RSM4CPS 2018 - 1st International Workshop on Real-Time Stream Analytics and Machine Learning for Cyber-Physical Systems

The 1st International Workshop on Real-Time Stream Analytics and Machine Learning for Cyber-Physical Systems (RSM4CPS-2018) will be held at the Campus of IIT BHU Varanasi in conjunction with 19th International Conference on Distributed Computing and Networking (ICDCN 2018) on January 4th 2018.
Cyber Physical Systems (CPSs) are built around various services and applications which are deployed on the top of sensors and actuators, communication topolo­gies and variety of computing platforms. Connection among these components creates multiple end-to-end task chains which need to work under resource constrained environments. CPSs have ability to adapt and to learn. CPSs continuously analyze their environment. They learn patterns and correlations among various instances/events based on their observations about the environment. Identifying these correlations and patterns, they tend to build significant predictive models. Applications of CPSs are in multiple domains ranging from condition monitoring and maintenance to image processing and diagnosis. In CPSs, the tight coupling between physical and computation processes has resulted in production of large volumes of data. Machine Learning (ML) techniques can significantly collaborate with CPSs to carry out monitoring and control to minimize the latency between control message reception and decision making. Traditionally used model-based ML techniques with CPSs design are unable to handle and model the complexity of extremely larger and dynamic CPSs which is the demand of current era so that they may respond in real time as well as adapt to the changing environment. Thus, current CPSs capture, analyze and learn from infinitely coming streaming data for real time decision making. Emerging distributed frameworks, computing architectures, models and algorithms that leverage infinite stream computing fit well in fulfilling above mentioned requirements. Moreover, multiple open sourced technologies in this domain may help to design even complex CPSs in a robust and cost-effective manner.
This workshop will help in capturing inherent challenges and opportunities associated with real-time stream analytics and machine learning with respect to CPS. Focus of the workshop is in exploring the state of art technologies involved in real-time analytics of infinite scale streaming data in context of CPSs. Multi-dimensional aspects of distributed real-time analytics and learning for wide range of applications such as healthcare, transportation, energy sector, and other domains will be evaluated in order to extract pros and cons along with futuristic advancements that is in progress or may be incorporated to enable large scale real-time and adaptive stream analytics and learning in CPSs. Overall goal of the workshop is to provide an inter-disciplinary open forum for academia and industry to exchange the knowledge and research experience on recent advancements in field of Cyber Physical System design equipped with real-time stream analytics and learning in a robust, reliable and cost-effective manner.
Topics of interests include but are not limited to the following areas related to CPSs
Distributed data ingestion frameworks and tools
Distributed data storage architectures
Distributed data integration models and techniques
Semantic stream processing in dynamic environments
Distributed stream computing architectures and frameworks
Stream learning algorithms
Spatio-Temporal stream modeling techniques
Self-adaptive modeling for CPSs
Programming models for real time Cyber Physical Systems
Evolving algorithms for real time CPSs
Real time analytics in healthcare, aeronautics, transportation, energy, medical and other Cyber Physical Systems
Real time machine learning and Societal Cyber Physical Systems
IoT and Geo-Distributed real time analytics
Cloud based real time analytics for CPS
Stability, Safety, Reliability in real time Cyber Physical Systems
Security and Privacy modeling in CPS
Security risk analysis, evaluation and management