Interactive Demonstration of Probabilistic Predicates

Summary: Prototype engine uses probabilistic predicates (PPs) to push UDF-based ML inference earlier, pruning inputs before heavy work. PPs on raw inputs cover image, doc, video tasks; optimizations extend pushdown to complex predicates with tunable accuracy. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5504
Venue: SIGMOD
Year: 2018
Pagerank: 4.996343e-05
Overall Rank: 6,590 | 54.20%
DOI: 10.1145/3183713.3183751

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,078	Visual Road: A Video Data Management Benchmark	2019	SIGMOD	6.4633815e-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
22	SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets	2008	VLDB	0.00084679526
66	Spark SQL: Relational Data Processing in Spark	2015	SIGMOD	0.00061707583
70	Hive - A Warehousing Solution Over a Map-Reduce Framework	2009	VLDB	0.00059744625
140	Predicate Migration: Optimizing Queries with Expensive Predicates	1993	SIGMOD	0.00042289025
317	NoScope: Optimizing Neural Network Queries over Video at Scale	2017	VLDB	0.0002798145
332	Accelerating Machine Learning Inference with Probabilistic Predicates	2018	SIGMOD	0.00027173479
1,303	Query Optimization by Predicate Move-Around	1994	VLDB	0.00012692678
1,774	Optimizing Disjunctive Queries with Expensive Predicates	1994	SIGMOD	0.0001059836
4,022	Exploiting Correlations for Expensive Predicate Evaluation	2015	SIGMOD	6.5185189e-05

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