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 \section{Conclusion}
 \label{sec:conclusion}
 
-This chapter discussed the creation of a framework for the dynamic extension of
-static indexes designed for various sampling problems. Specifically, extensions
-were created for the alias structure (WSS), the in-memory ISAM tree (IRS), and
-the alias-augmented B+tree (WIRS). In each case, the SSIs were extended
-successfully with support for updates and deletes, without compromising their
-sampling performance advantage relative to existing dynamic baselines. This was
-accomplished by leveraging ideas borrowed from the Bentley-Saxe method and the
-design space of the LSM tree to divide the static index into multiple shards,
-which could be individually reconstructed in a systematic fashion to
-accommodate new data. This framework provides a large design space for trading
-between update performance, sampling performance, and memory usage, which was
-explored experimentally. The resulting extended indexes were shown to approach
-or match the insertion performance of the B+tree, while simultaneously
-performing significantly faster in sampling operations under most situations.
+In this chapter, we discussed the creation of a dynamization system
+based
+ upon the Bentley-Saxe method that can be used to create dynamized
+sampling
+ data structures that outperform dynamic baselines and feature a
+configurable
+ design space. Specifically, we discussed dynamized versions
+of the alias
+ structure for weighted set sampling, the alias-augmented
+B+tree for
+ weighted independent range sampling, and the ISAM tree for
+independent
+ range sampling. In each case, the static structures were
+dynamized with
+ support for inserts and deletes without compromising
+their query performance
+ advantage over dynamic baselines, and while
+matching or exceeding the dynamic structures' insertion performance.
+
+The techniques proposed in chapter, however, are limited to a very
+specific class
+ of data structures for addressing a very specific type
+of search problem. While these results are promising, they fall short
+of a general
+ solution to data structure dynamization that addresses
+the
+ limitations of classical dynamization techniques discussed in
+Chapter~\ref{chap:background}. In the next chapter, we will take several
+of the results of this chapter, generalize them, and apply them to a much
+wider range of data structures.