Demo Data from EDBT 2026 / reviewers in the wild / expert
Timo Soininen
dblp:48/6773
· DBLP profile ↗
6ranked-venue papers
1as first author
0since 2021 · last 2007
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 5 · 1 first-authorSoftware engineering, systems software and programming languages · 2 · 1 first-authorDatabases, data management, data science and information retrieval · 2Theory of computation · 1Applied, interdisciplinary, general and emerging computing · 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.
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
logic-based reasoning |
0.0 | 1 | 2002 | Extending and implementing the stable model semantics · Artif. Intell. 2002 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › logic programming
stable model semantics |
0.0 | 1 | 2002 | Extending and implementing the stable model semantics · Artif. Intell. 2002 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | Kumbang: A domain ontology for modelling variability in software product families
Timo Asikainen, Tomi Männistö, Timo Soininen |
Adv. Eng. Informatics | 3 |
| 2006 | A Unified Conceptual Foundation for Feature ModellingabstractFeature modelling has become perhaps the most popular method for representing variabilities and commonalities in software product families. A large number of feature modelling methods and supporting tools have been reported. The conceptual foundation of feature models remains vague, a fact that severely undermines the usability of feature models. Therefore, we introduce Forfamel, a rigorous conceptual foundation for feature modelling. Forfamel synthesises existing feature modelling methods in the sense that it covers most concepts and constructs found in existing feature modelling methods. In addition, Forfamel includes a few additional constructs that may prove to be useful. We show the novel contribution of Forfamel by reflecting it against previous feature modelling methods and arguing for its underlying design decisions Timo Asikainen, Tomi Männistö, Timo Soininen |
SPLC | 3 |
| 2002 | Extending and implementing the stable model semantics
Patrik Simons, Ilkka Niemelä, Timo Soininen |
Artif. Intell. | 3 |
| 2001 | Multiple abstraction levels in modelling product structures
Tomi Männistö, Hannu Peltonen, Timo Soininen, Reijo Sulonen |
Data Knowl. Eng. | 3 |
| 1999 | A Fixpoint Definition of Dynamic Constraint Satisfaction
Timo Soininen, Esther M. Gelle, Ilkka Niemelä |
CP | 1 |
| 1999 | Stable Model Semantics of Weight Constraint Rules
Ilkka Niemelä, Patrik Simons, Timo Soininen |
LPNMR | 3 |