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Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data

Summary: Proposes a time-series data cube for multi-dimensional market segments to capture subspaces and detect anomalies via higher-level expectations. Introduces an efficient iterative subspace search to identify approximate top-k anomalies in each subspace; validated on synthetic and real-world data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9598
Venue
VLDB
Year
2007
Pagerank
4.5549459e-05
Overall Rank
8,228 | 42.76%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
7,356 GAMPS: Compressing Multi Sensor Data by Grouping and Amplitude Scaling 2009 SIGMOD 4.7529612e-05
11,217 Efficient Approximation Framework for Attribute Recommendation 2023 SIGMOD 4.1945683e-05
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Showing 13 of 13 cited papers.

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

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