From c95b02bd7e0c2f2dee8f2fb1275a36416cdcc768 Mon Sep 17 00:00:00 2001
From: Nicholas Novak <34256932+NickyBoy89@users.noreply.github.com>
Date: Wed, 13 Dec 2023 23:54:33 -0800
Subject: [PATCH] feat: Added start of paper to comps

---
 paper/block-search.drawio.png | Bin 0 -> 22268 bytes
 paper/document.tex            | 542 ++++++++++++++++++++++++++++++++++
 paper/oxycomps.sty            | 102 +++++++
 paper/references.bib          | 307 +++++++++++++++++++
 4 files changed, 951 insertions(+)
 create mode 100644 paper/block-search.drawio.png
 create mode 100644 paper/document.tex
 create mode 100644 paper/oxycomps.sty
 create mode 100644 paper/references.bib

diff --git a/paper/block-search.drawio.png b/paper/block-search.drawio.png
new file mode 100644
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+\documentclass[10pt,twocolumn]{article}
+
+\usepackage{oxycomps}
+\bibliography{references}
+
+\pdfinfo{
+    /Title (SpatialDB: A Database for Storing Dense Three-Dimensional Voxel Structures)
+    /Author (Nicholas Novak)
+}
+
+\title{SpatialDB: A Database for Storing Dense Three-Dimensional Voxel Structures}
+\author{Nicholas Novak}
+\affiliation{Occidental College}
+\email{nnovak@oxy.edu}
+
+\begin{document}
+
+\maketitle
+
+\section{Introduction and Problem Context}
+
+% What my project is
+In my senior comprehensive project, I have designed and implemented a database
+application that is designed specifically to store complex shapes in ``voxels'',
+or three-dimensional pixels.
+
+% Applications of voxels
+A voxel\cite{enwiki:1186283262} represents a single point or cube in a
+three-dimensional grid, at a variable size. This feature allows them to
+approximately model many three-dimensional structures, in order to reduce the
+computational complexity in analyzing the shape, which has led to many
+data-related use cases outside of computer science. For example, to model the
+inner workings of the brain, Neuroscientists track oxygen concentration through
+neural tissue on a voxel grid as part of fMRI studies\cite{norman2006beyond},
+and Movie studios such as DreamWorks use voxel data structures to model light
+reflections for visual effects\cite{museth2013vdb}. The output of MRI scans in
+hospitals are very high-resolution voxel grids. Most recently, machine learning
+models are being trained on the LIDAR data from self-driving
+cars\cite{li2020deep} in order to better process their environments. However,
+voxels are not often thought of as a way to store three-dimensional shapes, and
+existing research focuses mainly on efficiently representing and processing
+shapes. My approach models this problem of voxel storage and representation, and
+turns it into a problem of database design.
+
+\subsection{Using Minecraft as a Model for a Database}
+
+% The problems with Minecraft
+Minecraft\footnote{https://www.minecraft.net/en-us}, released 2009, is a sandbox
+game that is played in a world entirely composed of cubic voxels, where the
+player has complete freedom to manipulate the world by building, destroying, or
+exploring any part of it. I am focusing this database on the requirements of
+Minecraft because the game involves some additional challenges that traditional
+databases do not consider. Primarily, the world of Minecraft is infinite in the
+horizontal $x$ and $z$ axes, but fixed in the $y$ axis, which limits the amount
+of information that can be stored by the database at once. The world also
+contains a denser voxel grid than in many other applications, meaning that far
+more of the blocks in the world are filled than empty.
+
+A game is also a real-time application, which means that any performance issues
+will be immediately be present to the user. Most databases can be evaluated on
+only their speed, but as the Minecraft server processes new information 20 times
+per second, the game has a time budget of 50ms to handle all game logic,
+including the storing of data. Less time processing the data in the world means
+that more time will be freed up for the game to process other work, although
+finishing work earlier will not necessarily be faster for the end user, if it
+still under the budget of 50ms. Most databases do not meet this requirement, and
+even though they may be faster, their complexity does not mean that they will
+always finish operations within this time limit.
+
+These limitations also make Minecraft unable to take advantage of a cache, since
+the number of different operations that can be done on the world is infinitely
+large, remembering any previous operations will often not be helpful for the
+system's performance. Minecraft also provides a good benchmark for the database,
+because the unpredictability of players stresses the system's ability to return
+results in a variety of settings.
