VLDB 2026 Research / reviewers in the wild / expert
Willem Mertens
dblp:34/9191
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
0000-0002-1635-3041ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Software testing · 100% | |
| Theoretical computer science
1 paper |
Logic in computer science · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing › protocol testing
conformance checking |
0.3 | 1 | 2018 | Complete and Interpretable Conformance Checking of Business Processes · IEEE Trans. Software Eng. 2018 |
Logic in computer science › concurrency theory
concurrency models |
0.1 | 1 | 2018 | Complete and Interpretable Conformance Checking of Business Processes · IEEE Trans. Software Eng. 2018 |
Logic in computer science › concurrency theory
event structures |
0.1 | 1 | 2018 | Complete and Interpretable Conformance Checking of Business Processes · IEEE Trans. Software Eng. 2018 |
Methods — techniques the papers use, named apart from their topics
natural language verbalization · 0.7error-correcting synchronized product · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Complete and Interpretable Conformance Checking of Business ProcessesabstractThis article presents a method for checking the conformance between an event log capturing the actual execution of a business process, and a model capturing its expected or normative execution. Given a process model and an event log, the method returns a set of statements in natural language describing the behavior allowed by the model but not observed in the log and vice versa. The method relies on a unified representation of process models and event logs based on a well-known model of concurrency, namely event structures. Specifically, the problem of conformance checking is approached by converting the event log into an event structure, converting the process model into another event structure, and aligning the two event structures via an error-correcting synchronized product. Each difference detected in the synchronized product is then verbalized as a natural language statement. An empirical evaluation shows that the proposed method can handle real datasets and produces more concise and higher-level difference descriptions than state-of-the-art conformance checking methods. In a survey designed according to the technology acceptance model, practitioners showed a preference towards the proposed method with respect to a state-of-the-art baseline. Luciano García-Bañuelos, Nick R. T. P. van Beest, Marlon Dumas, Marcello La Rosa, Willem Mertens |
IEEE Trans. Software Eng. | 5 |