Big Data, Multivariate, Window based Feature Engineering using Microsoft R Server

IoT data is characterized by long time signals recorded from multiple sensors at the same time. Such sensors are utilized to monitor intricate systems and machine learning can be used to understand complex patterns of sensor data (called features) and their association with events of interest like failures or abnormal system behaviors (prediction labels). Due to recordings long period of time and the rich and complex nature of IoT information, the important aspect that needs to be captured for prediction is the multivariate relationship between different sensors, rather than isolated sensors statistics. Multivariate time windows based feature engineering is a fundamental step for building advanced predictive modeling solutions for IoT systems, but is difficult to implement in Big Data cases by using standard row-focused Map/Reduce tools like Hive.  We discuss here how Microsoft R server (MRS) can be used to perform several such complex multivariate feature engineering me