PTO: A Workload-driven Predictive Table Optimizer for Lakehouse Systems

Summary: Workload-driven table layout tuning for lakehouses: PTO predicts partitioning/sort/file-size/bin-packing params from query predicates, using heuristic candidate pruning + table sampling + Gradient Boosting to avoid exhaustive search. Implemented for Presto/Iceberg; significant latency gains on TPC-H/TPC-DS. (summarized by gpt-5-mini on Apr 11 2026)

Paper ID: 7508
Venue: SIGMOD
Year: 2026
Pagerank: 4.1905499e-05
Overall Rank: 10,196 | 29.14%
DOI: 10.1145/3786681

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 12 of 12 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
70	Hive - A Warehousing Solution Over a Map-Reduce Framework	2009	VLDB	0.00059744625
659	The Making of TPC-DS	2006	VLDB	0.00018514913
739	Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores	2020	VLDB	0.00017365933
1,356	Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics	2021	CIDR	0.00012409986
2,533	Velox: Meta’s Unified Execution Engine	2022	VLDB	8.5870599e-05
4,516	An Empirical Evaluation of Columnar Storage Formats	2024	VLDB	6.1146215e-05
5,105	Analyzing and Comparing Lakehouse Storage Systems	2023	CIDR	5.6916018e-05
5,537	Presto: A Decade of SQL Analytics at Meta	2023	SIGMOD	5.453017e-05
8,223	Automated Multidimensional Data Layouts in Amazon Redshift	2024	SIGMOD	4.5509217e-05
8,410	Pruning in Snowflake: Working Smarter, Not Harder	2025	SIGMOD	4.5154358e-05
8,607	Unity Catalog: Open and Universal Governance for the Lakehouse and Beyond	2025	SIGMOD	4.4810982e-05
9,236	AutoComp: Automated Data Compaction for Log-Structured Tables in Data Lakes	2025	SIGMOD	4.3648789e-05

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