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Continuous Outlier Detection in Data Streams: An Extensible Framework and State-Of-The-Art Algorithms

Summary: Extensible, open-source MOA extension for continuous, distance-based outlier detection over data streams. Four online algorithms for streaming outliers, including two novel methods; emphasis on faster runtimes, flexibility, and reduced space. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4625
Venue
SIGMOD
Year
2013
Pagerank
6.6309693e-05
Overall Rank
3,920 | 72.74%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
1,627 Data Cleaning: Overview and Emerging Challenges 2016 SIGMOD 0.00011086905
1,854 Distance-based Outlier Detection in Data Streams 2016 VLDB 0.00010317762
3,012 NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing 2019 VLDB 7.7153586e-05
3,171 Interactive Outlier Exploration in Big Data Streams 2014 VLDB 7.4447236e-05
8,244 PROUD: PaRallel OUtlier Detection for streams 2020 SIGMOD 4.5517083e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank Cited Paper Year Venue Pagerank
91 M-tree: An Efficient Access Method for Similarity Search in Metric Spaces 1997 VLDB 0.0005181666
774 Algorithms for Mining Distance-Based Outliers in Large Datasets 1998 VLDB 0.00016865771
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