Program

The program of SA4PM workshop will take place virtually using the WHOVA virtual event platform that is provided by the ICPM conference. You will receive further information from the ICPM organizers on how to access the session.

• 12:00-13:00: Keynote Talk: Adaptive Machine Learning for Data Streams by Albert Bifet
• 13:00-13:30: Break
• 13:30-14:00: Online Anomaly Detection Using Statistical Leverage for Streaming Business Process Events by Jonghyeon Ko and Marco Comuzzi
• 14:00-14:30: Concept Drift Detection on Streaming Data with Dynamic Outlier Aggregation by Ludwig Zellner, Florian Richter, Janina Sontheim, Andrea Maldonado, and Thomas Seidl
• 14:30-14:45: Break
• 14:45-15:15: OTOSO: Online Trace Ordering for Structural Overviews by Florian Richter, Andrea Maldonado, Ludwig Zellner, and Thomas Seidl
• 15:15-15:45: Performance Skyline: Inferring Process Performance Models from Interval Events by Andrea Maldonado, Janina Sontheim, Florian Richter, and Thomas Seidl

We are excited to announce that Albert Bifet will be the Keynote Speaker of SA4PM'20. This is the abstract of his keynote:

Abstract of: Adaptive Machine Learning for Data Streams

Big Data and the Internet of Things (IoT) have the potential to fundamentally shift the way we interact with our surroundings. The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in stream mining. In this talk, I will present an overview of data stream mining, and I will introduce some popular open source tools for data stream mining.

Albert Bifet is Professor at University of Waikato, and Institut Polytechnique de Paris. Previously he worked at Huawei Noah’s Ark Lab in Hong Kong, Yahoo Labs in Barcelona, and UPC BarcelonaTech. He is the co-author of a book on Machine Learning from Data Streams published at MIT Press. He is one of the leaders of MOA, scikit-multiflow and Apache SAMOA software environments for implementing algorithms and running experiments for online learning from evolving data streams. He was serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of KDD BigMine (2012-2019), and ACM SAC Data Streams Track (2012-2019).