Introduction:

Name: Xiangdong Huang
Position: Assistant Researcher
Email: sainthxd@gmail.com, huangxdong@tsinghua.edu.cn
Address: 1-105, FIT Building,Tsinghua University, Beijing, China


Education background:

Ph.D. Tsinghua University, Software Engineering, 2012-2017
B.E. Chongqing University, Computer Science, 2008-2012


Experience:

Jul. 2019 – Present, Assistant researcher, School of Software, Tsinghua University
Jul. 2017 – Jul. 2019, Postdoctoral Researcher, School of Software, Tsinghua University
Mar. 2017 – Present, Data Storage Group, National Engineering Laboratory for Big Data Software


Areas of Research Interests/ Research Projects:

Big data management, Time series data management, Apache IoTDB


Academic Achievement:

Journals:
[1] Jialin Qiao, Xiangdong Huang, Jianmin Wang, et al. Dual-PISA: an Index for Aggregation Operations on Time Series Data[J]. Information Systems, 2019.
[2] Xiangdong Huang, Jianmin Wang,  Yu Zhong, et al. Optimizing data partition for scaling out NoSQL cluster[J]. Concurrency and Computation: Practice and Experience, 2015, 27(18): 5793-5809.
[3] Xiangdong Huang, Jianmin Wang, Philips. S. Yu, et al. An experimental study on tuning the consistency of NoSQL systems[J]. Concurrency and Computation: Practice and Experience, 2017, 29(12): e4129.

Conferences:
[1] Jialin Qiao, Xiangdong Huang, Jianmin Wang, et al. Heterogeneous Replicas for Multi-dimensional Data Management[C]// DASFAA 2020
[2] Xiangdong Huang, Jianmin Wang, Jialin Qiao, et al. Performance and replica consistency simulation for quorum-based NoSQL system cassandra[C]//International Conference on Application and Theory of Petri Nets and Concurrency. Springer, Cham, 2017: 78-98.
[3] Xiangdong Huang, Jianmin Wang, Raymond Wong, et al. Pisa: An index for aggregating big time series data[C]//Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2016: 979-988.
[4] Xiangdong Huang, Jianmin Wang, Jian Bai, et al. Inherent replica inconsistency in cassandra[C]//2014 IEEE International Congress on Big Data. IEEE, 2014: 740-747.