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PFunk-H

Daniel Alabi, Eugene Wu
<span title="">2016</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vxrc3vebzzachiwy3nopwi3h5u" style="color: black;">Proceedings of the Workshop on Human-In-the-Loop Data Analytics - HILDA &#39;16</a> </i> &nbsp;
This paper explores a preliminary model of sampling-based approximate query processing that uses perceptual models (encoded as functions) to construct approximate answers intended for visualization.  ...  To bridge the gap between data sizes and interactivity, many visualization systems have turned to sampling-based approximate query processing frameworks.  ...  The introduction of PFunk-H and our preliminary experiments is an initial foray into the use of perceptual models in approximate query processing.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2939502.2939512">doi:10.1145/2939502.2939512</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/sigmod/Alabi016.html">dblp:conf/sigmod/Alabi016</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ljsxzzcuzzgazob2foafljm2i4">fatcat:ljsxzzcuzzgazob2foafljm2i4</a> </span>
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