BURST: Rendering Clustering Techniques Suitable for Evolving Streams

Summary: BURST adapts partition-based clustering to evolving time-series streams by integrating streaming support with AutoKC, an online estimator that infers k. Enables robust, real-time clustering across partitioners with SOTA accuracy on evolving streams. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: ha5faf404de6b4081
Venue: VLDB
Year: 2025
Pagerank: 4.9769913e-05
Overall Rank: 11,355 | 23.69%
DOI: 10.14778/3749646.3749675
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Apostolos Giannoulidis (Aristotle University of Thessaloniki)
2. Anastasios Gounaris (Aristotle University of Thessaloniki)
3. John Paparrizos (Aristotle University of Thessaloniki; Ohio State University)

BibTeX Citation

@article{giannoulidis_vldb25,
        title = {{BURST: Rendering Clustering Techniques Suitable for Evolving Streams}},
        author = {Giannoulidis, Apostolos and Gounaris, Anastasios and Paparrizos, John},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {11},
        pages = {4054--4063},
        doi = {10.14778/3749646.3749675},
        url = {https://doi.org/10.14778/3749646.3749675},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
7,096	Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods	2025	VLDB	5.5991152e-05
9,494	Beyond Compression: A Comprehensive Evaluation of Lossless Floating-Point Compression	2025	VLDB	5.168414e-05
10,480	HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series	2026	SIGMOD	4.9769913e-05
10,494	MUFASA: Fast and Accurate Multivariate Time-Series Clustering	2026	SIGMOD	4.9769913e-05
10,521	The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis]	2026	SIGMOD	4.9769913e-05

Outgoing Citations (Sorted by Pagerank)

Showing 19 of 19 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
32	BIRCH: An Efficient Data Clustering Method for Very Large Databases	1996	SIGMOD	0.00049714561
928	A Framework for Clustering Evolving Data Streams	2003	VLDB	0.0001301967
1,579	k-Shape: Efficient and Accurate Clustering of Time Series	2015	SIGMOD	0.00010182038
1,629	SAND: Streaming Subsequence Anomaly Detection	2021	VLDB	0.0001003165
1,938	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB	9.334286e-05
3,299	Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection	2022	VLDB	7.441222e-05
3,728	Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series	2023	VLDB	7.0667899e-05
4,317	GRAIL: Efficient Time-Series Representation Learning	2019	VLDB	6.6651881e-05
4,705	Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures	2020	SIGMOD	6.4591377e-05
4,910	METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection	2024	VLDB	6.3554126e-05
7,096	Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods	2025	VLDB	5.5991152e-05
7,647	A Structured Study of Multivariate Time-Series Distance Measures	2025	SIGMOD	5.4746904e-05
8,087	Data Stream Clustering: An In-depth Empirical Study	2023	SIGMOD	5.3917406e-05
9,493	TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection	2025	VLDB	5.168414e-05
9,499	Time-Series Anomaly Detection: Overview and New Trends	2024	VLDB	5.168414e-05
9,665	Odyssey: An Engine Enabling The Time-Series Clustering Journey	2023	VLDB	5.142891e-05
9,916	SPARTAN: Data-Adaptive Symbolic Time-Series Approximation	2025	SIGMOD	5.1079647e-05
11,755	Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances	2023	VLDB	4.9769913e-05
13,782	SAND in Action: Subsequence Anomaly Detection for Streams	2021	VLDB	-

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