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SieveJoin: Boosting Multi-way Joins by Filtering Unneeded Intermediate Results
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Renrui Li1, Qingzhi Ma1, Xiaomeng Shi2, An Liu1
Data Science and Engineering | 2026, 11(1) : 143 - 154
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Data Science and Engineering | 2026, 11(1): 143-154
RESEARCH PAPERS
SieveJoin: Boosting Multi-way Joins by Filtering Unneeded Intermediate Results
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Renrui Li1, Qingzhi Ma1, Xiaomeng Shi2, An Liu1
Affiliations
  • 1School of Computer Science and Technology, Soochow University, Suzhou, China
  • 2IBSS, Xi'an Jiaotong-Liverpool University, Suzhou, China
Published: 2026-03-01 doi: 10.1007/s41019-025-00325-7
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Improving the performance of data systems for join operations has long been a critical challenge. Recently, substantial attention has been focused on optimizing multi-way join performance, particularly in reducing the overhead caused by generating intermediate tuples that do not contribute to the final result. In this paper, we propose a novel algorithm called SieveJoin, which extends the established Bloomjoin approach to support multi-way joins. SieveJoin sets a new benchmark for the efficiency of join query execution. A key innovation of SieveJoin is its ability to propagate Bloom filters along the join path, allowing the system to terminate early and avoid producing superfluous intermediate results. The primary design objective of SieveJoin is to efficiently estimate join results using Bloom filters, while maintaining minimal memory overhead. We analyze the bottlenecks associated with deferred multi-way joins and detail how Bloom filters are utilized to suppress the creation of redundant intermediate tuples. To assess the effectiveness of SieveJoin, we conduct a comprehensive experimental evaluation using the TPC-H benchmark, citation datasets, and a synthetic dataset. Our results compare SieveJoin with a state-of-the-art column-store database and a worst-case optimal join algorithm, highlighting its advantages in both response time and memory usage.

Multi-way join  /  Worst-case optimal join  /  Query processing  /  Bloom filter
Renrui Li, Qingzhi Ma, Xiaomeng Shi, An Liu. SieveJoin: Boosting Multi-way Joins by Filtering Unneeded Intermediate Results[J]. Data Science and Engineering, 2026 , 11 (1) : 143 -154 . DOI: 10.1007/s41019-025-00325-7
  • Natural Science Foundation of China(62272332)
  • Priority Academic Program Development of Jiangsu Higher Education Institutions
Year 2026 volume 11 Issue 1
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Article Info
doi: 10.1007/s41019-025-00325-7
  • Receive Date:2025-08-02
  • Online Date:2026-08-06
  • Published:2026-03-01
Article Data
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History
  • Received:2025-08-02
  • Revised:2025-10-10
  • Accepted:2025-10-28
Funding
Natural Science Foundation of China(62272332)
Priority Academic Program Development of Jiangsu Higher Education Institutions
Affiliations
    1School of Computer Science and Technology, Soochow University, Suzhou, China
    2IBSS, Xi'an Jiaotong-Liverpool University, Suzhou, China

Corresponding:

Qingzhi Ma 
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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种数
Number of
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占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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