Demo Data from EDBT 2026 / reviewers in the wild / expert
Arthur G. Telea
dblp:137/2144
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 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.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational finance and economics · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
visual analytics |
0.2 | 1 | 2013 | Decision Exploration Lab: A Visual Analytics Solution for Decision Management · IEEE Trans. Vis. Comput. Graph. 2013 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
ontology |
0.0 | 1 | 2013 | Decision Exploration Lab: A Visual Analytics Solution for Decision Management · IEEE Trans. Vis. Comput. Graph. 2013 |
Computational finance and economics
insurance |
0.0 | 1 | 2013 | Decision Exploration Lab: A Visual Analytics Solution for Decision Management · IEEE Trans. Vis. Comput. Graph. 2013 |
Methods — techniques the papers use, named apart from their topics
production rules · 0.5ontology-based modeling · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Decision Exploration Lab: A Visual Analytics Solution for Decision ManagementabstractWe present a visual analytics solution designed to address prevalent issues in the area of Operational Decision Management (ODM). In ODM, which has its roots in Artificial Intelligence (Expert Systems) and Management Science, it is increasingly important to align business decisions with business goals. In our work, we consider decision models (executable models of the business domain) as ontologies that describe the business domain, and production rules that describe the business logic of decisions to be made over this ontology. Executing a decision model produces an accumulation of decisions made over time for individual cases. We are interested, first, to get insight in the decision logic and the accumulated facts by themselves. Secondly and more importantly, we want to see how the accumulated facts reveal potential divergences between the reality as captured by the decision model, and the reality as captured by the executed decisions. We illustrate the motivation, added value for visual analytics, and our proposed solution and tooling through a business case from the car insurance industry. Bertjan Broeksema, Thomas Baudel, Arthur G. Telea, Paolo Crisafulli |
IEEE Trans. Vis. Comput. Graph. | 3 |