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: h5b0f85300faa4af4
Venue: SIGMOD
Year: 2010
Pagerank: 5.1788416e-05
Overall Rank: 9,426 | 36.63%
DOI: 10.1145/1807167.1807189

Incoming Non-self Citations Over Time

Authors

1. Peng Wang (Fudan University; Microsoft)
2. Haixun Wang (Microsoft)
3. Majin Liu (Fudan University)
4. Wei Wang (Fudan University)

BibTeX Citation

@inproceedings{wang_sigmod10,
        title = {{An Algorithmic Approach to Event Summarization}},
        author = {Wang, Peng and Wang, Haixun and Liu, Majin and Wang, Wei},
        series = {{SIGMOD} '10},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1807167.1807189},
        url = {https://dl.acm.org/doi/10.1145/1807167.1807189},
        year = {2010}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,434	Finding Semantics in Time Series	2011	SIGMOD	7.3027739e-05
9,386	Behavior Query Discovery in System-Generated Temporal Graphs	2016	VLDB	5.1868213e-05

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
42	Fast Subsequence Matching in Time-Series Databases	1994	SIGMOD	0.00045773967
193	Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases	2001	SIGMOD	0.00025648171

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