+
+\section{Technical Background}
+
+\subsection{What is a database?}
+When I refer to the concept of a database, I am referencing a program that sits
+more or less as a ``black box'' between the user and a software application,
+storing any data required for the application. In most existing applications,
+this is done by a category of databases called ``relational databases'', which
+offer a very general-purpose way to store user data that is highly connected.
+For instance, a person stored in a relational database would be efficiently
+linked to with any of their associated information, such as name or age.
+
+% The model of a database
+In database terms, any amount of data added to the database is called a
+``write'', data retrieved from the database is called a ``read'', and any
+questions asked, such as ``how many people have done this'', are called
+``queries''. Developers ask these questions through computer languages, one such
+example being Structured Query Language or SQL, which allow
+the database to be queried efficiently.
+
+\subsection{Challenges With Existing Databases}
+
+% Software development and SQL
+Most software engineering projects start with a simple front-end and back-end,
+typically implemented with some sort of Model-View-Controller architecture, and
+connected to a relational SQL database \cite{sqliteOnlyDatabase}. This idea was
+popularized by frameworks such as Ruby on Rails and Django, where the model was
+most often modeled by structures within the database. This framework allowed
+software developers to not have to worry about inner workings of the database,
+and focus on writing business logic. This is how many start-ups were built, such
+as GitHub \cite{githubSingleSQL}, who recently moved off its single SQL database
+after 13 years, citing performance issues.
+
+% Challenges with working with SQL: Performance
+Using a single SQL-speaking database can be a significant advantage development
+speed, but the database can have some issues keeping up with the demands of the
+application as the performance requirements expand.
+% Caching
+As soon as this happens, companies typically put smaller caching applications in
+front of their database, such as \verb|Redis|\footnote{https://redis.io/},
+\verb|memcached|\cite{nishtala2013scaling}, or \verb|TAO| \cite{bronson2013tao},
+to allow the application to remember some of the commonly asked questions and
+reduce load on the database by not having to do the same work again.
+
+\subsubsection{The Complexity of General-Purpose Databases}
+% What is being done about this
+Modern SQL databases are also very complex. Three of the most popular SQL
+databases, PostreSQL, MySQL and Sqlite have 1.4 million lines
+\footnote{https://wiki.postgresql.org/wiki/GSoC\_2018, in reference to the
+text ``PostgreSQL is over 1.3M lines of code and some of the code paths can be
+tricky to reach.''} of code, 2.9 million lines
+\footnote{https://www.openhub.net/p/mysql}, and 150,000 lines
+\footnote{https://www.sqlite.org/testing.html} respectively.
+
+% Why are databases inefficient?
+Why are databases so complex? Most of the reason for the complexity is that
+because these database systems so general-purpose, they cannot assume anything
+about the data stored in them. For the database, finding an efficient plan to
+answer each query is a known NP-hard problem\cite{chatterji2002complexity}, and
+to keep itself fast, the database must construct this plan with a complex set of
+approximations, based on the assumptions that it can make, which leads to
+ever-evolving complexity.
+
+% Impossible to maintain
+With this complexity, it is impossible for a single person to understand the
+complete inner workings of a database. Thus, the problem of the company's
+database often becomes a dedicated person in companies that can afford it, or
+become entire teams of engineers at larger organizations such as
+Google\cite{googlePerfTeam}.
+
+% Intro to special-purpose databases
+What happens in the larger companies that can afford more engineering time, and
+have a specific problem that they cannot solve with a traditional database?
+Typically, this leads to the creation of special-purpose database solutions. For
+instance, the global scale of iCloud and Apple's cloud solutions required them
+to create FoundationDB\cite{zhou2021foundationdb}. A different set of challenges
+in the Facebook inbox led to the creation of Apache
+Cassandra\cite{lakshman2010cassandra}, which is optimized to allow for many
+emails to be received, at the expense of search speed, which is done far less
+frequently.
+
+\subsubsection{The Special-Purpose Database}
+
+Limiting a database's design to a specific use-case can make the development
+process much simpler, to the point where it can be done by a single person, and
+can offer higher performance. The first question that needs to be asked is
+whether the application is \textit{write-heavy} or \textit{read-heavy}.
+Read-heavy applications occur often in web development, and most social media
+platforms have far more users reading the content, than writing new content for
+the platform. In contrast, write-heavy applications are often seen in analytics
+workloads, where data is written from many sources, and analyzed infrequently by
+users.
