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<div class="PlainText">This is an announcement of Xi Liang's Dissertation Defense.<br>
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Candidate: Xi Liang<br>
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Date: Monday, March 07, 2022<br>
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Time: 10 am CST<br>
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Remote Location: <a href="https://zoom.us/j/92237751551?pwd=VFh4bXVKQzVacXlSSU9QVHo3VTFuZz09">
https://zoom.us/j/92237751551?pwd=VFh4bXVKQzVacXlSSU9QVHo3VTFuZz09</a> Passcode: 1XaqE8<br>
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Title: Synopses for Efficient and Reliable Approximate Query Processing<br>
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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.<br>
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In this thesis, we propose novel AQP techniques of different characteristics for different scenarios where AQP can be useful. We first discuss PASS, a system that combines sampling and aggregation for better accuracy while keeping the latency and storage cost
at a favorable level. As a follow-up of PASS, we present JanusAQP, a dynamic AQP system that extends the static partition tree proposed in PASS and addresses several challenges in a dynamic environment that make the system more practical. Thirdly, we propose
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. Lastly, we discuss DQM, our effort in applying machine learning to manage materialized views
in a robust manner. <br>
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Advisors: Sanjay Krishnan<br>
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Committee Members: Aaron Elmore, Sanjay Krishnan, and Raul Castro Fernandez<br>
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