Database Paper Browser

Back to papers

Conditioning and Aggregating Uncertain Data Streams: Going Beyond Expectations

Summary: Conditioning and aggregating uncertain data streams; introduces a framework with a unified model, metrics, and bounded-error representations. Delivers fast deterministic and randomized streaming algorithms to approximate conditioned aggregate distributions. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10064
Venue
VLDB
Year
2010
Pagerank
4.1945683e-05
Overall Rank
12,272 | 14.63%
DOI
-

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
7,623 Optimizing Probabilistic Query Processing on Continuous Uncertain Data 2011 VLDB 4.6933659e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
101 ULDBs: Databases with Uncertainty and Lineage 2006 VLDB 0.0004955674
321 MCDB: A Monte Carlo Approach to Managing Uncertain Data 2008 SIGMOD 0.00027527389
477 Model-Driven Data Acquisition in Sensor Networks 2004 VLDB 0.00022221803
3,041 Sketching Probabilistic Data Streams 2007 SIGMOD 7.6697078e-05
3,385 Estimating Statistical Aggregates on Probabilistic Data Streams 2007 PODS 7.1580391e-05
5,764 PODS: A New Model and Processing Algorithms for Uncertain Data Streams 2010 SIGMOD 5.3369195e-05
Previous Page 1 / 1 Next

Semantically Similar Papers