A Bayesian Method for Guessing the Extreme Values in a Data Set

Summary: Statistically rigorous Bayesian method to infer global extrema (min/max) from samples. Demonstrates applicability to min/max online aggregation, top-k processing, outlier detection, and distance joins, with two concrete data-management problems used as demonstrations. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 9601
Venue: VLDB
Year: 2007
Pagerank: 4.4278388e-05
Overall Rank: 8,868 | 38.37%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Mingxi Wu
2. Christopher Jermaine

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,808	Turbo-Charging Estimate Convergence in DBO	2009	VLDB	6.7416988e-05
9,790	Distance-Based Outlier Detection: Consolidation and Renewed Bearing	2010	VLDB	4.2782534e-05

Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
14	Online Aggregation	1997	SIGMOD	0.0010813443
147	Efficient Processing of Spatial Joins Using R-trees	1993	SIGMOD	0.00041201518
216	Ripple Joins for Online Aggregation	1999	SIGMOD	0.00033560137
697	Efficient Algorithms for Mining Outliers from Large Data Sets	2000	SIGMOD	0.00017964755
1,174	Spatial Hash-Joins	1996	SIGMOD	0.00013490827
1,605	Incremental Distance Join Algorithms for Spatial Databases	1998	SIGMOD	0.00011180126
2,119	Scalable Sweeping-Based Spatial Join	1998	VLDB	9.5036401e-05
2,552	Probabilistic Optimization of Top N Queries	1999	VLDB	8.5540341e-05
6,496	Adaptive Multi-Stage Distance Join Processing	2000	SIGMOD	5.0327729e-05

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