updates

4 files changed, 118 insertions, 9 deletions

diff --git a/chapters/design-space.tex b/chapters/design-space.tex
index c8876de..f85883c 100644
--- a/chapters/design-space.tex
+++ b/chapters/design-space.tex
@@ -642,10 +642,7 @@ values themselves, and so this is not a significant limitation
 for our analysis.  The resulting distributions are shown in
 Figure~\ref{fig:design-policy-ins-latency}. These distributions
 are representing using a ``reversed'' CDF with log scaling on both
-axes. This representation has proven very useful for interpreting the
-latency distributions that we see in evaluating dynamization, but is
-slightly unusual, and so we've included a guide to interpreting these
-charts in Appendix~\ref{append:rcdf}.
+axes. 
 
 \begin{figure}
 \centering
diff --git a/chapters/related-works.tex b/chapters/related-works.tex
index e828593..8e24750 100644
--- a/chapters/related-works.tex
+++ b/chapters/related-works.tex
@@ -274,10 +274,76 @@ partial compactions to reduce the size of reconstructions. These factors
 let SILK maintain the LSM tree structure without having to resort to
 insertion throttling, as we do in our system. 
 
-\section{GiST and GIN}
-
 \section{Automated Index Composition}
-\subsection{Periodic Table of Data Structures, etc.}
-\subsection{Gene}
 
+At the beginning of this work, we described what we see as the three
+major lines of work that are attempting to partially automate index
+design. The first of these we discussed was something we called automated
+index composition. This is an approach to index design which uses a series
+of data structure primitives to compose an index over a set of data that
+has been optimized for a particular workload. Of these works, \emph{all}
+of them are focused explicitly on single dimensional data with range
+scans and point lookups, and only Cosine supports inserting new records.
+
+Two closely related papers in this line are the so-called Data
+Alchemist~\cite{ds-alchemy} and the Periodic Table of Data
+Structures~\cite{periodic-table}. Both of these works consider
+automatically designing indexes for single dimensional range data,
+capable of addressing point lookups and range scans. The Periodic Table
+of Data Structures proposes a wide design space for data structures
+based on creating individual ''nodes`` over the data, each of which
+have different design decisions applied to them, which are termed
+``first principles''. They consider first principles to be any design
+decision that is irreducible to other decisions, such as whether the
+type of partitioning used (range, radix, etc.), whether  the data is
+stored in a row-based or columnar format, etc. From this model, an
+index can be designed by iteratively describing the first principles of
+each node. Given these first principles, and the set of nodes, access
+algorithms can be automatically devised. The Data Alchemist extends
+this model of data structures with learned cost models and machine
+learning based systems for automatically composing an optimal data
+structure for a given workload. Both of these papers discuss their
+core ideas, but don't include testing of a working system. The same
+authors have two further works in this area that go more into detail on
+cost models~\cite{data-calc} and explore the design continuum in more
+detail~\cite{ds-continuum}.
+
+GENE~\cite{gene} advances the same basic line of research as the
+previously mentioned works, but actually includes a functioning,
+end-to-end index generation system.  GENE decomposes data structures
+in a few specific primitives based upon search algorithm and data
+layout. Specifically, it supports scanning, binary search, interpolation
+search, exponential search, hashing with closed addressing, and linear
+regression modeling, as search algorithms. The supported data layout
+parameters include column vs. row orientation, sorted vs. unsorted
+ordering, compression, and function mapping (which obviates the need
+to make other layout decisions, and is intended to be used with linear
+regression search algorithms). GENE then designs an index based upon
+these options automatically for a given workload by applying a genetic
+algorithm.
+
+Cosine~\cite{cosine} is another similar system, which has been designed
+for cloud based systems and accounts for cloud SLAs and (monetary)
+budgeting concerns. It includes sophisticate cost modeling systems
+to allow it to dynamically adapt the structure of the index, shifting
+elements of it between an LSM-like structure, a B-tree-like structure,
+and an LSH-like (log-structured hash) structure, as well as adjusting
+various configuration parameters associated with each of these primitive
+components. It is particularly notable for being the only work discussed
+in this section that supports updates.
+
+Finally, fluid data structures~\cite{fluid-ds} represents a more formal
+work in this area, based upon immutable data primitives. The core idea of
+this work is that the physical representation of the data can be mutated
+while maintaining its logical ordering, under certain circumstances. This
+allows for regions of the data structure to shift dynamically to optimize
+for particular types of search. To accomplish this, the authors define
+a formal grammar, which they call a compositional organizational grammar
+(COG) as a description of a data structure, and the transformation rules
+that can be applied to a data structure based on this language while
+ensuring logical equivalence. A runtime system, then, can automatically
+apply these transformations to the structure. This is the only of the
+works in this section that discusses generalizing its techniques to
+non-single dimensional data.
 
