We have developed an improved version of the OntoDM ontology of data mining. It represents entities such as data, data mining tasks and algorithms, and generalizations (resulting from the latter). The improved version allows us to cover much of the diversity in data mining research, including recently developed approaches to mining structured data and constraint-based data mining. In contrast to other ontologies of data mining, OntoDM is a deep ontology and compliant to best practices in ontology engineering.
COBISS.SI-ID: 24216359
We have developed methods for learning trees for single and multi-target regression from massive or streaming data. To our knowledge, no other methods for structured prediction on streaming (or massive) data have been proposed so far. The methods can be used for analyzing very large datasets, such as those generated by high-throughput omics techniques in the area of systems biology.
COBISS.SI-ID: 24647719