Distance-based Outlier Detection in Data Streams
Summary: Systematic benchmarking of DODDS algorithms on identical streams and platforms; unsupervised, distribution-free distance-based outlier detection. Results: MCOD dominates across settings; Thresh LEAP competitive only in limited cases, highlighting MCOD’s time/space efficiency. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Luan Tran
- 2. Liyue Fan
- 3. Cyrus Shahabi
Incoming Citations (Sorted by Pagerank)
Showing 12 of 12 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 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 |
| 701 | Efficient Algorithms for Mining Outliers from Large Data Sets | 2000 | SIGMOD | 0.00017938417 |
| 774 | Algorithms for Mining Distance-Based Outliers in Large Datasets | 1998 | VLDB | 0.00016865771 |
| 2,629 | Online Outlier Detection in Sensor Data Using Non-Parametric Models | 2006 | VLDB | 8.4160309e-05 |
| 3,171 | Interactive Outlier Exploration in Big Data Streams | 2014 | VLDB | 7.4447236e-05 |
| 3,920 | Continuous Outlier Detection in Data Streams: An Extensible Framework and State-Of-The-Art Algorithms | 2013 | SIGMOD | 6.6309693e-05 |
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