From 0dc1a8ea20820168149cedaa14e223d4d31dc4b6 Mon Sep 17 00:00:00 2001
From: "Douglas B. Rumbaugh" <doug@douglasrumbaugh.com>
Date: Sun, 6 Jul 2025 18:21:32 -0400
Subject: updates

---
 chapters/sigmod23/background.tex | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'chapters/sigmod23/background.tex')

diff --git a/chapters/sigmod23/background.tex b/chapters/sigmod23/background.tex
index 984e36c..8d3a88f 100644
--- a/chapters/sigmod23/background.tex
+++ b/chapters/sigmod23/background.tex
@@ -21,8 +21,8 @@ set; the specific usage should be clear from context.
 In each of the problems considered, sampling can be performed either
 with-replacement or without-replacement. Sampling with-replacement
 means that a record that has been included in the sample set for a given
-sampling query is "replaced" into the dataset and allowed to be sampled
-again. Sampling without-replacement does not "replace" the record,
+sampling query is ``replaced'' into the dataset and allowed to be sampled
+again. Sampling without-replacement does not ``replace'' the record,
 and so each individual record can only be included within the a sample
 set once for a given query. The data structures that will be discussed
 support sampling with-replacement, and sampling without-replacement can
@@ -38,7 +38,7 @@ in the sample set to match the distribution of source data set. This
 requires that the sampling of a record does not affect the probability of
 any other record being sampled in the future. Such sample sets are said
 to be drawn i.i.d (independently and identically distributed). Throughout
-this chapter, the term "independent" will be used to describe both
+this chapter, the term ``independent'' will be used to describe both
 statistical independence, and identical distribution.
 
 Independence of sample sets is important because many useful statistical
@@ -192,7 +192,7 @@ requiring greater than $k$ traversals to obtain a sample set of size $k$.
 \Paragraph{Static Solutions.}
 There are also a large number of static data structures, which we'll
 call static sampling indices (SSIs) in this chapter,\footnote{
-  We used the term "SSI" in the original paper on which this chapter
+  We used the term ``SSI'' in the original paper on which this chapter
   is based, which was published prior to our realization that a strong
   distinction between an index and a data structure would be useful. I
   am retaining the term SSI in this chapter for consistency with the
-- 
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