References
Cherniack M, Balakrishnan H, Balazinska M, Carney D, Çetintemel U, Xing Y, Zdonik S B. Scalable distributed stream processing. In: Proceedings of the Conference on Innovative Data Systems Research. 2003
Kloudas K, Mamede M, Preguiça N, Rodrigues R. Pixida: optimizing data parallel jobs in wide-area data analytics. Proceedings of theVLDB Endowment, 2015, 9(2): 72–83
Rupprecht L, Culhane W, Pietzuch P. Squirreljoin: network-aware distributed join processing with lazy partitioning. Proceedings of the VLDB Endowment, 2017, 10(11): 1250–1261
Yi L, Shanbhag A A, Jindal A, Madden S R. AdaptDB: adaptive partitioning for distributed joins. Proceedings of the VLDB Endowment, 2017, 10(5): 589–600
Li T, Xu Z, Tang T, Wang Y. Model-free control for distributed stream data processing using deep reinforcement learning. Proceedings of the VLDB Endowment, 2018, 11(6): 705–718
Ammar K, Mcsherry F, Salihoglu S, Joglekar M. Distributed evaluation of subgraph queries using worstcase optimal lowmemory dataflows. Proceedings of the VLDB Endowment, 2018, 11(6): 691–704
Kathuria T, Sudarshan S. Efficient and provable multi-query optimization. In: Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. 2017, 53–67
Acknowledgements
This project was supported by Key Research and Development Program (2018YFB1003403), the National Natural Science Foundation of China (Grant Nos. 61732014, 61672432, 61672434) and Natural Science Basic Research Plan in Shaanxi Province of China (2017JM6104).
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Gao, J., Li, Z., Liu, W. et al. A new fragments allocating method for join query in distributed database. Front. Comput. Sci. 14, 144608 (2020). https://doi.org/10.1007/s11704-019-9032-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11704-019-9032-1