Engineering
Flowable Open Source release brings enhanced audit and query capabilities
The latest release of the Flowable Open Source engines brings a range of major enhancements that ripple through the Flowable Product range. There’s been particular focus on additional audit capabilities as well as major improvements to engine queries.
This release builds on work that was started in the previous release back in October, which laid some of the technical groundwork necessary for the innovations we have been planning for a while. One of the most significant being the process migration service. With the new release, we can perform process migrations that make Flowable a leader in its class. Migrate instances with call activities, nested subprocesses and multi-instance activities among other complex but common BPMN constructs.
A new data source allows all the historic data about active processes and cases to be retrieved without having to go to the full historic data source (which may contain millions of completed process instances). This means you no longer have to compromise on how you store historic data, or worry about pruning history to keep performace as sharp as possible. There’s another new data source that provides effiecient querying of nested case and process hierarchies. Of course, knowing exactly what’s been done as part of a case or process execution is critical for understanding or validating business performance. New audit data recorded includes full details of sequence flows and user / human tasks changes.
There’s a swathe of improvements to the CMMN engine that bring it to the same expressive power and performance as the BPMN engine. As well as adding our unique dynamic instance manipulation, there’s enhancements driven by the real-world scenarios our customers are facing.
Finally, there’s wider support for our existing muti-tenancy capabilities, including cross-tenant models, and imporved tenant-based queries for a range of information from all the engines.
Find the Open Source release on GitHub.
In the past few months, this has culminated into a clear understanding of the strengths and weaknesses of the Generative AI (GenAI) technology; and where it makes sense to integrate with it and – perhaps more important – where it doesn’t make sense.
As AI gains prominence as a pivotal technology and enterprises increasingly seek to leverage its capabilities, we are actively exploring diverse avenues for integrating AI into process automation.
The key to managing complexity is to combine different and multiple tools leads to better, faster, and more maintainable solutions. For example, combining BPMN with CMMN.