data Mining. Springer, 2009. Google ScholarDigital Library
- R. Vilalta, C. Giraud-Carrier, P. Brazdil, and C.Soares.Using meta-learning tosupport data mining. International Journal of Computer Science and Applications, 1(1):31--45, 2004.Google Scholar
- R. Srikant and R. Agrawal. Mining sequential patterns: generalizations and performance improvements. In Proc.5th International Conference on Extending DatabaseTechnology, pages 3--17. Springer, 1996. Google ScholarDigital Library
- Steven Skiena.Implementing discrete mathematics: combinatorics and graph theorywith Mathematica. Addison-Wesly Longman Publishing Co., Inc., Boston, MA, USA, 1991. Google ScholarDigital Library
- M. Zaki. Efficiently mining frequent trees in a forest: algorithms and applications. IEEE.Google Scholar
- FANG Lehong, HAO Wenning, YU Xiaohan, et al. Method about effectiveness evaluation of data reduction based on user interest degree{J}. Computer Engineering and Applications, 2017, 53(15):144--148.Google Scholar
- KANG Ruizhi, HAO Wenning.Mehod about evaluating effect of data reduction{J}. Computer Engineering and Applications, 2016, 52(15):93--96.Google Scholar
Index Terms
- Data Reduction Workflow Patterns Mining and Optimization based on Ontology
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