VLDB 2026 Research / reviewers in the wild / expert
Karl Kegel
dblp:244/5326
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2026
0009-0003-6829-4260ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 1 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Enabling Consistent Recombination of Heterogeneous Artifacts in Reactive Consistency Restoration Mechanisms
Andreas Domanowski, Christoph Seidl 0001, Marie Clausnitzer, Karl Kegel, Uwe Aßmann |
MODELSWARD | 4 |
| 2023 | Towards Variability-Aware Instance Handling for Model Evolution at RuntimeabstractModel-driven software development addresses the growing need for individualized software and fast-changing requirements. To support timely changes to the software, adaptive object modeling realizes the domain model of an application as a runtime model represented by a changeable metamodel. Users can change both runtime- and metamodel. This leads, in principle, to a user-driven eternal system at runtime. However, model evolution is non-trivial as it introduces the co-evolution problem, i.e., what happens to the instances of an evolved model? Various approaches to address this problem exist. These approaches have weaknesses, leading to a system that ages with each evolution step. This work introduces a novel approach to cope a priori with the evolution of entity models at runtime. The Eternal Subspace Instance (ESI) approach bypasses the co-evolution problem by defining model instances as a "rich" composite structure capturing not just the current state of an instance. ESI are generated from a model extended with metadata specifying variants and versions. This information is used to interpret each ESI individually, independent of its variant and version. This work defines ESIs and proposes a framework for the resulting heterogeneous but variability-aware instances. We evaluate our approach with a publicly available reference implementation called modicio applied to an example scenario. Karl Kegel, Sebastian Götz |
SEAA | 1 |