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Diversity based Relevance Feedback for Time Series Search

Summary: Diversity-based relevance feedback for time series search using dual-tree CWT and SAX representations to boost retrieval accuracy. Representation-aware feedback with weighting for single and multi-representation cases yields gains with simple NN and claims diversification via representation feedback. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h4e01a42bdfda1416
Venue
VLDB
Year
2014
Pagerank
5.2916397e-05
Overall Rank
8,662 | 41.79%
DOI
10.14778/2732228.2732230

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{eravci_vldb14,
        title = {{Diversity based Relevance Feedback for Time Series Search}},
        author = {Eravci, Bahaeddin and Ferhatosmanoglu, Hakan},
        journal = {PVLDB},
        series = {{VLDB} '14},
        volume = {7},
        number = {2},
        doi = {10.14778/2732228.2732230},
        url = {https://doi.org/10.14778/2732228.2732230},
        year = {2014}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
6,333 New Trends on Exploratory Methods for Data Analytics 2017 VLDB 5.8095741e-05
8,516 Exploring the Data Wilderness through Examples 2019 SIGMOD 5.3239705e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1,913 MindReader: Querying databases through multiple examples 1998 VLDB 9.386654e-05
3,965 Using Trees to Depict a Forest 2009 VLDB 6.8890329e-05
6,614 Similarity Search: A Matching Based Approach 2006 VLDB 5.7323821e-05
Previous Page 1 / 1 Next

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