Intelligent Process Monitoring
Monitoring is important for process analysis which in turn is essential for process improvement. When processes are modelled using a domain-specific approach as described here (link to the modelling page), it is natural to expect monitoring information to match the domain concepts and abstractions.
We are exploring ways to integrate low level monitoring data from execution environments spanning the BPM and SOA spaces. By lifting lower-level technical events to domain-specific concept probes, it is possible to manipulate execution information at a level of abstraction that makes sense to business stakeholders. This also enables a fine-grained analysis of root causes of certain issues (such as slow partner services, network issues or underperforming resources in general). In addition, it is possible to use such data in reports and analyses in a way that naturally matches the key performance indicators and the process improvement metrics for the target business domains.
A related axis of process monitoring focuses on human-executed business tasks. In organizations with large numbers of employees involved in a variety of business processes it can be useful to understand the context of performance data when it comes to human tasks. This is why when monitoring such tasks, specific contextual data needs to be collected and correlated. For instance, the workload of a particular employee at a given time may determine their performance on tasks that need to be finished on the same day. By correlating additional data such as the role of the person, their duties with respect to the business domain and similarly qualified people that may be available, better planning may be put in place automatically by the process execution environment.
- Paper accepted at the International Conference on Software & System Engineering, May 2015: Human Task Monitoring and Contextual Analysis for Domain Specific Business Processes
- Paper accepted at the International Conference on Service Oriented Computing, Nov 2014: Domain specific Monitoring of Business Processes Using Concept Probes