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MAtchmaking Features for mAchine Learning Data Analysis
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README.md

README.md

Mafalda - MAtchmaking Features for mAchine Learning Data Analysis

Mafalda is a semantic-enhanced framework for data mining on sensor streams, amenable to resource-constrained pervasive contexts. It merges an ontology-based characterization of data distributions with non-standard reasoning for a fine-grained event detection by treating the typical classification problem of Machine Learning as a resource discovery.

Reference ontologies and datasets used for the framework evaluation are available in the project sub-folders.

References

If you want to refer to Mafalda dataset and ontologies in a publication, please cite the following paper:

@article{mafalda-swj18,
	author 	= {Michele Ruta and Floriano Scioscia and Giuseppe Loseto and Agnese Pinto and Eugenio {Di Sciascio}},
	title 	= {Machine Learning in the Internet of Things: a Semantic-enhanced Approach},
	journal = {Semantic Web},
	year    = 2018,
	number  = {Pre-press},
	pages   = {1-22},
	volume  = {Pre-press}
	url 	= {http://dx.doi.org/10.3233/SW-180314}
}
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