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Identifying Similarities, Periodicities and Bursts for Online Search Queries

Summary: From MSN query logs, builds per-query daily demand time series and uses Fourier-based similarity with energy of omitted components, indexed by a metric-tree. Identifies periodicities and bursts, enables query-by-burst, and offers an interactive time-series discovery tool. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3575
Venue
SIGMOD
Year
2004
Pagerank
8.286986e-05
Overall Rank
2,659 | 81.76%
DOI
10.1145/1007568.1007586

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{vlachos_sigmod04,
        title = {{Identifying Similarities, Periodicities and Bursts for Online Search Queries}},
        author = {Vlachos, Michail and Meek, Chris and Gunopulos, Dimitrios and Vagena, Zografoula},
        series = {{SIGMOD} '04},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1007568.1007586},
        url = {https://dl.acm.org/doi/10.1145/1007568.1007586},
        year = {2004}
}

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