From 5e4ad2777acc4c2420514e39fb98b7cf2e200996 Mon Sep 17 00:00:00 2001
From: Douglas Rumbaugh <dbr4@psu.edu>
Date: Sun, 27 Apr 2025 17:36:57 -0400
Subject: Initial commit

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+\section{Introduction} \label{sec:intro} 
+
+As a first attempt at realizing a dynamic extension framework, one of the
+non-decomposable search problems discussed in the previous chapter was
+considered: independent range sampling, along with a number of other
+independent sampling problems. These sorts of queries are important in a
+variety of contexts, including including approximate query processing
+(AQP)~\cite{blinkdb,quickr,verdict,cohen23}, interactive data
+exploration~\cite{sps,xie21}, financial audit sampling~\cite{olken-thesis}, and
+feature selection for machine learning~\cite{ml-sampling}. However, they are
+not well served using existing techniques, which tend to sacrifice statistical
+independence for performance, or vise versa. In this chapter, a solution for
+independent sampling is presented that manages to achieve both statistical
+independence, and good performance, by designing a Bentley-Saxe inspired
+framework for introducing update support to efficient static sampling data
+structures. It seeks to demonstrate the viability of Bentley-Saxe as the basis
+for adding update support to data structures, as well as showing that the
+limitations of the decomposable search problem abstraction can be overcome
+through alternative query processing techniques to preserve good
+performance.
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