Framework for Storing and Processing Relational Entities in Stream Mining
P. Matuszyk, and M. Spiliopoulou. Advances in Knowledge Discovery and Data Mining
, volume 7819 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2013)
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.