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authorDouglas B. Rumbaugh <doug@douglasrumbaugh.com>2025-06-01 15:09:25 -0400
committerDouglas B. Rumbaugh <doug@douglasrumbaugh.com>2025-06-01 15:09:25 -0400
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@@ -114,7 +114,7 @@ of problems that will be directly addressed within this chapter.
\subsection{Algorithmic Solutions}
Relational database systems often have native support for IQS using
-SQL's \texttt{TABLESAMPLE} operator~\cite{postgress-doc}. However, the
+SQL's \texttt{TABLESAMPLE} operator~\cite{postgres-doc}. However, the
algorithms used to implement this operator have significant limitations
and do not allow users to maintain statistical independence of the results
without also running the query to be sampled from in full. Thus, users must
@@ -137,7 +137,7 @@ in full anyway before returning only some of the results.\footnote{
For performance, the statistical guarantees can be discarded and
systematic or block sampling used instead. Systematic sampling considers
only a fraction of the rows in the table being sampled from, following
-some particular pattern~\cite{postgress-doc}, and block sampling samples
+some particular pattern~\cite{postgres-doc}, and block sampling samples
entire database pages~\cite{db2-doc}. These allow for query performance
to be decoupled from data size, but tie a given record's inclusion in the
sample set directly to its physical storage location, which can introduce