MacroBase: Analytic Monitoring for the Internet of Things
Peter D. Bailis, Deepak Narayanan, Samuel R. Madden · arXiv (Cornell University) · 2016
An increasing proportion of data today is generated by automated processes, sensors, and devices---collectively, the Internet of Things (IoT). IoT applications' rising data volume, demands for time-sensitive analysis, and heterogeneity exacerbate the challenge of identifying and highlighting important trends in IoT deployments. In response, we present MacroBase, a data analytics engine that performs statistically-informed analytic monitoring of IoT data streams by identifying deviations within streams and generating potential explanations for each. MacroBase is the first analytics engine to combine streaming outlier detection and streaming explanation operators, allowing cross-layer optimizations that deliver order-of-magnitude speedups over existing, primarily non-streaming alternatives. As a result, MacroBase can deliver accurate results at speeds of up to 2M events per second per query on a single core. MacroBase has delivered meaningful analytic monitoring results in production, including an IoT company monitoring hundreds of thousands of vehicles.