Temporal Data Abstraction
Temporal Data Abstraction is a methodological framework that systematically reduces the complexity of time-varying datasets by identifying and representing higher-level temporal patterns, events, or states through the elimination of redundant or less significant temporal details, thereby enabling more effective analysis and visualization of dynamic phenomena.
106
Publications
8.1K
Citations
226
Authors
110
Institutions
Publications per year
2017–2026
17
Parents
106
Model-based Reinforcement Learning: A Survey
Thomas M. Moerland, Joost Broekens, Aske Plaat et al. · Foundations and Trends® in Machine Learning · 2023 · 485 citations · Full text
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Stanford University
Stanford, United States
12 h-index
17 publications
1.1K citations
Ben-Gurion University of the Negev
Beersheba, Israel
10 h-index
20 publications
1K citations
University of Pavia
Pavia, Italy
10 h-index
22 publications
1.1K citations

TU Wien
Vienna, Austria
4 h-index
11 publications
315 citations

Università degli Studi del Piemonte Orientale “Amedeo Avogadro”
Vercelli, Italy
4 h-index
12 publications
166 citations

University of Massachusetts Amherst
Amherst Center, United States
3 h-index
5 publications
3.3K citations

University of Rostock
Rostock, Germany
3 h-index
7 publications
671 citations
University of California, Berkeley
Berkeley, United States
3 h-index
6 publications
190 citations
1–8 of 110
Repository · 14 publications · 351 citations
11
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Repository · 7 publications · 171 citations
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AIM · 5 publications · 554 citations
5
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TVCG · 4 publications · 543 citations
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TKDE · 3 publications · 116 citations
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