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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

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2018 Impact Factor: 1.129

Front. Comput. Sci.    2020, Vol. 14 Issue (4) : 144608    https://doi.org/10.1007/s11704-019-9032-1
LETTER
A new fragments allocating method for join query in distributed database
Jintao GAO1(), Zhanhuai LI1, Wenjie LIU1, Zhijun GUO2, Yantao YUE2
1. School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
2. Bank of Communications, Shanghai 201201, China
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Corresponding Author(s): Jintao GAO   
Just Accepted Date: 15 October 2019   Issue Date: 11 March 2020
 Cite this article:   
Jintao GAO,Zhanhuai LI,Wenjie LIU, et al. A new fragments allocating method for join query in distributed database[J]. Front. Comput. Sci., 2020, 14(4): 144608.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-019-9032-1
https://academic.hep.com.cn/fcs/EN/Y2020/V14/I4/144608
1 M Cherniack, H Balakrishnan, M Balazinska , Carney D, Çetintemel U, Xing Y, Zdonik S B. Scalable distributed stream processing. In: Proceedings of the Conference on Innovative Data Systems Research. 2003
2 K Kloudas, M Mamede, N Preguiça, R Rodrigues. Pixida: optimizing data parallel jobs in wide-area data analytics. Proceedings of the VLDB Endowment, 2015, 9(2): 72–83
https://doi.org/10.14778/2850578.2850582
3 L, Rupprecht W Culhane, P Pietzuch. Squirreljoin: network-aware distributed join processing with lazy partitioning. Proceedings of the VLDB Endowment, 2017, 10(11): 1250–1261
https://doi.org/10.14778/3137628.3137636
4 L Yi, A A, Shanbhag A Jindal, S R Madden. AdaptDB: adaptive partitioning for distributed joins. Proceedings of the VLDB Endowment, 2017, 10(5): 589–600
https://doi.org/10.14778/3055540.3055551
5 T Li, Z Xu, T Tang, Y Wang. Model-free control for distributed stream data processing using deep reinforcement learning. Proceedings of the VLDB Endowment, 2018, 11(6): 705–718
https://doi.org/10.14778/3199517.3199521
6 K Ammar, F, Mcsherry S Salihoglu, M Joglekar. Distributed evaluation of subgraph queries using worstcase optimal lowmemory dataflows. Proceedings of the VLDB Endowment, 2018, 11(6): 691–704
https://doi.org/10.14778/3199517.3199520
7 T Kathuria, S Sudarshan. Efficient and provable multi-query optimization. In: Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. 2017, 53–67
https://doi.org/10.1145/3034786.3034792
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