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authorDouglas Rumbaugh <dbr4@psu.edu>2025-05-13 17:29:40 -0400
committerDouglas Rumbaugh <dbr4@psu.edu>2025-05-13 17:29:40 -0400
commit40bff24fc2e2da57f382e4f49a5ffb7c826bbcfb (patch)
treec00441b058255de08a32d227ce7af46bf11d8eb8 /chapters/sigmod23/extensions.tex
parent5ffc53e69e956054fdefd1fe193e00eee705dcab (diff)
downloaddissertation-40bff24fc2e2da57f382e4f49a5ffb7c826bbcfb.tar.gz
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+++ b/chapters/sigmod23/extensions.tex
@@ -9,7 +9,7 @@ concurrency and external data structures.
\subsection{External Data Structures}
\label{ssec:ext-external}
-Our dynamization techniques can easily accomodate external data structures
+Our dynamization techniques can easily accommodate external data structures
as well as in-memory ones. To demonstrate this, we have implemented
a dynamized version of an external ISAM tree for use in answering IRS
queries. The mutable buffer remains an unsorted array in memory, however
@@ -46,7 +46,7 @@ file or a Spark RDD, and a centralized control node can manage the
mutable buffer. Flushing this buffer would create a new file/RDD, and
reconstructions could likewise be performed by creating new immutable
structures through the merging of existing ones, using the same basic
-scheme as has already been discussed in this chapter. Using thes tools,
+scheme as has already been discussed in this chapter. Using these tools,
SSIs over datasets that exceed the capacity of a single node could be
supported. Such distributed SSIs do exist, such as the RDD-based sampling
structure using in XDB~\cite{li19}.