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Arthur G. Telea

dblp:137/2144 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
visual analytics
0.212013
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
0.012013
Decision Exploration Lab: A Visual Analytics Solution for Decision Management · IEEE Trans. Vis. Comput. Graph. 2013
Computational finance and economics
insurance
0.012013
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
YearPublicationVenuePosition
2013 Decision Exploration Lab: A Visual Analytics Solution for Decision Management
abstract
We 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