George Pallis (University of Cyprus) QUERY-DRIVEN DESCRIPTIVE ANALYTICS FOR IOT AND EDGE COMPUTING
With consumers embracing the prevalence of ubiquitously connected smart devices, edge computing is emerging as a principal computing paradigm for latency-sensitive and in-proximity services. However, as the plethora of data generated across connected devices continues to vastly increase, the need to query the “edge” and derive in-time analytic insights is more evident than ever. This talk presents our vision for a rich and declarative query model abstraction particularly tailored for the unique characteristics of edge computing and presents a prototype framework that realizes our vision. Towards this, we present StreamSight, a framework for edge-enabled IoT services which provides a rich and declarative query model abstraction for expressing complex analytics on monitoring data streams and then dynamically compiling these queries into stream processing jobs for continuous execution on distributed processing engines. To overcome the resource restrictive barriers in edge computing deployments, StreamSight outputs the query execution plan so that intermediate results are reused and not continuously recomputed. In turn, StreamSight enables users to express various optimization strategies (e.g., approximate answers, query prioritization) and constraints (e.g., sample size, error-bounds) so that delay-sensitive requirements relevant to the deployment are not violated. We evaluate our framework on Apache Spark with real-world workloads and show that leveraging StreamSight can significantly increase performance by at least 4× while still satisfying all accuracy guarantees. We conclude by presenting a number of potential use-cases which stand to benefit from the realization of query-driven descriptive analytics for edge computing.
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