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: 11822
Venue: VLDB
Year: 2018
Pagerank: 5.9990929e-05
Overall Rank: 6,038 | 58.58%
DOI: 10.14778/3236187.3236199

Incoming Non-self Citations Over Time

Authors

1. Sona Hasani (University of Texas)
2. Saravanan Thirumuruganathan (Hamad Bin Khalifa University; Qatar Computing Research Institute; University of Texas)
3. Abolfazl Asudeh (University of Michigan)
4. Nick Koudas (University of Toronto)
5. Gautam Das (University of Texas)

BibTeX Citation

@article{hasani_vldb18,
        title = {{Efficient Construction of Approximate Ad-Hoc ML models Through Materialization and Reuse}},
        author = {Hasani, Sona and Thirumuruganathan, Saravanan and Asudeh, Abolfazl and Koudas, Nick and Das, Gautam},
        journal = {PVLDB},
        series = {{VLDB} '18},
        volume = {11},
        number = {11},
        pages = {1468--1481},
        doi = {10.14778/3236187.3236199},
        url = {https://doi.org/10.14778/3236187.3236199},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,114	Optimizing Machine Learning Inference Queries with Correlative Proxy Models	2022	VLDB	6.8941194e-05
4,130	PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models	2020	SIGMOD	6.8839621e-05
6,309	Materialization and Reuse Optimizations for Production Data Science Pipelines	2022	SIGMOD	5.9189554e-05
8,648	ApproxML: Efficient Approximate Ad-Hoc ML Models Through Materialization and Reuse	2019	VLDB	5.3920944e-05
10,861	Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching	2025	VLDB	5.093636e-05
11,681	Shahin: Faster Algorithms for Generating Explanations for Multiple Predictions	2021	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
106	The MADlib Analytics Library or MAD Skills, the SQL	2012	VLDB	0.00033539462
136	Join Synopses for Approximate Query Answering	1999	SIGMOD	0.00030123303
363	Approximate Query Processing: Taming the TeraBytes! A Tutorial	2001	VLDB	0.0002005475
514	Goods: Organizing Google's Datasets	2016	SIGMOD	0.00017178673
640	Materialization Optimizations for Feature Selection Workloads	2014	SIGMOD	0.00015409494
715	Learning Generalized Linear Models Over Normalized Data	2015	SIGMOD	0.00014655327
764	To Join or Not to Join? Thinking Twice about Joins before Feature Selection	2016	SIGMOD	0.00014226652
909	Dynamic Sample Selection for Approximate Query Processing	2003	SIGMOD	0.00013291205
1,250	Data Management in Machine Learning: Challenges, Techniques, and Systems	2017	SIGMOD	0.00011485301
1,670	MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis	2018	SIGMOD	0.00010045615
3,490	MLog: Towards Declarative In-Database Machine Learning	2017	VLDB	7.3667971e-05
5,955	Mining Multi-Dimensional Constrained Gradients in Data Cubes	2001	VLDB	6.0290092e-05
6,414	Prediction Cubes	2005	VLDB	5.883228e-05
7,000	A Cost-based Optimizer for Gradient Descent Optimization	2017	SIGMOD	5.7287645e-05
8,121	NetCube: A Scalable Tool for Fast Data Mining and Compression	2001	VLDB	5.4834308e-05

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