Presented By: Integrative Systems + Design
Predictive Analytics for Internet of Things (IoT) Enables Systems
Raed Al Kontar, Industrial & Operations Engineering, University of Michigan
Abstract
The Internet of things (IoT) enabled systems have become increasingly available in practice. Examples include GM’s OnStar® tele-service system, the InSite® telemonitoring system from GE, smart home appliances and various personalized remote structural or health monitoring systems. The unprecedented data availability in such connected systems provides significant opportunities for smart data analytics but, at the same time, it reveals critical challenges. First, the high dimensional stream data with heterogeneity, diverse data types and complex spatiotemporal structure often hinders establishing a unified analytics framework. Second, individual-level data has become available in large scale and consequently, there is a pressing need for individualized modeling and prediction.
In this talk, we try to address some of these challenges through predictive data analytics methodologies designed for IoT enabled systems. Specifically, we establish non-parametric models that predict the evolution of condition/system monitoring signals through borrowing strength from historical and in-service data. These frameworks leverage on kernel methods, functional component analysis and Bayesian inference. Further, we discuss how these methods can consistently scale to big data settings. The methodologies are validated using numerical studies and a case study with real world data in the application to cloud-based vehicle health monitoring service systems.
Bio
Raed Al Kontar is an Assistant Professor in the Department of Industrial & Operations Engineering at the University of Michigan and an affiliate with both the Michigan Institutes for Data science (MIDAS) and Computational Discovery and Engineering (MICDE). His research broadly focuses on developing data analytics and decision-making methodologies specifically tailored for Internet of Things (IoT) enabled products/systems.
Raed received his Ph.D. in Industrial and Systems Engineering in 2018 and M.S in Statistics from the University of Wisconsin Madison in 2017. He also received his B.S in Civil and Environmental Engineering with a minor in Mathematics from the American University of Beirut (AUB) in 2014. Some of his awards include: Best Paper Award Finalist from Quality, Statistics, and Reliability (QSR) Section of INFORMS 2018, Best Student Paper Award Winner from QSR Section of INFORMS 2017, E. Wayne Kay Graduate Scholarship from the Society of Manufacturing Engineers (SME), Gilbreth Memorial Fellowship from Institute of Industrial and Systems Engineers (IISE), Valedictorian and student speaker in the graduation commencement ceremony at AUB.
The Internet of things (IoT) enabled systems have become increasingly available in practice. Examples include GM’s OnStar® tele-service system, the InSite® telemonitoring system from GE, smart home appliances and various personalized remote structural or health monitoring systems. The unprecedented data availability in such connected systems provides significant opportunities for smart data analytics but, at the same time, it reveals critical challenges. First, the high dimensional stream data with heterogeneity, diverse data types and complex spatiotemporal structure often hinders establishing a unified analytics framework. Second, individual-level data has become available in large scale and consequently, there is a pressing need for individualized modeling and prediction.
In this talk, we try to address some of these challenges through predictive data analytics methodologies designed for IoT enabled systems. Specifically, we establish non-parametric models that predict the evolution of condition/system monitoring signals through borrowing strength from historical and in-service data. These frameworks leverage on kernel methods, functional component analysis and Bayesian inference. Further, we discuss how these methods can consistently scale to big data settings. The methodologies are validated using numerical studies and a case study with real world data in the application to cloud-based vehicle health monitoring service systems.
Bio
Raed Al Kontar is an Assistant Professor in the Department of Industrial & Operations Engineering at the University of Michigan and an affiliate with both the Michigan Institutes for Data science (MIDAS) and Computational Discovery and Engineering (MICDE). His research broadly focuses on developing data analytics and decision-making methodologies specifically tailored for Internet of Things (IoT) enabled products/systems.
Raed received his Ph.D. in Industrial and Systems Engineering in 2018 and M.S in Statistics from the University of Wisconsin Madison in 2017. He also received his B.S in Civil and Environmental Engineering with a minor in Mathematics from the American University of Beirut (AUB) in 2014. Some of his awards include: Best Paper Award Finalist from Quality, Statistics, and Reliability (QSR) Section of INFORMS 2018, Best Student Paper Award Winner from QSR Section of INFORMS 2017, E. Wayne Kay Graduate Scholarship from the Society of Manufacturing Engineers (SME), Gilbreth Memorial Fellowship from Institute of Industrial and Systems Engineers (IISE), Valedictorian and student speaker in the graduation commencement ceremony at AUB.
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