[CS] Xi Liang Candidacy Exam/May 20, 2021

[pbaclawski at uchicago.edu]

This is an announcement of Xi Liang's Candidacy Exam.
===============================================
Date: Thursday, May 20, 2021

Time: 9:00AM CST

Location: via zoom

https://zoom.us/j/93530622372?pwd=bUdId0NrU2lPSUVQd3E4WnpzWWwyUT09 
Password: cmX7Xx

Candidacy Candidate: Xi Liang

Title: Synopsis for Efficient and Reliable Approximate Query Processing

Abstract: Answering queries accurately at interactive speeds has become more challenging in modern data systems due to the massive growth of data. Such challenges lead to an increasing interest in Approximate Query Processing (AQP) techniques because they enable timely query execution in scenarios that can tolerate some degree of inaccuracy. While latency and accuracy have been the two main factors considered by many AQP systems, in our studies, we found other dimensions like applicability, reliability, robustness and data availability, etc. could also be the main considerations in certain scenarios and such demands call for the design of novel AQP techniques.

In our research, we propose three new AQP techniques designed with favorable trade-off profiles: 1) PASS, a system that combines sampling and aggregation for better accuracy while keeping the latency and storage cost at a favorable level; 2) PC, a novel missing-data analysis framework that not only enables a presentation of missing-data but also the derivation of a tight hardbound for optimal reliability; 3) DQM, a system that uses deep reinforcement learning for robust opportunistic view materialization as workloads evolving.

Advisor: Sanjay Krishnan

Committee Members: Aaron Elmore, Sanjay Krishnan, and Raul Castro Fernandez