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Tracking Entities in the Dynamic World: A Fast Algorithm for Matching Temporal Records

Summary: Proposes a static-first, dynamic-second approach to temporal entity matching: cluster records ignoring evolution, then merge by potential state changes. Achieves equivalent accuracy to state-of-the-art with an order-of-magnitude speedup on several temporal datasets, enabling scalable longitudinal resolution. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10925
Venue
VLDB
Year
2014
Pagerank
4.6723595e-05
Overall Rank
7,706 | 46.40%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
7,345 Linking Temporal Records for Profiling Entities 2015 SIGMOD 4.756212e-05
11,183 Matching Roles from Temporal Data 2023 SIGMOD 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
936 Framework for Evaluating Clustering Algorithms in Duplicate Detection 2009 VLDB 0.0001521549
2,405 Linking Temporal Records 2011 VLDB 8.8815018e-05
3,130 Behavior Based Record Linkage 2010 VLDB 7.4993061e-05
3,487 Longitudinal Analytics on Web Archive Data: It's About Time! 2011 CIDR 7.0480733e-05
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