Invited Tutorial

Professor Avigdor Gal | Technion – Israel Institute of Technology

Professor Matthias Weidlich | Department of Computer Science – Humboldt Universität zu Berlin


Online Temporal Analysis of Complex Systems Using IoT Data Sensing


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.

Bio Avigdor Gal:

Avigdor Gal is a Professor at the Technion – Israel Institute of Technology. His research is focused on developing novel models and algorithms for data integration in general, and in particular for schema matching, entity resolution, and process matching. He has published more than 150 papers in leading professional journals and authored the 2011 book Uncertain Schema Matching. He serves in various editorial capacities for periodicals and helped organize professional workshops and conferences nearly every year since 1998. He has won the IBM Faculty Award each year from 2002 to 2004 and is the recipient of the 2019 JPMorgan AI Faculty Award. He is also the Academic Co-Director of Data Science & Engineering for the Technion undergraduate program and the head of the Big Data Integration laboratory.

Bio Matthias Weidlich:

Matthias Weidlich is a full professor at the Department of Computer Science at Humboldt-Universität zu Berlin (HU), Germany. Earlier, he held positions at Imperial College London, UK, and the Technion, Israel. He holds a PhD in Computer Science from the Hasso-Plattner-Institute, University of Potsdam, Germany. His research focuses on process-oriented and event-driven information systems, including methods for their specification, their data-driven analysis, and optimisations of their run-time behaviour. He is a Junior-Fellow of the German Informatics Society and recipient of the Berlin Young Researcher Award 2016.