Title:  Process-aware pattern recognition and anomaly detection under uncertainty
Speakers: Jiawei Zheng
Abstract:

In today’s increasingly digital world, recognising patterns and detecting anomalies have become increasingly crucial for ensuring efficient operations, enhancing productivity and promoting human healthy. Processes often embody contextual information, such as domain-specific knowledge, optimal or expected behaviour, or established best practices. By considering the process information from context, we can interpret patterns more accurately and enhance the performance of anomaly detection. However, the structure of underlying processes in different domains varies greatly. Due to this, the patterns and anomalies we are interested in have different focuses and come from different perspectives, and thus pose different challenges in different domains.

In this talk, I will present the problems associated with incorporating process knowledge for pattern recognition and anomaly detection over uncertain data in heterogenous domains, the current progress that I have achieved, and finally the future plans to finish the project.