Incremental Filtering
Incremental Filtering is a dynamic filtering methodology that progressively refines results by incorporating newly arriving data in a sequential manner, distinguishing itself from batch or static filtering approaches through its iterative update mechanism.
31
Publications
1.2K
Citations
96
Authors
60
Institutions
Publications per year
2017–2026
4
Parents
31
1–5 of 31
96
2
h-index
2
h-index
National Institute of Standards and Technology, United States
2 publications · 33 citations
2
h-index
2
h-index
2
h-index
1–5 of 96
60
National Institute of Standards and Technology
Gaithersburg, United States
2 h-index
4 publications
66 citations

Chongqing University
Chongqing, China
2 h-index
6 publications
753 citations
University of California, Berkeley
Berkeley, United States
2 h-index
2 publications
179 citations
Massachusetts Institute of Technology
Cambridge, United States
2 h-index
5 publications
85 citations

University of Minnesota
Minneapolis, United States
2 h-index
2 publications
179 citations

Chinese Academy of Sciences
Beijing, China
2 h-index
8 publications
196 citations
Institute of Automation
Beijing, China
2 h-index
8 publications
196 citations
Hunan University of Science and Engineering
Hunan, China
1 h-index
1 publications
12 citations
1–8 of 60
2 publications · 110 citations
2
h-index
2 publications · 251 citations
2
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Conference · 2 publications · 33 citations
2
h-index
1 publications · 18 citations
1
h-index
1 publications · 55 citations
1
h-index
1–5