DBScholar

Back to papers

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
3657
Venue
SIGMOD
Year
2004
Pagerank
5.2945481e-05
Overall Rank
9,271 | 36.40%
DOI
10.1145/1007568.1007695

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{cai_sigmod04,
        title = {{MAIDS: Mining Alarming Incidents from Data Streams}},
        author = {Cai, Y. Dora and Clutter, David and Pape, Greg and Han, Jiawei and Welge, Michael and Auvil, Loretta},
        series = {{SIGMOD} '04},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1007568.1007695},
        url = {https://dl.acm.org/doi/10.1145/1007568.1007695},
        year = {2004}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
7,400 Effective Variation Management for Pseudo Periodical Streams 2007 SIGMOD 5.6255397e-05
Previous Page 1 / 1 Next

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.

Previous Page 1 / 1 Next

Semantically Similar Papers