+\section{Generalized Index Templates}
diff --git a/paper.tex b/paper.tex
index 7ff5bd8..42ef40b 100644
--- a/paper.tex
+++ b/paper.tex
@@ -395,7 +395,7 @@ of Engineering Science and Mechanics
 % lines that redefine the \thechapter and \thesection:
 %\renewcommand\thechapter{}
 %\renewcommand\thesection{\arabic{section}}
-\input{appendices/reverse-cdf.tex}
+%\input{appendices/reverse-cdf.tex}
 % \include{Appendix-B/Appendix-B}
 % \include{Appendix-C/Appendix-C}
 % \include{Appendix-D/Appendix-D}
diff --git a/references/references.bib b/references/references.bib
index 297f67b..8a3049f 100644
--- a/references/references.bib
+++ b/references/references.bib
@@ -2119,3 +2119,49 @@ keywords = {database systems, low-latency transactions, preemptive scheduling, u
   bibsource    = {dblp computer science bibliography, https://dblp.org}
 }
 
+@inproceedings{ds-continuum,
+  author       = {Stratos Idreos and
+                  Niv Dayan and
+                  Wilson Qin and
+                  Mali Akmanalp and
+                  Sophie Hilgard and
+                  Andrew Ross and
+                  James Lennon and
+                  Varun Jain and
+                  Harshita Gupta and
+                  David Li and
+                  Zichen Zhu},
+  title        = {Design Continuums and the Path Toward Self-Designing Key-Value Stores
+                  that Know and Learn},
+  booktitle    = {9th Biennial Conference on Innovative Data Systems Research, {CIDR}
+                  2019, Asilomar, CA, USA, January 13-16, 2019, Online Proceedings},
+  publisher    = {www.cidrdb.org},
+  year         = {2019},
+  url          = {http://cidrdb.org/cidr2019/papers/p143-idreos-cidr19.pdf},
+  timestamp    = {Mon, 18 Jul 2022 17:13:00 +0200},
+  biburl       = {https://dblp.org/rec/conf/cidr/IdreosDQAHRLJGL19.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+ 
+@inproceedings{data-calc,
+  author       = {Stratos Idreos and
+                  Kostas Zoumpatianos and
+                  Brian Hentschel and
+                  Michael S. Kester and
+                  Demi Guo},
+  editor       = {Gautam Das and
+                  Christopher M. Jermaine and
+                  Philip A. Bernstein},
+  title        = {The Data Calculator: Data Structure Design and Cost Synthesis from
+                  First Principles and Learned Cost Models},
+  booktitle    = {Proceedings of the 2018 International Conference on Management of
+                  Data, {SIGMOD} Conference 2018, Houston, TX, USA, June 10-15, 2018},
+  pages        = {535--550},
+  publisher    = {{ACM}},
+  year         = {2018},
+  url          = {https://doi.org/10.1145/3183713.3199671},
+  doi          = {10.1145/3183713.3199671},
+  timestamp    = {Wed, 21 Nov 2018 12:44:08 +0100},
+  biburl       = {https://dblp.org/rec/conf/sigmod/IdreosZHKG18.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}