Parallel Index-based Stream Join on a Multicore CPU
Summary: Partitioned in-memory merge tree for indexing highly dynamic streaming windows, with a low-cost concurrency control for high-rate updates. A parallel index-based stream join exploits multicore CPUs; octa-core experiments report up to 5.5x throughput over a single-threaded approach. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Amirhesam Shahvarani (Technical University of Munich)
- 2. Hans-Arno Jacobsen (Technical University of Munich)
BibTeX Citation
@inproceedings{shahvarani_sigmod20,
title = {{Parallel Index-based Stream Join on a Multicore CPU}},
author = {Shahvarani, Amirhesam and Jacobsen, Hans-Arno},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3380576},
url = {https://dl.acm.org/doi/10.1145/3318464.3380576},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,820 | Parallelizing Intra-Window Join on Multicores: An Experimental Study | 2021 | SIGMOD | 5.5373345e-05 |
| 9,998 | SWIX: A Memory-efficient Sliding Window Learned Index | 2024 | SIGMOD | 5.1814573e-05 |
| 10,102 | Distributed Stream KNN Join | 2021 | SIGMOD | 5.1488731e-05 |
| 10,765 | HoneyComb: A Parallel Worst-Case Optimal Join on Multicores | 2025 | SIGMOD | 5.093636e-05 |
| 10,873 | Unraveling the Impact of Window Semantics: Optimizing Join Order for Efficient Stream Processing | 2025 | VLDB | 5.093636e-05 |
| 11,159 | PECJ: Stream Window Join on Disorder Data Streams with Proactive Error Compensation | 2024 | SIGMOD | 5.093636e-05 |
| 11,181 | Low-Latency Adaptive Distributed Stream Join System Based on a Flexible Join Model | 2024 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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