SEER: An End-to-End Toolkit for Benchmarking Time Series Database Systems in Monitoring Applications
Summary: SEER is an interactive, end-to-end TSDB benchmarking toolkit for monitoring workloads, supporting synthetic data augmentation, configurable queries, and deployment. It combines precomputed TSM-Bench results with custom mixed-workload evaluation and use-case-based system recommendations. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Luca Althaus (University of Freiburg)
- 2. Mourad Khayati (University of Freiburg)
- 3. Abdelouahab Khelifati (University of Freiburg)
- 4. Anton Dignoes (Free University of Bolzano)
- 5. Djellel Difallah (New York University)
- 6. Philippe Cudre-Mauroux (University of Freiburg)
BibTeX Citation
@article{althaus_vldb24,
title = {{SEER: An End-to-End Toolkit for Benchmarking Time Series Database Systems in Monitoring Applications}},
author = {Althaus, Luca and Khayati, Mourad and Khelifati, Abdelouahab and Dignoes, Anton and Difallah, Djellel and Cudre-Mauroux, Philippe},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4361--4364},
doi = {10.14778/3685800.3685875},
url = {https://doi.org/10.14778/3685800.3685875},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,541 | Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science | 2018 | VLDB | 8.4500033e-05 |
| 4,762 | Time Series Data Encoding for Efficient Storage: A Comparative Analysis in Apache IoTDB | 2022 | VLDB | 6.519484e-05 |
| 5,518 | Self-supervised and Interpretable Data Cleaning with Sequence Generative Adversarial Networks | 2023 | VLDB | 6.1885722e-05 |
| 6,579 | A Deep Generative Model for Trajectory Modeling and Utilization | 2023 | VLDB | 5.8364579e-05 |
| 6,816 | Inspector Gadget: A Data Programming-based Labeling System for Industrial Images | 2021 | VLDB | 5.7649828e-05 |
| 7,369 | SmartBench: A Benchmark For Data Management In Smart Spaces | 2020 | VLDB | 5.6314085e-05 |
| 8,959 | TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications | 2023 | VLDB | 5.3444909e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,524 | Database Benchmarking for Supporting Real-Time Interactive Querying of Large Data | 2020 | SIGMOD |
| 2 | 226 | OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases | 2014 | VLDB |
| 3 | 13,496 | Demonstration of ModelarDB: Model-Based Management of Dimensional Time Series | 2019 | SIGMOD |
| 4 | 9,941 | An Adaptive Benchmark for Modeling User Exploration of Large Datasets | 2025 | SIGMOD |
| 5 | 4,000 | DBSeer: Resource and Performance Prediction for Building a Next Generation Database Cloud | 2013 | CIDR |
| 6 | 1,931 | Performance and Resource Modeling in Highly-Concurrent OLTP Workloads | 2013 | SIGMOD |
| 7 | 7,369 | SmartBench: A Benchmark For Data Management In Smart Spaces | 2020 | VLDB |
| 8 | 9,817 | Lindorm TSDB: A Cloud-native Time-series Database for Large-scale Monitoring Systems | 2023 | VLDB |
| 9 | 9,016 | DBSeer: Pain-free Database Administration through Workload Intelligence | 2015 | VLDB |
| 10 | 8,959 | TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications | 2023 | VLDB |