Exploiting IoT Data Crossings for Gradual Pattern Mining Through Parallel Processing

Today, with the proliferation of Internet of Things (IoT) applications in almost every area of our society comes the trouble of deducing relevant information from real-time time-series data (from different sources) for decision making. In this paper, we propose a fuzzy temporal approach for crossing such data sets with the ultimate goal of exploiting them for temporal gradual pattern mining. A temporal gradual pattern may take the form: “the higher the humidity, the lower the temperature, almost 15 min later” . In addition, we apply parallel processing on our implementation and measure its computational performance.