PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics
Summary: PREDIcT predicts runtime for large-scale iterative analytics by using small sample runs plus per-iteration features aligned with dataset characteristics. It tolerates diverse convergence dynamics and high iteration-to-iteration variability (up to 100x), achieving 10-30% relative error on scale-free graphs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,154 | SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning | 2018 | VLDB | 9.4176683e-05 |
| 2,222 | SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning | 2019 | SIGMOD | 9.2598438e-05 |
| 6,507 | Expand your Training Limits! Generating Training Data for ML-based Data Management | 2021 | SIGMOD | 5.0273414e-05 |
| 7,291 | Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and Opportunities | 2022 | SIGMOD | 4.7677422e-05 |
| 9,614 | Auto-Approximation of Graph Computing | 2014 | VLDB | 4.3136057e-05 |
| 11,059 | Agile-Ant: Self-managing Distributed Cache Management for Cost Optimization of Big Data Applications | 2024 | VLDB | 4.1905499e-05 |
| 11,343 | Juggler: Autonomous Cost Optimization and Performance Prediction of Big Data Applications | 2022 | SIGMOD | 4.1905499e-05 |
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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.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,266 | Incrementalizing Graph Algorithms | 2021 | SIGMOD | 5.5949839e-05 |
| 1,876 | Large-Scale Distributed Graph Computing Systems: An Experimental Evaluation | 2015 | VLDB | 0.00010242818 |
| 2,916 | Pregel Algorithms for Graph Connectivity Problems with Performance Guarantees | 2014 | VLDB | 7.9062736e-05 |
| 11,655 | Query-Driven Learning for Next Generation Predictive Modeling & Analytics | 2019 | SIGMOD | 4.1905499e-05 |
| 2,332 | Optimizing Graph Algorithms on Pregel-like Systems | 2014 | VLDB | 9.0173968e-05 |
| 4,075 | Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads | 2013 | VLDB | 6.4699689e-05 |
| 2,179 | Spinning Fast Iterative Data Flows | 2012 | VLDB | 9.3632007e-05 |
| 7,778 | Runtime Variation in Big Data Analytics | 2023 | SIGMOD | 4.6491879e-05 |
| 1,684 | Fast Iterative Graph Computation with Block Updates | 2013 | VLDB | 0.00010912102 |
| 11,158 | Predicting Query Execution time for JIT Compiled Database Engines | 2023 | CIDR | 4.1905499e-05 |