Approximate Aggregation
Approximate Aggregation is a data aggregation method that belongs to the class of estimation techniques designed to efficiently compute aggregate query results, distinguished by its deliberate trade-off of precision for reduced computational cost, memory usage, or response time through the use of statistical summaries, sampling, or sketching algorithms.
116
Publications
8.3K
Citations
246
Authors
122
Institutions
Publications per year
2017–2026
18
Parents
116
Approximate aggregation techniques for sensor databases
Jeffrey Considine, Fanrong Li, George Kollios et al. · 2004 · 564 citations
Nisheeth Shrivastava, Chiranjeeb Buragohain, Divyakant Agrawal et al. · 2004 · 487 citations
Processing complex aggregate queries over data streams
Alin Dobra, Minos Garofalakis, Johannes Gehrke et al. · 2002 · 324 citations
Dynamic sample selection for approximate query processing
Brian Babcock, Surajit Chaudhuri, Gautam Das · 2003 · 259 citations
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122
AT&T (United States)
Bedminster, United States
9 h-index
17 publications
743 citations

Harbin Institute of Technology
Harbin, China
8 h-index
14 publications
877 citations

Universidad de Alcalá
Alcalá de Henares, Spain
6 h-index
8 publications
164 citations
Georgia State University
Atlanta, United States
5 h-index
11 publications
880 citations
Microsoft (United States)
Redmond, United States
5 h-index
14 publications
1.8K citations
Tsinghua University
Beijing, China
4 h-index
9 publications
493 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
4 h-index
11 publications
1.3K citations

Cornell University
Ithaca, United States
4 h-index
11 publications
1.1K citations
1–8 of 122
PVLDB · 8 publications · 370 citations
7
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6 publications · 909 citations
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TKDE · 4 publications · 122 citations
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TODS · 3 publications · 322 citations
3
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2 publications · 141 citations
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