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Moreover, configuring and managing context-aware stream processing queries in semantic IoT platforms requires much manual, labor-intensive effort. Generic queries can deal with context changes but often lead to performance issues caused by the need for expressive real-time semantic reasoning. In addition, query window parameters are part of the manual configuration and cannot be made context-dependent. To tackle these problems, this paper presents DIVIDE, a component for a semantic IoT platform that adaptively derives and manages the queries of the platform\u2019s stream processing components in a context-aware and scalable manner, and that enables privacy by design. By performing semantic reasoning to derive the queries when context changes are observed, their real-time evaluation does require any reasoning. The results of an evaluation on a homecare monitoring use case demonstrate how activity detection queries derived with DIVIDE can be evaluated in on average less than 3.7 seconds and can therefore successfully run on low-end IoT devices.<\/jats:p>","DOI":"10.3233\/sw-223281","type":"journal-article","created":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T11:43:24Z","timestamp":1680867804000},"page":"893-941","source":"Crossref","is-referenced-by-count":6,"title":["Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design"],"prefix":"10.1177","volume":"14","author":[{"given":"Mathias","family":"De\u00a0Brouwer","sequence":"first","affiliation":[{"name":"IDLab, Ghent University\u00a0\u2013 imec, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bram","family":"Steenwinckel","sequence":"additional","affiliation":[{"name":"IDLab, Ghent University\u00a0\u2013 imec, 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