+
+My application has a relatively even write and read balance, and I evaluated
+three different storage data structures before choosing to implement my own
+
+% Special-purpose databases
+Recently, companies such as Tigerbeetle\cite{tigerbeetleDesign} have taken this
+domain-driven approach to database design even further, while designing a
+database from the ground up to do financial accounting, which outperforms a
+reference MySQL implementation at 76 accounting transactions per second, to
+1,757 transactions per second \cite{tigerbeetlePerf}. This highly specialized
+and domain-specific approach to creating databases is what my project is going
+to be based on, to create a database around the challenges that the game
+Minecraft has.
+
+\subsubsection{Key-Value Databases}
+
+One of the main architectures that I considered for my project is a design
+called a key-value store\cite{kvdatabase}, which would store the relationship of
+a single voxel to its value. Many other voxel databases use this method to
+achieve constant-time operations on retrieving points, which means that
+regardless of the size of the dataset, the database will always be able to
+return a result in the same amount of time. This structures is behind many of
+the high-performance caches that are commonly used to speed up web applications,
+such as Redis and RocksDB\cite{dong2021rocksdb}. In order to provide high speeds
+for this data, the key-value mappings are usually stored in main memory, which
+is far more expensive and limited than the system's disk drive, but offers
+a speed advantage of several orders of magnitude\cite{latencyKnow}.
+
+\section{Prior Work}
+
+\subsection{Voxels for Efficient Computation}
+
+Most existing literature on the topic of using voxels to store shapes focuses on
+the application of the voxel grid for efficient computation. Since voxel points
+are completely independent of each other, this allows for efficient parallel
+processors, which are increasingly more common on consumer hardware, to take
+advantage of this speedup. In VDB\cite{museth2013vdb} Museth demonstrates that
+by modeling a sparse voxel grid in different resolutions, a computer cluster can
+efficiently approximate a physical structures such as a cloud, in order to
+calculate expensive lighting operations.
+
+\subsection{Parallel Processing on Voxel Databases}
+
+Williams\cite{williams1992voxel} expands upon the uses of a voxel database to
+model graph and mesh-based problems. Taking advantage of the parallelism in the
+grid, many problems can be reframed in the representation of voxels, and solve
+those problems far more efficiently. This model however, assumes that every
+voxel is stored in shared memory, making this process only viable to solve
+problems that can be modeled on one machine, and are far more computationally
+expensive, rather than data-intensive.
+
+\subsection{Large Voxel Data Set Processing}
+
+Another approach to the problem of storing voxel data is the distributed
+approach in Gorte et. al. \cite{gorte2023analysis}. Since memory is limited
+within one computer, this workload can be split up between many servers, which
+allows very large datasets to be worked on by a single workstation through an
+API. This method keeps many of the same performance considerations, but also
+assumes that the voxel data is not very dense, and uses a three-dimensional
+data structure called an octree, which allows the user to change the resolution
+of the data that they are working on. In the paper, Gorte acknowledges the need
+to split large datasets up into smaller regions, which is similar to the concept
+of ``chunks'' in my implementation.
+
+\subsection{Previous Special-Purpose Databases}
+
+The design of my database was also inspired by the LSM tree and data-driven
+designs of Tigerbeetle\cite{tigerbeetleDesign}, which is also able to handle
+concurrent operations on the same design. Another database,
+CockroachDB\footnote{https://www.cockroachlabs.com/product/}, uses a key-value
+mapping backend to store a SQL-like tables and rows. Finally, the design of
+caching layers in modern SQL caches such as Noria\cite{gjengset2018noria} show
+that it it possible to efficiently remember the complex queries found in SQL,
+and replicate these in real-time.
+
+\section{Methods}
+
+Almost every part of the database was designed so that most operations could be
+done in constant time.
+
+The database provides a simple interface to read and write data, consisting of
+the following:
+\begin{itemize}
+  \item Read a single block
+  \item Write a single block
+  \item Change a range of blocks
+  \item Read a pre-defined ``chunk'' of blocks
+\end{itemize}
+
+
+The process of fetching the data for a single point in the world starts at that
+point's $x, y$ and $z$ location. The world is infinite in size on the horizontal
+$x$ and $z$ axes, but limited in the vertical $y$ axis. In my database, the
+world is composed of an infinite grid of ``chunks'', or columns that are a fixed
+16 x 16 blocks in the $x$ and $z$ axes, but 256 blocks in the vertical $y$ axis.
