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Demo Data from EDBT 2026 / reviewers in the wild / expert

Jeanne M. VanBriesen

dblp:65/2208 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 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.

Databases, data mining, and information retrieval
1 paper
Data mining · 67% Web and social media mining · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › structured data mining
graph mining		 0.112007
Cost-effective outbreak detection in networks · KDD 2007
Web and social media mining › social network analysis
influence maximization		 0.112007
Cost-effective outbreak detection in networks · KDD 2007
Data mining › anomaly detection › temporal anomaly detection
outbreak detection		 0.112007
Cost-effective outbreak detection in networks · KDD 2007

Methods — techniques the papers use, named apart from their topics

sensor placement optimization · 0.1
YearPublicationVenuePosition
2007 Cost-effective outbreak detection in networks
abstract
Given a water distribution network, where should we place sensors toquickly detect contaminants? Or, which blogs should we read to avoid missing important stories?.
Jure Leskovec, Andreas Krause 0001, Carlos Guestrin, Christos Faloutsos, Jeanne M. VanBriesen, Natalie S. Glance
 KDD5