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An Intermediate Representation for Hybrid Database and Machine Learning Workloads

Summary: IFAQ: an IR and compiler for hybrid DB/ML workloads, expressed as iterative programs with functional aggregates. Demonstrates OLAP, linear algebra, and factorization-machine learning on training data from relational feature-extraction queries. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12486
Venue
VLDB
Year
2021
Pagerank
4.4520434e-05
Overall Rank
8,753 | 39.17%
DOI
10.14778/3476311.3476356

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
6,157 Optimizing Tensor Programs on Flexible Storage 2023 SIGMOD 5.1755682e-05
10,909 Tight Bounds of Circuits for Sum-Product Queries 2024 PODS 4.1905499e-05
11,284 Demonstration of OpenDBML, a Framework for Democratizing In-Database Machine Learning 2023 VLDB 4.1905499e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank Cited Paper Year Venue Pagerank
59 Efficiently Compiling Efficient Query Plans for Modern Hardware 2011 VLDB 0.0006445664
2,383 How to Architect a Query Compiler 2016 SIGMOD 8.9198524e-05
3,280 A Layered Aggregate Engine for Analytics Workloads 2019 SIGMOD 7.2813732e-05
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