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poster

Towards Building The Next Generation Computation Engine

Published: 25 September 2023 Publication History

Abstract

In this poster, I first briefly introduce several system work in the Database Research Group at Southern University of Science and Technology, (i.e., DBGroup@SUSTech); I next present the key ideas of our on-going project, i.e., architecting the next generation computation engine, which is designed for data-intensive applications on heterogeneous computing environment.

References

[1]
2023. QEVIS repository. https://github.com/vis4db/qevis
[2]
Haotian Liu, Bo Tang, and others.2022. GHive: A Demonstration of GPU-Accelerated Query Processing in Apache Hive. In SIGMOD. 2417–2420.
[3]
Haotian Liu, Bo Tang, and others.2022. GHive: Accelerating Analytical Query Processing in Apache Hive via CPU-GPU Heterogeneous Computing. In SoCC. 158–172.
[4]
Kyriakos Mouratidis and Bo Tang. 2018. Exact processing of uncertain top-k queries in multi-criteria settings. PVLDB 11, 8 (2018), 866–879.
[5]
Qiaomu Shen, Zhenxin You, Xiao Yan, Chaozu Zhang, Ke Xu, Jianbin Qin, Dan Zeng, and Bo Tang. 2023. QEVIS: Multi-grained Visualization of Distributed Query Execution. IEEE VIS (conditionally accepted) (2023).
[6]
Bo Tang, Kyriakos Mouratidis, and Man Lung Yiu. 2017. Determining the impact regions of competing options in preference space. In SIGMOD. 805–820.
[7]
Bo Tang, Man Lung Yiu, and Kien A Hua. 2016. Exploit every bit: Effective caching for high-dimensional nearest neighbor search. TKDE 28, 5 (2016), 1175–1188.
[8]
Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, and Kai Wang. 2017. Efficient motif discovery in spatial trajectories using discrete Fréchet distance. In EDBT, Vol. 24. 378–389.
[9]
Fang Wang, Xiao Yan, Man Lung Yiu, Shuai LI, Zunyao Mao, and Bo Tang. 2023. Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation. Proceedings of the ACM on Management of Data 1, 1 (2023), 1–25.

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ACM TURC '23: Proceedings of the ACM Turing Award Celebration Conference - China 2023
July 2023
173 pages
ISBN:9798400702334
DOI:10.1145/3603165
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 25 September 2023

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