ApproxML: Efficient Approximate Ad-Hoc ML Models Through Materialization and Reuse

Summary: ApproxML presents materialization-driven approximate ML, reusing previously built models to construct new ones for ad-hoc predictive queries. By caching and composing GLMs, K-means and GMMs, it speeds exploration with bounded accuracy loss, suited to data-management workloads. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 11897
Venue: VLDB
Year: 2019
Pagerank: 4.475291e-05
Overall Rank: 8,653 | 39.81%
DOI: 10.14778/3352063.3352096

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,469	Materialization and Reuse Optimizations for Production Data Science Pipelines	2022	SIGMOD	5.0519488e-05
10,471	Approximating Opaque Top-k Queries	2025	SIGMOD	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
903	To Join or Not to Join? Thinking Twice about Joins before Feature Selection	2016	SIGMOD	0.0001547016
6,330	Efficient Construction of Approximate Ad-Hoc ML models Through Materialization and Reuse	2018	VLDB	5.1077416e-05

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