Demo Data from EDBT 2026 / reviewers in the wild / expert
Teck-Tim Tan
dblp:02/8012
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, 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 · 62% Web and social media mining · 38% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Web and social media mining
social network extraction |
0.1 | 1 | 2010 | STEvent: Spatio-temporal event model for social network discovery · ACM Trans. Inf. Syst. 2010 |
Data mining
spatiotemporal data mining |
0.1 | 1 | 2010 | STEvent: Spatio-temporal event model for social network discovery · ACM Trans. Inf. Syst. 2010 |
Data mining › pattern mining › association analysis
co-occurrence analysis |
0.0 | 1 | 2010 | STEvent: Spatio-temporal event model for social network discovery · ACM Trans. Inf. Syst. 2010 |
Data mining
pattern mining |
0.0 | 1 | 2010 | STEvent: Spatio-temporal event model for social network discovery · ACM Trans. Inf. Syst. 2010 |
Methods — techniques the papers use, named apart from their topics
event weighting · 0.1aggregation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | STEvent: Spatio-temporal event model for social network discoveryabstractSpatio-temporal data concerning the movement of individuals over space and time contains latent information on the associations among these individuals. Sources of spatio-temporal data include usage logs of mobile and Internet technologies. This article defines a spatio-temporal event by the co-occurrences among individuals that indicate potential associations among them. Each spatio-temporal event is assigned a weight based on the precision and uniqueness of the event. By aggregating the weights of events relating two individuals, we can determine the strength of association between them. We conduct extensive experimentation to investigate both the efficacy of the proposed model as well as the computational complexity of the proposed algorithms. Experimental results on three real-life spatio-temporal datasets cross-validate each other, lending greater confidence on the reliability of our proposed model. Hady Wirawan Lauw, Ee-Peng Lim, HweeHwa Pang, Teck-Tim Tan |
ACM Trans. Inf. Syst. | 4 |