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MAIDS: Mining Alarming Incidents from Data Streams

Summary: MAIDS enables alarming-incident mining on data streams with tilted time windows for multi-resolution modeling and a stream data cube for online multi-dimensional analysis. Online classification, frequent-pattern mining, clustering, and visualization for instant anomaly detection. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3595
Venue
SIGMOD
Year
2004
Pagerank
4.4034035e-05
Overall Rank
9,067 | 36.93%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
6,535 Effective Variation Management for Pseudo Periodical Streams 2007 SIGMOD 5.0243433e-05
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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
33 BIRCH: An Efficient Data Clustering Method for Very Large Databases 1996 SIGMOD 0.00077324389
43 Models and Issues in Data Stream Systems 2002 PODS 0.00072723062
181 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00036992674
341 CURE: An Efficient Clustering Algorithm for Large Databases 1998 SIGMOD 0.00026810548
662 A Framework for Clustering Evolving Data Streams 2003 VLDB 0.00018475968
1,955 Efficient Computation of Iceberg Cubes with Complex Measures 2001 SIGMOD 9.9629452e-05
2,448 Multi-Dimensional Regression Analysis of Time-Series Data Streams 2002 VLDB 8.8032353e-05
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