Goal-Driven On-Line Imbalanced Streaming Data Preprocessing

For the upcoming era of Internet of things (IoT), plethora of data needs to be processed on a real-time basis. However, most prior data streaming related researches ignored the imbalanced data and its semantic meaning, thus often leading to unexpected or even unacceptable results. To address the above issue, this study proposed goal-driven on-line data preprocessing for the analytics of IoT-enabled streaming data. The proposed ontology-based approach can enhance the abilities of semantic understanding for real-time IoT applications. With the above enhancement, the experiment results show its potential in guaranteeing that the model does not go astray from the learning goal due to data imbalance.