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: 10769
Venue: VLDB
Year: 2014
Pagerank: 4.4849545e-05
Overall Rank: 8,614 | 40.08%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Bahaeddin Eravci
2. Hakan Ferhatosmanoglu

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,237	New Trends on Exploratory Methods for Data Analytics	2017	VLDB	5.1435341e-05
8,344	Exploring the Data Wilderness through Examples	2019	SIGMOD	4.5428111e-05

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
1,608	MindReader: Querying databases through multiple examples	1998	VLDB	0.00011151257
3,654	Using Trees to Depict a Forest	2009	VLDB	6.873144e-05
6,164	Similarity Search: A Matching Based Approach	2006	VLDB	5.1733919e-05

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