Comprehensive and Efficient Workload Compression
Summary: Construct a representative workload from an input analytical workload with guarantees on representativity and coverage. A greedy, approximation-guaranteed algorithm solves the NP-hard problem, preserving distributions and outliers, and compares with sampling and clustering. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Shaleen Deep
- 2. Anja Gruenheid
- 3. Paraschos Koutris
- 4. Jeffrey Naughton
- 5. Stratis Viglas
Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,435 | Real-time Workload Pattern Analysis for Large-scale Cloud Databases | 2023 | VLDB | 7.0946114e-05 |
| 3,750 | DIAMetrics: Benchmarking Query Engines at Scale | 2020 | VLDB | 6.7864305e-05 |
| 5,673 | Budget-aware Index Tuning with Reinforcement Learning | 2022 | SIGMOD | 5.3789277e-05 |
| 6,364 | ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning | 2022 | SIGMOD | 5.0895007e-05 |
| 7,332 | Refactoring Index Tuning Process with Benefit Estimation | 2024 | VLDB | 4.7553758e-05 |
| 8,043 | DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning | 2022 | VLDB | 4.5954398e-05 |
| 9,469 | Database Gyms | 2023 | CIDR | 4.3304872e-05 |
| 9,637 | TracEx: Understanding and Analyzing Database Traces | 2024 | CIDR | 4.3068069e-05 |
| 9,930 | Wred: Workload Reduction for Scalable Index Tuning | 2024 | SIGMOD | 4.2469394e-05 |
| 9,955 | SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression | 2025 | VLDB | 4.2332427e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 329 | Neo: A Learned Query Optimizer | 2019 | VLDB | 0.00027301488 |
| 662 | Database Tuning Advisor for Microsoft SQL Server 2005 | 2004 | VLDB | 0.00018478597 |
| 1,443 | Compressing SQL Workloads | 2002 | SIGMOD | 0.00011944621 |
| 3,344 | F1 Query: Declarative Querying at Scale | 2018 | VLDB | 7.1944106e-05 |
| 3,750 | DIAMetrics: Benchmarking Query Engines at Scale | 2020 | VLDB | 6.7864305e-05 |
| 4,280 | Primitives for Workload Summarization and Implications for SQL | 2003 | VLDB | 6.28375e-05 |
| 4,679 | Adaptive Sampling for Rapidly Matching Histograms | 2018 | VLDB | 5.9978792e-05 |
| 6,085 | Answering Top-k Representative Queries on Graph Databases | 2014 | SIGMOD | 5.2165029e-05 |
| 8,360 | Query Log Compression for Workload Analytics | 2019 | VLDB | 4.5314883e-05 |
| 8,361 | Efficient Summarization Framework for Multi-Attribute Uncertain Data | 2014 | SIGMOD | 4.5314883e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,813 | A Robust, Optimization-Based Approach for Approximate Answering of Aggregate Queries | 2001 | SIGMOD | 8.0816314e-05 |
| 7,564 | Modeling Shifting Workloads for Learned Database Systems | 2024 | SIGMOD | 4.7049893e-05 |
| 8,521 | Computing A Well-Representative Summary of Conjunctive Query Results | 2024 | PODS | 4.4893996e-05 |
| 7,431 | CompressDB: Enabling Efficient Compressed Data Direct Processing for Various Databases | 2022 | SIGMOD | 4.7274757e-05 |
| 6,160 | Compression Aware Physical Database Design | 2011 | VLDB | 5.1750659e-05 |
| 5,645 | Database Workload Characterization with Query Plan Encoders | 2022 | VLDB | 5.3928148e-05 |
| 8,575 | Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems | 2022 | VLDB | 4.4880409e-05 |
| 1,098 | Query Optimization In Compressed Database Systems | 2001 | SIGMOD | 0.00014070252 |
| 8,360 | Query Log Compression for Workload Analytics | 2019 | VLDB | 4.5314883e-05 |
| 1,443 | Compressing SQL Workloads | 2002 | SIGMOD | 0.00011944621 |