+
+Once you know a point's location, you can find with a modulus what chunk the
+point is located within. From there, the database only needs to retrieve the
+data for the chunk stored at that location.
+
+Initial implementations for my database focused on tree-based approaches for
+finding the files for chunks, but with their complexity and non-constant
+complexity, I decided to store each chunk separately. However, with worlds with
+chunk counts in the hundreds of thousands, the filesystem implementations had
+issues with searching through so many files, which led to performance problems.
+Finally, I settled on merging all the chunk data into one file, and use the
+filesystem's \verb|seek| syscall to lookup the offset for the correct chunk. A
+simple hash table was then used to store each chunk's location with its offset
+in the file, which keeps the memory cost low, even with chunk counts in the
+millions. This allows for constant-time searches for the chunk's data.
+
+Once a chunk is retrieved from disk, the format of the chunk is broken down into
+smaller cubic slices of the chunk, called ``sections'' each section is a
+16x16x16 cubic area that keeps an index for every chunk. The point's $y$
+position tells the database what section the point is in, and a simple formula
+is done to convert the remaining $x$ and $z$ axes into an index within the
+section.
+
+Every section additionally stores a look-up-table, that stores a mapping of a
+\textit{palette index} to the state of a block. When the value for the point is
+retrieved from the section, the value returned is not the block's state, but
+simply an index into this palette. The palette lookup is done in constant time,
+and when a new block is added into the section that needs an additional state in
+the palette, this value is added in constant time as well. The existence of this
+palette supports the efficient operation of another part of the database, which
+is the ability to change large portions of blocks in the world.
+
+Once the value of the point is found in the palette, the value can be returned
+to the user. A visual diagram of this process can be found in figure
+\ref{fig:lookup}.
+
+\begin{figure}
+  \centering
+  \includegraphics[width=8cm]{block-search.drawio.png}
+  \caption{The process of looking up a single block}
+  \label{fig:lookup}
+\end{figure}
+
+The ability to change a region of blocks is also a common operation within the
+database, and which isn't locked to a specific range of chunks. This operation
+is implemented as overwriting the palettes for a specific region. By overwriting
+every palette index to the same value, every value in the chunk effectively gets
+set to the same value. This does however create the need for an additional
+``compaction'' step, where the palette is shrunk to remove duplicate values, and
+every block within the section must be updated to point to the correct index in
+the palette. This compaction is done upon any subsequent writes to the section
+by inserting a block, because only this fixed-size section needs to be changed,
+preserving the time of the operation as constant time.
+
+Finally, the retrieval of a single chunk can be done efficiently, because the
+database already stores chunks separately, and serializes these to the client.
+
+% \cite{vohra2016apache}.
+
+\section{Evaluation Metrics}
+
+\subsection{Reading Single Voxels}
+
+Reads and writes of single voxels are the most common fundamental operation for
+my database, and the database should be handle this operation in the same amount
+of time, regardless of the size of the world. Both my implementation and the
+simpler key-value store meet this criteria.
+
+\subsection{Changing Regions of Voxels}
+
+Changing regions of voxels should be able to be done in linear time. This is
+because resetting or changing a region of voxels is important while drawing
+shapes of various resolutions. Lower resolution shapes are less precise, and
+thus are able to be written faster.
+
+\subsection{Memory Requirements}
+
+The memory requirement is set quite low, at 256MB, in order to require the
+database to store most of its data on disk, and limit its memory usage to
+important caching features. This limitation was chosen for larger datasets that
+don't fit within memory on a single machine, because memory is much more
+expensive than disk storage, and would limit the analysis to smaller voxel grids.
+
+\subsection{Reading Regions of Voxels}
+
+The ability to retrieve large shapes from the database is important, because in
+order to export a shape, another operation must be present to efficiently do
+this. This operation therefore must be done in constant time, because as
+Gorte\cite{gorte2023analysis} identifies, many researchers might want to work
+on the same dataset, and exporting all this data would become inefficient for
+the database to process. In the use-case of Minecraft, this allows the server to
+support many more players at once, by not sending every individual block to each
+client. This requirement is not met by the key-value database, but is reached by
+my implementation, by sending the stored chunks on disk.
