A new fragments allocating method for join query in distributed database
Jintao GAO, Zhanhuai LI, Wenjie LIU, Zhijun GUO, Yantao YUE
A new fragments allocating method for join query in distributed database
[1] |
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
|
[2] |
Kloudas K, Mamede M, Preguiça N, Rodrigues R. Pixida: optimizing data parallel jobs in wide-area data analytics. Proceedings of the VLDB Endowment, 2015, 9(2): 72–83
CrossRef
Google scholar
|
[3] |
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
CrossRef
Google scholar
|
[4] |
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
CrossRef
Google scholar
|
[5] |
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
CrossRef
Google scholar
|
[6] |
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
CrossRef
Google scholar
|
[7] |
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
CrossRef
Google scholar
|
/
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