Database Paper Browser

Back to papers

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
4626
Venue
SIGMOD
Year
2013
Pagerank
6.6245531e-05
Overall Rank
3,921 | 72.76%
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
1,629 Data Cleaning: Overview and Emerging Challenges 2016 SIGMOD 0.00011073148
1,859 Distance-based Outlier Detection in Data Streams 2016 VLDB 0.00010307902
3,014 NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing 2019 VLDB 7.7079954e-05
3,176 Interactive Outlier Exploration in Big Data Streams 2014 VLDB 7.4380543e-05
8,238 PROUD: PaRallel OUtlier Detection for streams 2020 SIGMOD 4.5473446e-05
Previous Page 1 / 1 Next

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 0.00051785122
768 Algorithms for Mining Distance-Based Outliers in Large Datasets 1998 VLDB 0.00016864875
Previous Page 1 / 1 Next

Semantically Similar Papers