Mining Deviants in a Time Series Database

Summary: Defines a time-series deviant via a representation sparsity metric and provides an efficient algorithm to identify deviants. Shows deviants uncover artifacts and, as a side benefit, enable lower-error histograms for the same storage, aiding selectivity estimation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 8628
Venue: VLDB
Year: 1999
Pagerank: 4.5566051e-05
Overall Rank: 8,218 | 42.83%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
7,395	MOST: Model-Based Compression with Outlier Storage for Time Series Data	2023	SIGMOD	4.7420041e-05
8,090	Probabilistic Histograms for Probabilistic Data	2009	VLDB	4.5888589e-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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