Relational stream mining involves learning a model on relational entities, which are enriched with information from further streams that reference them. To incorporate such information into the entities in an efficient incremental way, we propose a multi-threaded framework with a weighting function that prioritizes the entities delivered to the learner for learning and adaption to drift. We further propose a generator for drifting relational streams, and use it to show that our framework reaches substantial reduction of computation time. © Springer-Verlag 2013.
CITATION STYLE
Matuszyk, P., & Spiliopoulou, M. (2013). Framework for storing and processing relational entities in stream mining. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7819 LNAI, pp. 497–508). https://doi.org/10.1007/978-3-642-37456-2_42
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