Visual Exploration of Time Series Anomalies with Metro-Viz
Summary: Metro-Viz is visualization-driven exploration tool for time-series anomaly results, enabling comparison and what-if reasoning. It offers workload-aware data-management and demonstrates applicability across multiple time-series datasets and detectors. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Philipp Eichmann
- 2. Franco Solleza
- 3. Nesime Tatbul
- 4. Stan Zdonik
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,770 | ShapeSearch: A Flexible and Efficient System for Shape-based Exploration of Trendlines | 2020 | SIGMOD | 5.3328309e-05 |
| 9,087 | A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection | 2021 | VLDB | 4.3993112e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,147 | RINSE: Interactive Data Series Exploration with ADS+ | 2015 | VLDB | 9.4325176e-05 |
| 3,049 | Qetch: Time Series Querying with Expressive Sketches | 2018 | SIGMOD | 7.6513435e-05 |
| 4,541 | Interactive Time Series Analytics Powered by ONEX | 2017 | SIGMOD | 6.1023704e-05 |
| 6,123 | Data Ingestion for the Connected World | 2017 | CIDR | 5.1991194e-05 |
| 7,312 | Querying and Exploring Polygamous Relationships in Urban Spatio-Temporal Data Sets | 2017 | SIGMOD | 4.7653242e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,637 | TAB: Unified Benchmarking of Time Series Anomaly Detection Methods | 2025 | VLDB | 4.1945683e-05 |
| 4,554 | A Demonstration of AutoOD: A Self-Tuning Anomaly Detection System | 2022 | VLDB | 6.0911296e-05 |
| 4,426 | Data Debugging and Exploration with Vizier | 2019 | SIGMOD | 6.1969994e-05 |
| 3,171 | Interactive Outlier Exploration in Big Data Streams | 2014 | VLDB | 7.4447236e-05 |
| 10,830 | EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB | 4.1945683e-05 |
| 1,253 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB | 0.00013032074 |
| 9,087 | A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection | 2021 | VLDB | 4.3993112e-05 |
| 6,440 | An Experimental Evaluation of Anomaly Detection in Time Series | 2024 | VLDB | 5.0603878e-05 |
| 7,182 | TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms | 2022 | VLDB | 4.8072409e-05 |
| 11,094 | Time-Series Anomaly Detection: Overview and New Trends | 2024 | VLDB | 4.1945683e-05 |