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<div class="PlainText">This is an announcement of Bruno Barbarioli's Candidacy Exam.<br>
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Candidate: Bruno Barbarioli<br>
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Date: Wednesday, October 26, 2022<br>
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Time: 3:30 pm CST<br>
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Location: JCL 390<br>
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Title: Hierarchical Residual Encoding for Multiresolution Spatial-Temporal Data Compression<br>
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Abstract: Data compression is a key technique for reducing the cost of data transfer from storage to compute nodes. Increasingly, modern data scales necessitate lossy compression techniques, where exactness is sacrificed for a smaller compressed representation.<br>
One challenge in lossy compression is that different applications may have different accuracy demands.<br>
Today's compression techniques struggle in this setting either forcing the user to compress to the strictest accuracy demand, or to re-encode the data at multiple resolutions.<br>
This work proposes a simple, but effective multi-resolution compression algorithm for time series data, where a single encoding can be effectively decompressed at multiple output resolutions.<br>
There are a number of benefits over current state-of-the-art techniques.<br>
(1) The storage footprint of this encoding is smaller than re-encoding the data at multiple resolutions. (2) Similarly, the compression time is generally smaller than re-encoding at multiple resolutions. (3) Finally, the decompression latency with our encoding
is significantly faster than single encodings at the strictest accuracy demand.<br>
We further propose an extension to the time series case, where we also take into account the spatial dependence of certain data sets. We generalize our model to a high dimensional setting with applications ranging from image segmentation to overlapping satellite
images.<br>
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Advisors: Sanjay Krishnan<br>
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Committee Members: Sanjay Krishnan, Aaron Elmore, Raul Castro Fernandez, and Stavros Sintos<br>
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