Name
Title | ||
|---|---|---|
Aligning Real Process Executions and Prescriptive Process Models through Automated Planning. |
Abstract | ||
|---|---|---|
We propose a planning-based approach to compute optimal alignments.The approach has been implemented with a state-of-the-art planning system.We performed an in-depth evaluation on real-life & synthetic process models and logs.The approach outperforms existing techniques when the size/noise of the inputs grows. Modern organizations execute processes to deliver product and services, whose enactment needs to adhere to laws, regulations and standards. Conformance checking is the problem of pinpointing where deviations are observed. This paper shows how instances of the conformance checking problem can be represented as planning problems in PDDL (Planning Domain Definition Language) for which planners can find a correct solution in a finite amount of time. If conformance checking problems are converted into planning problems, one can seamlessly update to the recent versions of the best performing automated planners, with evident advantages in term of versatility and customization. The paper also reports on results of experiments conducted on two real-life case studies and on eight larger synthetic ones, mainly using the Fast-downward planner framework to solve the planning problems due to its performances. Some experiments were also repeated though other planners to concretely showcase the versatility of our approach. The results show that, when process models and event logs are of considerable size, our approach outperforms existing ones even by several orders of magnitude. Even more remarkably, when process models are extremely large and event log traces very long, the existing approaches are unable to terminate because they run out of memory, while our approach is able to properly complete the alignment task. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.eswa.2017.03.047 | Expert Syst. Appl. |
Keywords | Field | DocType |
Conformance checking,Automated planning,Business process management,Process mining | Business process management,Data mining,Computer science,Process modeling,Planner,Artificial intelligence,Conformance checking,Machine learning,Planning Domain Definition Language,Personalization,Process mining | Journal |
Volume | Issue | ISSN |
82 | C | 0957-4174 |
Citations | PageRank | References |
16 | 0.64 | 31 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Massimiliano de Leoni | 1 | 685 | 49.74 |
| Andrea Marrella | 2 | 273 | 35.71 |