From ae552423e36a12a458a1719a6607998ccf129a38 Mon Sep 17 00:00:00 2001
From: Douglas Rumbaugh <dbr4@psu.edu>
Date: Tue, 20 May 2025 17:15:37 -0400
Subject: updates

---
 chapters/introduction.tex | 44 ++++++++++++--------------------------------
 1 file changed, 12 insertions(+), 32 deletions(-)

(limited to 'chapters/introduction.tex')

diff --git a/chapters/introduction.tex b/chapters/introduction.tex
index bdde070..75fad37 100644
--- a/chapters/introduction.tex
+++ b/chapters/introduction.tex
@@ -22,38 +22,18 @@ point-lookups~\cite{mysql-btree-hash, cowbook}.
 This situation is unfortunate, because one of the major challenges
 currently facing data systems is the processing of complex analytical
 queries of varying types over large sets of data. These queries and
-data types are supported, nominally, by a relational database, but
-are not well addressed by existing indexing techniques and as a result
-have horrible performance. This has led to the development of a variety
-of specialized systems for particular types of query, such as spatial
-systems~\cite{postgis-doc}, vector databases~\cite{pinecone-db}, and
-graph databases~\cite{neptune, neo4j}.
-
-
-
-
-
-
-however the cost of this flexibility is
-
-Modern relational database systems are based upon the fundamental data
-
-
-highly optimized for addressing
-particular types of search problems, such as point lookups and range
-queries. 
-
-One of the major challenges facing current data systems is the processing
-of complex and varied analytical queries over vast data sets. One commonly
-used technique for accelerating these queries is the application of data
-structures to create indexes, which are the basis for specialized database
-systems and data processing libraries. Unfortunately, the development
-of these indexes is difficult because of the requirements placed on
-them by data processing systems. Data is frequently subject to updates,
-yet a large number of potentially useful data structures are static.
-Further, many large-scale data processing systems are highly concurrent,
-which increases the barrier to entry even further. The process for
-developing data structures that satisfy these requirements is arduous.
+data types are supported, nominally, by a relational database, but are
+not well addressed by existing indexing techniques and as a result have
+horrible performance. This has led to the development of a variety of
+specialized systems for particular types of query, such as spatial
+systems~\cite{postgis-doc}, vector databases~\cite{pinecone-db},
+and graph databases~\cite{neptune, neo4j}.  The development of these
+indexes is difficult because of the requirements placed on them by data
+processing systems. Data is frequently subject to updates, yet a large
+number of potentially useful data structures are static.  Further,
+many large-scale data processing systems are highly concurrent, which
+increases the barrier to entry even further. The process for developing
+data structures that satisfy these requirements is arduous.
 
 To demonstrate this difficulty, consder the recent example of the
 evolution of learned indexes. These are data structures designed to
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