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Finding Data in the Neighborhood

Summary: Proposes location-independent identifiers for distributed databases, backed by a distributed index and a lightweight replication strategy to dereference IDs without contacting their origin. Evaluates traversal options and benchmarks tradeoffs versus location-dependent approaches, with deployment guidance. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
8625
Venue
VLDB
Year
1997
Pagerank
5.093636e-05
Overall Rank
12,967 | 11.04%
DOI
-

Incoming Non-self Citations Over Time

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Authors

BibTeX Citation

@article{eickler_vldb97,
        title = {{Finding Data in the Neighborhood}},
        author = {Eickler, André and Kemper, Alfons and Kossmann, Donald},
        journal = {PVLDB},
        series = {{VLDB} '97},
        pages = {336},
        year = {1997}
}

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Outgoing Citations (Sorted by Pagerank)

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

Rank Cited Paper Year Venue Pagerank
299 Shoring Up Persistent Applications 1994 SIGMOD 0.00022084669
2,552 RP*: A Family of Order-Preserving Scalable Distributed Data Structures 1994 VLDB 8.4284603e-05
2,605 Lazy Updates for Distributed Search Structure 1993 SIGMOD 8.3494816e-05
4,075 Distributing a Search Tree Among a Growing Number of Processors 1994 SIGMOD 6.9224298e-05
4,611 A Performance Evaluation of OID Mapping Techniques 1995 VLDB 6.607869e-05
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