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By definition, those sources are heterogeneous and their integration is not a trivial task. A common approach to exploit streaming sensor data potential is to use machine learning techniques for predictive analytics in a way that is agnostic to the domain knowledge. Such an approach can be easily integrated in various use cases. In this paper, we propose a novel framework for data fusion of a set of heterogeneous data streams. The proposed framework enriches streaming sensor data with the contextual and historical information relevant for describing the underlying processes. The final result of the framework is a feature vector, ready to be used in a machine learning algorithm. The framework has been applied to a cloud and to an edge device. In the latter case, incremental learning capabilities have been demonstrated. The reported results illustrate a significant improvement of data-driven models, applied to sensor streams. Beside higher accuracy of the models the platform offers easy setup and thus fast prototyping capabilities in real-world applications.<\/jats:p>","DOI":"10.3390\/s19081955","type":"journal-article","created":{"date-parts":[[2019,4,25]],"date-time":"2019-04-25T11:19:04Z","timestamp":1556191144000},"page":"1955","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Streaming Data Fusion for the Internet of Things"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4918-0650","authenticated-orcid":false,"given":"Klemen","family":"Kenda","sequence":"first","affiliation":[{"name":"Artificial Intelligence Lab, Jozef Stefan Institute, 1000 Ljubljana, Slovenia"},{"name":"Jozef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1335-7960","authenticated-orcid":false,"given":"Bla\u017e","family":"Ka\u017ei\u010d","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Lab, Jozef Stefan Institute, 1000 Ljubljana, Slovenia"},{"name":"Jozef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7010-314X","authenticated-orcid":false,"given":"Erik","family":"Novak","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Lab, Jozef Stefan Institute, 1000 Ljubljana, Slovenia"},{"name":"Jozef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0360-6505","authenticated-orcid":false,"given":"Dunja","family":"Mladeni\u0107","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Lab, Jozef Stefan Institute, 1000 Ljubljana, Slovenia"},{"name":"Jozef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3073","DOI":"10.1007\/s11227-015-1501-1","article-title":"A general perspective of Big Data: Applications, tools, challenges and trends","volume":"72","author":"Cervantes","year":"2016","journal-title":"J. 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