+
+\subsection{Reading Neighboring Blocks}
+
+The last common operation in most voxel databases is the ability to read points
+that are neighboring another point. This is important because many voxel shapes
+approximate cubic shapes \cite{gorte2023analysis}, and in Minecraft, players are
+constantly affecting voxels that are nearer to each other.
+
+\section{Results and Discussion}
+
+Benchmarking on my laptop, inserting values in various spreads around the voxel
+world, I get the following benchmarks, comparing an in-memory implementation of
+SpatialDB, the disk-based implementation of SpatialDB, and a memory-based
+key-value implementation in figure \ref{fig:reads}:
+
+\begin{figure}
+  \centering
+  \begin{tabular}{c | c | c | c}
+    Spread of Points & In-memory & Disk & KeyValue\\
+    \hline
+    128 & 4275 & 4146669 & 176.7\\
+    512 & 4184 & 3319162 & 190.6\\
+    2048 & 2613 & 422938 & 184.8\\
+    65536 & 2382 & 18814 & 186.1
+  \end{tabular}
+  \caption{Time (in ns) to operate on a single voxel, based on the size of the
+  world (spread)}
+  \label{fig:reads}
+\end{figure}
+
+These results show that the scaling remains consistent between the in-memory
+version and the key-value store, although my implementation is about two orders
+of magnitude slower than the latter. This scaling is however not met by the
+performance of the on-disk database. Originally, I thought that these poor
+results were the result of no caching being done on the chunk files, which would
+have made searches much slower, but still doesn't explain the improvement in
+performance by larger worlds. This led me to implement a disk cache, which had
+similar results, to the final implementation where I combined all the data in
+one large file, and selectively read sections from that file. This leads me to
+believe that as the points tested grow more spread out, since the world is only
+so large, many points will be outside of the loaded chunks, and instantly return
+empty.
+
+This change could likely be addressed by a change in caching methods, and
+remembering the data for more chunks, but this still doesn't address the slow
+speeds for accessing data in the first place. The slow speeds are most likely
+the decoding of the JSON data stored on disk, which is relatively large at
+about 4 megabytes in size. A custom encoding method could be designed to replace
+this scheme, or additionally pre-allocate the entire storage space in the
+chunks, so that chunk data could be retrieved without decoding the entire chunk.
+However, this would require a much more constrained data layout, and limit the
+implementation of different voxels.
+
+Additionally, compression 
+
+\section{Ethical Considerations}
+
+\subsection{Considerations of Computing Resources}
+
+Since databases are at the core part of most complex systems, they are often
+built to be run on hardware that the normal consumer can afford
+\footnote{\url{https://docs.oracle.com/en/database/oracle/oracle-database/12.2/ntdbi/oracle-database-minimum-hardware-requirements.html}}
+\footnote{\url{https://wiki.lustre.org/Lustre_Server_Requirements_Guidelines}}.
+
+The large hardware requirements of these databases come from the environments
+where they are implemented, and at many of these companies, the ability to
+keep buying faster hardware allows the company to work on other things that are
+more important. However, what this does to the player is effectively prices them
+out of the game that they would be already playing, especially since the
+database would also have to run alongside the existing Java application of
+Minecraft, which quickly exhaust system memory.
+
+In the design of my server I have to prioritize both performance to take
+advantage of the existing hardware, but make sure that the accessibility for
+the application does not decrease as a result.
+
+\subsection{Considerations of Complexity}
+Another factor to consider in the implementation of my database is how complex
+the existing systems are. Some of the most popular SQL databases, PostgreSQL and
+MySQL have 1.4 and 4.4 million lines of code respectively
+\footnote{\url{https://news.ycombinator.com/item?id=24813239}}.
+
+With so much complexity going on, this significantly decreases the overall
+knowledge of the system, as well as the individual user who has to debug their
+game. Most of this is from the large amount of query logic that handles caching
+and speeding up certain queries, so knowing more about the specific problem that
+I am trying to solve removes this process from having to be done.
+
+Especially since most of the people in the Minecraft community are volunteers in
+the open-source community, debugging this large of an application would be out of
+scope for enjoying a game, and likely lead to it being replaced with something
+more simple. The reliability characteristics are also less than what are
+required for Minecraft, since they are being compared against a single-threaded
+Java program which has been tested to do the correct thing.
