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Efficient Construction of Approximate Ad-Hoc ML models Through Materialization and Reuse

Summary: Proposes materialization and reuse of previously built ML models to answer new analytic queries over OLAP-aligned data. A cost-based framework selects and composes models (GLMs, K-Means, GMM) to approximate new queries, yielding large speedups on big data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11634
Venue
VLDB
Year
2018
Pagerank
5.1077416e-05
Overall Rank
6,330 | 55.97%
DOI
10.14778/3236187.3236199

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