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+\chapter{Conclusion}
+\label{chap:conclusion}
+
+Using data structures, a wide range of analytical queries against large data
+sets can be accelerated. Unfortunately, these data structures must be
+concurrently updatable to ensure timely results, as the underlying data is
+frequently subject to change. This requirement for concurrent update support
+excludes many possible data structures from use in these contexts, and the
+creation of a data structure with update support is non-trivial.
+
+The framework proposed by this work would allow for existing data
+structures to be automatically extended with tunable support for
+concurrent updates, with potential for future work to add even more
+features. It is based on an extension of the Bentley-Saxe method,
+which supports updates in static structures by splitting the data
+structure into multiple partitions and systematically reconstructing
+them. The Bentley-Saxe method has been adjusted to utilize a different
+query interface, based on the newly proposed extended decomposability,
+which brings with it more efficient support for many types of search
+problems not well served by the original techniques. It also introduces
+two approaches for handling deletes, buffering of inserts, and a more
+tunable reconstruction strategy, as well as support for concurrency,
+none of which were present in the original method.
+
+Using this framework, many data structures and search problems can be
+used as the basis of an index, requiring only that they support the
+eDSP abstraction and can uniquely identify and locate each record. The
+creation of an index requires only a small amount of shim code between
+the structure and the framework (called a shard).
+
+The current version of the framework supports tunable, single-threaded
+updates, and has been experimentally validated to extend static data
+structures with update support, and maintain performance on-par
+with or better than existing dynamic alternatives for a number of
+complex search problems, including k-nearest neighbor and a variety
+of independent sampling problems. Beyond presenting these results,
+this work proposes the extension of this framework with support for
+concurrency with tail-latency mitigations, online and fine-grained
+tuning, and examining more sophisticated data partitioning schemes to
+ease certain challenges associated with large-scale reconstructions.
+The completion of this framework would be a major milestone in a larger
+project to vastly expand the capabilities of database management systems
+through the use of more complex data access primitives.