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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
11891
Venue
VLDB
Year
2018
Pagerank
5.4724852e-05
Overall Rank
8,180 | 43.88%
DOI
10.14778/3229863.3236252

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{chanial_vldb18,
        title = {{ConnectionLens: Finding Connections Across Heterogeneous Data Sources}},
        author = {Chanial, Camille and Dziri, Rédouane and Galhardas, Helena and Leblay, Julien and Le Nguyen, Minh-Huong and Manolescu, Ioana},
        journal = {PVLDB},
        series = {{VLDB} '18},
        volume = {11},
        number = {12},
        pages = {2030--2033},
        doi = {10.14778/3229863.3236252},
        url = {https://doi.org/10.14778/3229863.3236252},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
8,920 Full-Power Graph Querying: State of the Art and Challenges 2023 VLDB 5.3483178e-05
9,222 Enabling Rich Queries Over Heterogeneous Data From Diverse Sources In HealthCare 2020 CIDR 5.3038896e-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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