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ConnectionLens: Finding Connections Across Heterogeneous Data Sources

Summary: ConnectionLens: keyword search across heterogeneous, dynamic data sources using a novel algorithm for cross-source connections. Demonstrated with Le Monde journalist use cases, emphasizing interconnecting, traceable information across diverse data ecosystems. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11703
Venue
VLDB
Year
2018
Pagerank
4.6342491e-05
Overall Rank
7,858 | 45.34%
DOI
10.14778/3229863.3236252

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
8,750 Full-Power Graph Querying: State of the Art and Challenges 2023 VLDB 4.456315e-05
9,028 Enabling Rich Queries Over Heterogeneous Data From Diverse Sources In HealthCare 2020 CIDR 4.4043898e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

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

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