Yingjun Wu

School of Computing, National University of Singapore
yingjun@comp.nus.edu.sg


This homepage is no longer updated. Please visit my new homepage here.

About Me

I am currently a Ph.D. candidate (start from 2012 Fall) at the Database Group, National University of Singapore (advisor: Kian-Lee Tan). Previously, I was a visiting research scholar at the Database Group, Carnegie Mellon University (host advisor: Andrew Pavlo), a research intern at the System Group, Microsoft Research Asia, and a research intern at the Cloud Infrastructure Group, EMC Labs China. Before joining NUS, I earned my bachelor degree from South China University of Technology in 2012.

Research Interests

My research focuses on designing and implementing high performance database management systems (DBMSs) with modern hardware support. The ultimate goal is to enable DBMSs to achieve scalable on-line transaction processing in the main-memory and multi-core settings. This requires a complete redesign of several major DBMS components, including concurrency control, storage management, and fault tolerance.

I am enthusiastic to integrate research into real-world systems, and I am the key developer of two main-memory DBMSs, namely Cavalia and Peloton.

I also have particular interests in designing and implementing distributed systems.

Active Projects

Peloton: The Self-Driving Database Management System. [project]

Cavalia: A Transactional Main-Memory Database on Multicores. [project]

PhD Thesis

Title: Transaction Management In Multi-Core Main-Memory Database Systems. [thesis]
Thesis Committee: Bingsheng He, Yong Meng Teo, Alan Fekete.

Publications

Fast Failure Recovery for Main-Memory DBMSs on Multicores. [paper]
Yingjun Wu, Wentian Guo, Chee-Yong Chan, and Kian-Lee Tan.
SIGMOD 2017.

An Empirical Evaluation of In-Memory Multi-Version Concurrency Control. [paper]
Yingjun Wu, Joy Arulraj, Jiexi Lin, Ran Xian, and Andrew Pavlo.
VLDB 2017.

Self-Driving Database Management Systems. [paper]
Andrew Pavlo, Gustavo Angulo, Joy Arulraj, Haibin Lin, Jiexi Lin, Lin Ma, Prashanth Menon, Todd Mowry, Matthew Perron, Ian Quah, Siddharth Santurkar, Anthony Tomasic, Skye Toor, Dana Van Aken, Ziqi Wang, Yingjun Wu, Ran Xian, and Tieying Zhang.
CIDR 2017.

Transaction Healing: Scaling Optimistic Concurrency Control on Multicores. [paper]
Yingjun Wu, Chee-Yong Chan, and Kian-Lee Tan.
SIGMOD 2016.

Scalable In-Memory Transaction Processing with HTM. [paper] [website]
Yingjun Wu and Kian-Lee Tan.
USENIX ATC 2016.

ChronoStream: Elastic Stateful Stream Computation in the Cloud. [paper]
Yingjun Wu and Kian-Lee Tan.
ICDE 2015.

SocialTransfer: Transferring Social Knowledge for Cold-Start Crowdsourcing. [paper]
Zhou Zhao, James Cheng, Furu Wei, Ming Zhou, Wilfred Ng, and Yingjun Wu.
CIKM 2014.

Grand challenge: SPRINT Stream Processing Engine as a Solution. [paper]
Yingjun Wu, David Maier, and Kian-Lee Tan.
DEBS 2013. (Best Paper Award)

Understanding the Effects of Hypervisor I/O Scheduling for Virtual Machine Performance Interference. [paper]
Ziye Yang, Haifeng Fang, Yingjun Wu, Chunqi Li, Bin Zhao, and H. Howie Huang.
CloudCom 2012.

Invited Talks

Optimization Of OLTP Database Systems Through Program Analysis.
Carnegie Mellon University, Pittsburgh, PA, USA, May 2017. [link]
Brown University, Providence, RI, USA, May 2017.

Building Faster Main-Memory Database Management Systems on Multicores.
National University of Singapore, Singapore, October 2016. [link]

This is the Best Paper Ever on In-Memory Multi-Version Concurrency Control.
Carnegie Mellon University, Pittsburgh, PA, USA, September 2016. [link]

Scalable In-Memory Transaction Processing with HTM.
Carnegie Mellon University, Pittsburgh, PA, USA, June 2016.

Transaction Healing: Scaling Optimistic Concurrency Control on Multicores.
Carnegie Mellon University, Pittsburgh, PA, USA, March 2016. [link]

ChronoStream: Elastic Stateful Stream Computation in the Cloud.
National University of Singapore, Singapore, May 2015.

Teaching

CS3103 - Computer Networks and Protocols (AY14-15).





Last update: May 30th, 2017