Accelerating Machine Learning Inference with Probabilistic Predicates

Summary: Proposes probabilistic predicates to prune data blobs before expensive UDF feature extraction, with accuracy-aware filtering. Augments a cost-based optimizer to select plans from simple probabilistic predicates, enabling up to 10x ML inference speedups. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5533
Venue: SIGMOD
Year: 2018
Pagerank: 0.00022238183
Overall Rank: 295 | 97.98%
DOI: 10.1145/3183713.3183751

Incoming Non-self Citations Over Time

Authors

1. Yao Lu (Microsoft; University of Washington)
2. Aakanksha Chowdhery (Microsoft; Princeton University)
3. Srikanth Kandula (Microsoft)
4. Surajit Chaudhuri (Microsoft)

BibTeX Citation

@inproceedings{lu_sigmod18,
        title = {{Accelerating Machine Learning Inference with Probabilistic Predicates}},
        author = {Lu, Yao and Chowdhery, Aakanksha and Kandula, Srikanth and Chaudhuri, Surajit},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3183751},
        url = {https://dl.acm.org/doi/10.1145/3183713.3183751},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 10 of 60 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,230	Eliminating Redundant Feature Tests in Decision Tree and Random Forest Inference on SQL Predicates	2026	SIGMOD	5.093636e-05
10,286	ScaleDoc: Scaling LLM-based Predicates over Large Document Collections	2026	SIGMOD	5.093636e-05
10,476	On Efficient Approximate Aggregate Nearest Neighbor Queries over Learned Representations	2026	SIGMOD	5.093636e-05
10,504	Task Cascades for Efficient Unstructured Data Processing	2026	SIGMOD	5.093636e-05
10,623	KEN: An Execution Engine for Unstructured Database Systems	2026	VLDB	5.093636e-05
10,795	Scalable Complex Event Processing on Video Streams	2025	SIGMOD	5.093636e-05
11,268	Optimizing Video Queries with Declarative Clues	2024	VLDB	5.093636e-05
11,472	PAINE Demo: Optimizing Video Selection Queries With Commonsense Knowledge	2023	VLDB	5.093636e-05
11,483	EQUI-VOCAL Demonstration: Synthesizing Video Queries from User Interactions	2023	VLDB	5.093636e-05
11,624	Accelerating Queries over Unstructured Data with ML	2021	CIDR	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
24	Spark SQL: Relational Data Processing in Spark	2015	SIGMOD	0.00054865648
30	SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets	2008	VLDB	0.00051174276
32	Hive - A Warehousing Solution Over a Map-Reduce Framework	2009	VLDB	0.00050111008
132	Predicate Migration: Optimizing Queries with Expensive Predicates	1993	SIGMOD	0.00030378624
284	NoScope: Optimizing Neural Network Queries over Video at Scale	2017	VLDB	0.00022370521
813	Adaptive Ordering of Pipelined Stream Filters	2004	SIGMOD	0.00013846487
1,124	Query Optimization by Predicate Move-Around	1994	VLDB	0.00012087356
1,826	Optimizing Disjunctive Queries with Expensive Predicates	1994	SIGMOD	9.6721003e-05
3,945	Exploiting Correlations for Expensive Predicate Evaluation	2015	SIGMOD	7.0055154e-05
4,435	Filtering with Approximate Predicates	1998	VLDB	6.7078883e-05

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