ExplainIt! - A Declarative Root-cause Analysis Engine for Time Series Data
Summary: ExplainIt! is a declarative time-series root-cause engine for systems, exposing SQL-like enumeration of causal hypotheses. It ranks thousands of hypotheses to a handful, enabling rapid exploration of unknown probabilistic causal models in production data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Vimalkumar Jeyakumar
- 2. Omid Madani
- 3. Ali Parandeh
- 4. Ashutosh Kulshreshtha
- 5. Weifei Zeng
- 6. Navindra Yadav
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,640 | Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series | 2021 | VLDB | 0.00011048873 |
| 2,140 | Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases | 2020 | VLDB | 9.4565836e-05 |
| 6,904 | BALANCE: Bayesian Linear Attribution for Root Cause Localization | 2023 | SIGMOD | 4.8878659e-05 |
| 7,993 | RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems | 2025 | VLDB | 4.6080455e-05 |
| 9,870 | From Logs to Causal Inference: Diagnosing Large Systems | 2025 | VLDB | 4.2626861e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 66 | Spark SQL: Relational Data Processing in Spark | 2015 | SIGMOD | 0.00061707583 |
| 211 | Gorilla: A Fast, Scalable, In-Memory Time Series Database | 2015 | VLDB | 0.0003401421 |
| 1,588 | Druid: A Real-time Analytical Data Store | 2014 | SIGMOD | 0.00011232949 |
| 2,129 | MacroBase: Prioritizing Attention in Fast Data | 2017 | SIGMOD | 9.4799835e-05 |
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