Query-Driven Descriptive Analytics for IoT and Edge Computing
Moysis Symeonides, Demetris Trihinas, Zacharias Georgiou, George Pallis, Marios D. Dikaiakos · 2019
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 paper introduces 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, the declarative query model enables users to express high-level and descriptive analytic insights, while our framework compiles, optimizes and executes the query plan decoupled from the programming model of the underlying data processing engine. Afterwards, we showcase a number of potential use-cases which stand to benefit from the realization of query-driven descriptive analytics for edge computing. We conclude by elaborating on the open challenges that still must be addressed to realize our vision and potential research opportunities for the academic community to further advance the current State-of-the-Art.