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STEAM: A Platform for Scalable Spatiotemporal Analytics

Deva, Bersant; Raschke, Philip; Rodriguez Garzon, Sandro; Küpper, Axel

Spatiotemporal datasets have become increasingly available with the introduction of a various set of applications and services trac- ing the behavior of moving objects. Recently, there has been a high demand in understanding these datasets using spatiotemporal analytics. While being considered of high value, spatiotemporal analytics did not yet see a wide spreading into the actual business workflow or the direct configuration of services and applications. The computational complexity for spatiotemporal datasets and the heterogeneity of data sources are considered key factors for the current state. This paper introduces STEAM, a platform for distributed spatiotemporal analytics on heterogeneous spatiotemporal datasets. STEAM introduces a framework that abstracts the key components from incoming spatiotemporal datasets that originate from various positioning systems. This abstraction provides a common base for distributed and scalable analytics methods that is not bound to a specific underlying positioning technique. STEAM provides a distributed state-of-the-art implementation and is evaluated on a multi-machine testbed for linear scalability.
Published in: Procedia Computer Science, 10.1016/j.procs.2017.05.429, Elsevier