Database-Agnostic Workload Management
Summary: Database‑agnostic system that frames SQL workload tasks as query‑labeling and decouples learned query representation from labeling to support multi‑tenant, multi‑dialect platforms. Replaces per‑dialect feature engineering with learned SQL embeddings, enabling off‑the‑shelf ML and empirically outperforming handcrafted features on index sampling, security user labeling, and error prediction. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Shrainik Jain (University of Washington)
- 2. Jiaqi Yan (Snowflake)
- 3. Thierry Cruanes (Snowflake)
- 4. Bill Howe (University of Washington)
BibTeX Citation
@inproceedings{jain_cidr19,
address = {Amsterdam, Netherlands},
series = {{CIDR} '19},
title = {{Database-Agnostic Workload Management}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Jain, Shrainik and Yan, Jiaqi and Cruanes, Thierry and Howe, Bill},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 355 | Bao: Making Learned Query Optimization Practical | 2021 | SIGMOD | 0.00020146245 |
| 3,282 | Estimating Cardinalities with Deep Sketches | 2019 | SIGMOD | 7.4491295e-05 |
| 3,328 | Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift | 2023 | SIGMOD | 7.4094448e-05 |
| 4,101 | Towards Dynamic and Safe Configuration Tuning for Cloud Databases | 2022 | SIGMOD | 6.7930727e-05 |
| 5,160 | Facilitating SQL Query Composition and Analysis | 2020 | SIGMOD | 6.2354336e-05 |
| 6,658 | Workload Insights From The Snowflake Data Cloud: What Do Production Analytic Queries Really Look Like? | 2025 | VLDB | 5.7025639e-05 |
| 9,430 | Intelligent Automated Workload Analysis for Database Replatforming | 2022 | SIGMOD | 5.1658393e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 49 | Dremel: Interactive Analysis of Web-Scale Datasets | 2010 | VLDB | 0.00043085722 |
| 52 | The Snowflake Elastic Data Warehouse | 2016 | SIGMOD | 0.00041439488 |
| 224 | Self-Driving Database Management Systems | 2017 | CIDR | 0.00023973185 |
| 226 | Amazon Redshift and the Case for Simpler Data Warehouses | 2015 | SIGMOD | 0.0002389883 |
| 864 | SnipSuggest: Context-Aware Autocompletion for SQL | 2011 | VLDB | 0.00013358173 |
| 1,246 | Compressing SQL Workloads | 2002 | SIGMOD | 0.00011355447 |
| 2,543 | SQLShare: Results from a Multi-Year SQL-as-a-Service Experiment | 2016 | SIGMOD | 8.2993351e-05 |
| 2,899 | SQL QueRIE Recommendations | 2010 | VLDB | 7.8825624e-05 |
| 3,913 | Automatic Enforcement of Data Use Policies with DataLawyer | 2015 | SIGMOD | 6.9088542e-05 |
| 4,268 | Primitives for Workload Summarization and Implications for SQL | 2003 | VLDB | 6.6784565e-05 |
| 7,910 | Oracle Workload Intelligence | 2015 | SIGMOD | 5.4128663e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 4,792 | Learned Approximate Query Processing: Make it Light, Accurate and Fast | 2021 | CIDR |
| 2 | 2,768 | Query Performance Prediction for Concurrent Queries using Graph Embedding | 2020 | VLDB |
| 3 | 458 | Query-based Workload Forecasting for Self-Driving Database Management Systems | 2018 | SIGMOD |
| 4 | 6,402 | A Unified Transferable Model for ML-Enhanced DBMS | 2022 | CIDR |
| 5 | 2,892 | WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases | 2016 | VLDB |
| 6 | 320 | DeepDB: Learn from Data, not from Queries! | 2020 | VLDB |
| 7 | 5,160 | Facilitating SQL Query Composition and Analysis | 2020 | SIGMOD |
| 8 | 5,097 | Releasing Cloud Databases from the Chains of Performance Prediction Models | 2017 | CIDR |
| 9 | 4,489 | Real-time Workload Pattern Analysis for Large-scale Cloud Databases | 2023 | VLDB |
| 10 | 5,211 | Database Workload Characterization with Query Plan Encoders | 2022 | VLDB |