Mining Deviants in a Time Series Database

Summary: Defines time-series “deviants” via representation sparsity, capturing surprising points despite drift rather than relying on extrema or fixed models. An efficient detector also yields sparse, low-error histograms, outperforming optimal histograms at equal storage and aiding selectivity estimation. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 8819
Venue: VLDB
Year: 1999
Pagerank: 5.4650371e-05
Overall Rank: 8,216 | 43.64%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. H. V. Jagadish (University of Michigan)
2. Nick Koudas (AT&T)
3. S. Muthukrishnan (AT&T)

BibTeX Citation

@article{jagadish_vldb99,
        title = {{Mining Deviants in a Time Series Database}},
        author = {Jagadish, H. V. and Koudas, Nick and Muthukrishnan, S.},
        journal = {PVLDB},
        series = {{VLDB} '99},
        pages = {102--113},
        year = {1999}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,590	MOST: Model-Based Compression with Outlier Storage for Time Series Data	2023	SIGMOD	5.8316635e-05
8,059	Probabilistic Histograms for Probabilistic Data	2009	VLDB	5.4979799e-05

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank

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