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 (Cisco)
- 2. Omid Madani (Cisco)
- 3. Ali Parandeh (Cisco)
- 4. Ashutosh Kulshreshtha (Cisco)
- 5. Weifei Zeng (Cisco)
- 6. Navindra Yadav (Cisco)
BibTeX Citation
@inproceedings{jeyakumar_sigmod19,
title = {{ExplainIt! - A Declarative Root-cause Analysis Engine for Time Series Data}},
author = {Jeyakumar, Vimalkumar and Madani, Omid and Parandeh, Ali and Kulshreshtha, Ashutosh and Zeng, Weifei and Yadav, Navindra},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3314048},
url = {https://dl.acm.org/doi/10.1145/3299869.3314048},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,544 | Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series | 2021 | VLDB | 0.00010304574 |
| 1,857 | Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases | 2020 | VLDB | 9.4929201e-05 |
| 7,236 | RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems | 2025 | VLDB | 5.5790509e-05 |
| 7,652 | BALANCE: Bayesian Linear Attribution for Root Cause Localization | 2023 | SIGMOD | 5.4772833e-05 |
| 10,207 | From Logs to Causal Inference: Diagnosing Large Systems | 2025 | VLDB | 5.0596605e-05 |
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 23 | Spark SQL: Relational Data Processing in Spark | 2015 | SIGMOD | 0.00055406774 |
| 148 | Gorilla: A Fast, Scalable, In-Memory Time Series Database | 2015 | VLDB | 0.0002900671 |
| 1,236 | Druid: A Real-time Analytical Data Store | 2014 | SIGMOD | 0.00011402848 |
| 1,833 | MacroBase: Prioritizing Attention in Fast Data | 2017 | SIGMOD | 9.5405247e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 13,804 | Demonstration of Inferring Causality from Relational Databases with CaRL | 2020 | VLDB |
| 2 | 2,222 | Explaining Query Answers with Explanation-Ready Databases | 2016 | VLDB |
| 3 | 7,236 | RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems | 2025 | VLDB |
| 4 | 9,884 | TSExplain: Surfacing Evolving Explanations for Time Series | 2021 | SIGMOD |
| 5 | 6,699 | Toward Interpretable and Actionable Data Analysis with Explanations and Causality | 2022 | VLDB |
| 6 | 11,143 | CauSumX: Summarized Causal Explanations For Group-By-Average Queries | 2025 | SIGMOD |
| 7 | 11,142 | CausalExplain: Causal Explanations of Black-box Models with Training Data Subsets | 2025 | SIGMOD |
| 8 | 4,982 | XInsight: eXplainable Data Analysis Through The Lens of Causality | 2023 | SIGMOD |
| 9 | 6,436 | Summarized Causal Explanations For Aggregate Views | 2024 | SIGMOD |
| 10 | 1,981 | Causality and Explanations in Databases | 2014 | VLDB |