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An Algorithmic Approach to Event Summarization

Summary: Algorithmic event summarization via Hidden Markov Models to capture internal system dynamics and state transitions. Learned HMMs yield shorter description length and higher interpretability than piecewise summaries; experiments show efficiency and effectiveness. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4244
Venue
SIGMOD
Year
2010
Pagerank
4.3901967e-05
Overall Rank
9,108 | 36.70%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
2,684 Finding Semantics in Time Series 2011 SIGMOD 8.3154143e-05
9,056 Behavior Query Discovery in System-Generated Temporal Graphs 2016 VLDB 4.3997447e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank Cited Paper Year Venue Pagerank
65 Fast Subsequence Matching in Time-Series Databases 1994 SIGMOD 0.00061977022
243 Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases 2001 SIGMOD 0.00031052437
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