Cost-aware Search
Cost-Aware Search is a federated search strategy that optimizes query processing by explicitly accounting for resource expenditures—such as communication, computation, and latency costs—when determining which data sources to access and how to aggregate results across distributed repositories.
107
Publications
3.9K
Citations
305
Authors
154
Institutions
Publications per year
2017–2026
34
Parents
107
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions
Alvin Wan, Xiaoliang Dai, Peizhao Zhang et al. · 2020 · 299 citations
Challenges in building large-scale information retrieval systems
2009 · 192 citations
1–5 of 107
305
3
h-index
3
h-index
3
h-index
3
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3
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1–5 of 305
154
University of California, Berkeley
Berkeley, United States
4 h-index
6 publications
1K citations
Stanford University
Stanford, United States
4 h-index
6 publications
209 citations

National Taiwan University
Taipei, Taiwan
3 h-index
8 publications
278 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
7 publications
203 citations
Nagoya University
Nagoya, Japan
3 h-index
7 publications
135 citations
Georgetown University
Washington, United States
3 h-index
6 publications
72 citations

Centre National de la Recherche Scientifique
Paris, France
3 h-index
5 publications
80 citations

UNSW Sydney
Sydney, Australia
3 h-index
9 publications
175 citations
1–8 of 154
Repository · 6 publications · 283 citations
6
h-index
PVLDB · 4 publications · 58 citations
4
h-index
3 publications · 73 citations
3
h-index
2 publications · 35 citations
2
h-index
2 publications · 30 citations
2
h-index
1–5