About

I am a postdoc from the University of Amsterdam in the Netherland at the Intelligent Data Engineering Lab (INDELab). I achieved my Ph.D. degree at Huazhong University of Science and Technology in China. I have been an internship at Tencent Inc. as part of the Intelligent Cloud Storage Joint Research center of HUST and a visiting Ph.D. scholar at the Center for Data Science at New York University. My research focuses on using Artificial Intelligence (AI) for data management and storage systems.

News

June 1, 2020
xI am selected as the DAC 2020 Young Fellows program.

May 30, 2020
xOur work on "AlphaJoin: Join Order Selection à la AlphaGoo" has been accepted by PW@VLDB (VLDB 2020). Thanks for Kun Wan's help with this work.

May 28, 2020
xI will conduct a postdoctoral study on “Enhancing data processing systems with artificial intelligence” in association with the Intelligent Data Engineering Lab at the University of Amsterdam, starting from June 2020. The goal of my postdoctoral study is to investigate the impact of artifical intelligence technology for data management and storage systems to increase both their efficiency and user-friendliness. The work will encompass addressing problems related to performance optimization, workload prediction and failure detection.

April 25, 2020
xOur work on "HDDse: Enabling High-Dimensional Disk State Embedding for Generic Failure Detection System of Heterogeneous Disks in Large Data Centers" has been accepted by USENIX Annual Technical Conference (ATC 2020).

March 21, 2020
xOur work on "Minority Disk Failure Prediction based on Transfer Learning in Large Data Centers of Heterogeneous Disk Systems" has been accepted by IEEE Transactions on Parallel and Distributed Systems (TPDS).

February 11, 2020
Our work on "Tier-Scrubbing: An Adaptive and Tiered Disk Scrubbing Scheme with Improved MTTD and Reduced Cost" has been accepted as full paper at DAC 2020.

January 27, 2020
I am selected as a winner of Student Travel Award for FAST 2020.

January 23, 2020
Our work on "Tier-Scrubbing: An Adaptive and Tiered Disk Scrubbing Scheme" has been accepted as WIP and Poster at FAST 2020.

January 16, 2020
I am selected as a winner of Student Travel Award for FPGA 2020.

January 14, 2020
Our work on "Exploring Monte Carlo Tree Search for Join Order Selection" has been accepted as a poster at NEDB day 2020.

October 8, 2019
Being a visitor scholar in Center for Data Science of New York University for 6 months.

March 27, 2019
Our paper on "Transfer Learning based Failure Prediction for Minority Disks in Large Data Centers of Heterogeneous Disk Systems" got accepted at ICPP 2019. [PDF]

April 25, 2019
I am selected as an SRC Winner (Student Travel Award) for SIGMOD 2019.

November 21, 2018
Our paper on "An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning" got accepted at SIGMOD 2019. [PDF]

Publications

[1] Ji Zhang. AlphaJoin: Join Order Selection à la AlphaGo. The 46th International Conference on Very Large Data Bases (PW@VLDB), 2020, Tokyo, Japan, Aug 31 -Sep 4, 2020. [Full Paper] (CORE A*, CCF A)

[2] Ji Zhang, Ke Zhou, Ping Huang, Ming Xie, Sebastian Schelter. HDDse: Enabling High-Dimensional Disk State Embedding for Generic Failure Detection System of Heterogeneous Disks in Large Data Centers. USENIX Annual Technical Conference (ATC 2020). [Full Paper] (CORE A, CCF A)

[3] Ji Zhang, Ke Zhou, Ping Huang, Xubin He, Ming Xie, Bin Cheng, Yongguang Ji, Yinhu Wang. Minority Disk Failure Prediction based on Transfer Learning in Large Data Centers of Heterogeneous Disk Systems. IEEE Transactions on Parallel and Distributed Systems (TPDS). [Full Paper] (CCF A)

[4] Ji Zhang, Yuanzhang Wang, Yangtao Wang, Ke Zhou, Schelter Sebastian, Ping Huang, Bin Cheng and Yongguang Ji. Tier-Scrubbing: An Adaptive and Tiered Disk Scrubbing Scheme with Improved MTTD and Reduced Cost. 57th ACM/EDAC/IEEE Design Automation Conference (DAC) 2020, San Francisco, CA, USA, July 19-23, 2020. [Full Paper] (CORE A, CCF A)

[5] Ji Zhang, Ke Zhou, Ping Huang, Sebastian Schelter, Bin Cheng, Yongguang Ji. Tier-Scrubbing: An Adaptive and Tiered Disk Scrubbing Scheme. 18th USENIX Conference on File and Storage Technologies (FAST) 2020, Santa Clara, USA, Feburary 24-27, 2020. [WIP and Poster] (CORE A, CCF A)

[6] Ji Zhang, Kun Wan, Sebastian Schelter, Ke Zhou. Exploring Monte Carlo Tree Search for Join Order Selection. North East Database Day (NEDB) 2020, Boston, USA, January 27, 2020. [Poster]

[7] Ji Zhang, Ke Zhou, Ping Huang, Xubin He, Zhili Xiao, Bin Cheng, Yongguang Ji. Transfer Learning based Failure Prediction for Minority Disks in Large Data Centers of Heterogeneous Disk Systems. International Conference on Parallel Processing (ICPP) 2019, Kyoto, Japan, August 5-August 8, 2019. [Full Paper] (CORE A, CCF B)

[8] Ji Zhang, Yu Liu, Ke Zhou*, Guoliang Li, ZhiLi Xiao, Bin Cheng, JiaShu Xing .. An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning. ACM International Conference on Management of Data (SIGMOD) 2019, Amsterdam, Netherlands, June 30 - July 5, 2019. [Full Paper] (CORE A*, CCF A)

Awards

June 1, 2020
xI am selected as the DAC 2020 Young Fellows program.

February 27, 2020
xI am selected as a winner of Student Travel Award for FAST 2020.

January 16, 2020
xI am selected as a winner of Student Travel Award for FPGA 2020.

September 26, 2019
I am awarded by Tencent Inc. for Outstanding Contribution.

April 25, 2019
I am selected as an SRC Winner (Student Travel Award) for SIGMOD 2019.

November 16, 2018
The Academic Star of HUST

October 10, 2018
Model Student of Outstanding Capacity in HUST

July 06, 2018
Excellent Patent in Tencent Inc.

May 13, 2017
Tencent Inc. Knowledge Award