SIGBPS AOI: Process Mining Analytics

Track Chairs:

Xin Li (City Univerisy of Hong Kong)
G. Alan Wang (Virginia Tech)

Track Description:

Process mining and analytics, one of the emerging topics in Business Process Management (BPM), aims to help decision makers monitor, analyze, and visualize the execution of business process models based on process information extracted from information systems.

Studies in this area include but not limited to: 
1) Process discovery (e.g., extracting process models from event logs); 
2) Frequent and essential process pattern mining from process instances; 
3) Conformance checking and deviation detection for process models;
4) Real-time monitoring and visualization of process execution; 
5) Process model validation and optimization; 
6) Social/organizational network mining in business processes; 
7) Process mining for knowledge management (e.g., information dissemination, knowledge transfer, e-learning). 
Process mining and analytics techniques can be applied to a wide range of applications, such as financial service, healthcare, human resource management, mobile applications, e-commerce, cloud computing, workflow management, and so forth. Theoretical research on the organization, culture, policy, architecture, computational complexity, and infrastructure issues in process mining and analytics will also be considered.

The goal of this group is to promote research and understanding of process mining and analytics. We welcome research that facilitates the synergy between BPM and automated analytic techniques including, but not limited to, data/text mining, network analysis, simulation, and visual analytics. It is our hope that academic researchers and industry practitioners could both benefit from this interest group.