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Ranking Large Temporal Data

Summary: Introduces aggregate top-k ranking over temporal data (interval-based, not instant). Proposes exact and approximate methods with guarantees; analyzes construction cost, index size, updates, and query costs, and demonstrates scalable, efficient performance on large real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10377
Venue
VLDB
Year
2012
Pagerank
4.7180617e-05
Overall Rank
7,513 | 47.74%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
11,748 Durable Top-k Queries on Temporal Data 2018 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
986 Managing Intervals Efficiently in Object-Relational Databases 2000 VLDB 0.00014838568
1,359 Range Queries in OLAP Data Cubes 1997 SIGMOD 0.0001238588
2,041 Indexable PLA for Efficient Similarity Search 2007 VLDB 9.6992894e-05
4,849 Durable Top-k Search in Document Archives 2010 SIGMOD 5.8773304e-05
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