Declarative and Hybrid Process Discovery: Recent Advances and Open Challenges
Research output: Contribution to journal › Journal article › Research › peer-review
Knowledge-intensive processes, such as those encountered in health care, finance and government, tend to allow a large degree of flexibility: there are many possible solutions towards a goal, and it is left to the expertise of knowledge workers to find the one most suitable for the particular case at hand. As a result, such processes usually exhibit more varied behaviour than traditional production processes. This poses a challenge for process discovery algorithms that return imperative, flow-based, models. The models tend to become highly complex when representing many alternative paths, and therefore, the miners need to either sacrifice on simplicity, fitness, or precision. It has been proposed that one should discover the constraints of the process instead, based on the assumption that such a constraint-based, declarative process model can describe highly varied behaviour more concisely. More recently, it has been observed that many processes do not neatly fall in one category or the other; instead, they contain both flexible and rigid parts. In such cases, it may be helpful to identify these parts and mine constraints for some and flow for others, resulting in a hybrid model. In this paper, we provide an overview of recent advances in both declarative and hybrid process discovery, discuss a number of open challenges that still remain, and propose directions for future research.
|Journal||Journal on Data Semantics|
|Publication status||Published - 2020|
- Declarative models, Hybrid models, Process discovery