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A Skip-list Approach for Efficiently Processing Forecasting Queries

Summary: Uses hypothesis tests to select history lengths and samples for forecasting, exposed through an I/O-conscious skip list. Workload PMFs guide randomized prebuilt-model selection under update-cost budgets, supporting point, range, aggregate, and join queries. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
9921
Venue
VLDB
Year
2008
Pagerank
5.21582e-05
Overall Rank
9,805 | 32.73%
DOI
10.14778/1454159.1454265

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{ge_vldb08,
        title = {{A Skip-list Approach for Efficiently Processing Forecasting Queries}},
        author = {Ge, Tingjian and Zdonik, Stan},
        journal = {PVLDB},
        series = {{VLDB} '08},
        volume = {1},
        number = {1},
        pages = {984--995},
        doi = {10.14778/1454159.1454265},
        url = {https://doi.org/10.14778/1454159.1454265},
        year = {2008}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
1,250 Data Management in Machine Learning: Challenges, Techniques, and Systems 2017 SIGMOD 0.00011485301
2,177 The Case for Predictive Database Systems: Opportunities and Challenges 2011 CIDR 9.0159844e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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