Welcome to the InfoSci Platform
Reference Hub2
A Meta-Mining Ontology Framework for Data Processing

A Meta-Mining Ontology Framework for Data Processing

Man Tianxing, Nataly Zhukova, Alexander Vodyaho, Tin Tun Aung
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 20
ISSN: 1947-3176|EISSN: 1947-3184|EISBN13: 9781799861652|DOI: 10.4018/IJERTCS.2021040103
Cite Article Cite Article

MLA

Tianxing, Man, et al. "A Meta-Mining Ontology Framework for Data Processing." IJERTCS vol.12, no.2 2021: pp.37-56. http://doi.org/10.4018/IJERTCS.2021040103

APA

Tianxing, M., Zhukova, N., Vodyaho, A., & Aung, T. T. (2021). A Meta-Mining Ontology Framework for Data Processing. International Journal of Embedded and Real-Time Communication Systems (IJERTCS), 12(2), 37-56. http://doi.org/10.4018/IJERTCS.2021040103

Chicago

Tianxing, Man, et al. "A Meta-Mining Ontology Framework for Data Processing," International Journal of Embedded and Real-Time Communication Systems (IJERTCS) 12, no.2: 37-56. http://doi.org/10.4018/IJERTCS.2021040103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.