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)
Incoming Non-self Citations Over Time
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Authors
- 1. André Eickler (University of Passau)
- 2. Alfons Kemper (University of Passau)
- 3. Donald Kossmann (University of Passau)
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)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 302 | Shoring Up Persistent Applications | 1994 | SIGMOD | 0.0002167061 |
| 2,592 | RP*: A Family of Order-Preserving Scalable Distributed Data Structures | 1994 | VLDB | 8.2460099e-05 |
| 2,658 | Lazy Updates for Distributed Search Structure | 1993 | SIGMOD | 8.1636113e-05 |
| 4,163 | Distributing a Search Tree Among a Growing Number of Processors | 1994 | SIGMOD | 6.7683361e-05 |
| 4,700 | A Performance Evaluation of OID Mapping Techniques | 1995 | VLDB | 6.4631948e-05 |
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