Helios: An Adaptive and Query Workload-driven Partitioning Framework for Distributed Graph Stores
Summary: Helios: adaptive, workload-driven partitioning for distributed graph stores; uses workloads to locate active vertices and edges, balancing partition load. RDF integration lowers cross-node traffic and preserves partition quality under changing workloads. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Ali Davoudian (Carleton University)
BibTeX Citation
@inproceedings{davoudian_sigmod19,
title = {{Helios: An Adaptive and Query Workload-driven Partitioning Framework for Distributed Graph Stores}},
author = {Davoudian, Ali},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3300103},
url = {https://dl.acm.org/doi/10.1145/3299869.3300103},
year = {2019}
}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,387 | A Distributed Graph Engine for Web Scale RDF Data | 2013 | VLDB | 0.00010949523 |
| 2,332 | LEOPARD: Lightweight Edge-Oriented Partitioning and Replication for Dynamic Graphs | 2016 | VLDB | 8.7410236e-05 |
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