Online Temporal Analysis of Complex Systems Using IoT Data Sensing

Avigdor Gal, Arik Senderovich, Matthias Weidlich

Abstract: Temporal analysis for online monitoring and improvement of complex systems such as hospitals, public transportation networks, or supply chains has been in the focus of several areas in operations management. These include queueing theory for bottleneck analysis, mathematical scheduling for resource assignments to customers, and inventory management for ordering products under uncertain demand. In recent years, with the increasing availability of data sensed by Internet-of-Things (IoT) infrastructures, these online temporal analyses drift towards automated and data-driven solutions. In this tutorial, we cover existing approaches to answer online temporal queries based on sensed data. We discuss two complementary angles, namely operations management and machine learning. The operational approach is driven by models, while machine learning methods are grounded in feature encoding. Both techniques require methods for translating low-level data readings coming from sensors into high-level activities with their temporal relations. Further, some of the techniques consider only dependencies of the sensed entities on their own individual histories, while others take into account dependencies between entities that share system resources. We outline the state-of-the-art in temporal querying, with demonstrations of interesting phenomena and main results using a real-world case study in the healthcare domain. Finally, we chart the territory of online data analytics for complex systems in a broader context and provide future research directions.