+
+\subsection{Considerations in Security}
+
+Since these databases are very complex, there is also the risk that having a
+server exposed over the internet through the Minecraft game server might leave
+it exposed to attacks. While this is a large issue, an even more important
+implication is the ability to configure the database correctly. Since these
+databases are extremely complex, it is also very hard to make sure that they are
+configured securely. There have been many high-profile data
+breaches\footnote{\url{https://www.zdnet.com/article/hacker-ransoms-23k-mongodb-databases-and-threatens-to-contact-gdpr-authorities/}}
+that involve a single server, even at larger companies that have dedicated teams
+that involve a data breach.
+
+My plan to mitigate this risk is to implement the database in a memory-safe
+programming language, which should remove the risk class of memory-unsafety
+bugs, which account for around 70\% of all bugs in the Chromium browser
+engine\footnote{\url{https://www.chromium.org/Home/chromium-security/memory-safety/}},
+which is entirely written in non-memory safe C++.
+
+And if the database information is ever able to be leaked through the Minecraft
+protocol, the attacker would have access to the full data, because I am planning
+to store it unencrypted for performance reasons, and rely on the encryption of
+the Minecraft client. And, the data involved does not involve personally
+identifying information, so the usefulness of the data would be close to
+nothing.
+
+But, perhaps the most important security risk is if an attacker is able to
+access the database directly and bypass all the isolation in the Minecraft
+protocol, in order to wipe or corrupt the data for malicious reasons. This would
+likely lead to the Minecraft server being unable to be played, and degrade the
+experience of the players. It is my plan to take advantage of the limitations of
+the types of Minecraft items to provide resilience and easy backups to the
+system, because of the purpose-built nature of the system
+\footnote{\url{https://twitter.com/eatonphil/status/1568247643788267521?s=20}}.
+
+\subsection{Considerations in Fairness}
+
+In the implementation of databases, it can often be beneficial to make certain
+operations faster, at the expense of others that are not done as often. For
+instance, if I notice that players often pull items in and out of their systems
+often, but almost never search through the list of items, I can take advantage
+of this to speed up the database for the most common operations. However, this
+can be problematic if the things that I choose to sacrifice affect a certain
+group of users.
+
+This tradeoff between speed and reliability occurs so often in Computer Science 
+and is described in terms of percentiles. For instance, if we notice that some
+event occurs about half the time, we can say it is in the 50th percentile.
+Similarly, if an event only occurs 1\% of the time, we can say it occurs in the
+99th percentile. The impossible effect of not hurting anyone when a decision
+like this is make is written about by Google \cite{dean2013tail}, who have to make every
+decision like this at their scale.
+
+My plan is to not have any tradeoffs that affect the normal gameplay of the
+server, and keep it within the 50ms timeframe that the Minecraft has allocated
+to itself. Apart from this, one of the main goals of the project is to give
+consistent performance, so any further decisions will be made around the
+existing implementation of the Minecraft server.
+
+%https://www.embedded.com/implementing-a-new-real-time-scheduling-policy-for-linux-part-1/
+%https://www.kernel.org/doc/html/latest/scheduler/sched-design-CFS.html
+%https://helix979.github.io/jkoo/post/os-scheduler/
+
+\subsection{Considerations in Accessibility}
+
+By creating this system, I also have to consider if the players are going to
+require a certain type of computer. Requiring a certain operating system or a
+more powerful computer would limit access to many of the people that were
+playing the game before.
+
+However, by basing the goal of the project on improving the performance of the
+already existing implementation, any improvements would result in more people
+being able to play than before. Also, by designing the system for normal
+hardware and in a cross-platform way, this does not limit the people that are
+able to access the improvements.
+
+
+\subsection{Considerations in the Concentration of Power}
+
+With any improvements to performance to servers in Minecraft, this would allow
+many of the larger hosting companies, who rent servers monthly to individual
+people, to drive down their hosting costs, and allow them to have larger returns
+over the smaller providers. However, since this market is so competitive between
+companies, because of how easy it is to set up a company, and the options
+between companies aren't very different, I would expect any improvement to be
+quickly disappear into the competitive market, and benefit everyone equally.
+
+\section{Future Work, and Conclusion}
+
+\printbibliography
+
+\end{document}
diff --git a/paper/oxycomps.sty b/paper/oxycomps.sty
new file mode 100644
index 0000000..806ec11
--- /dev/null
+++ b/paper/oxycomps.sty
@@ -0,0 +1,102 @@
+% A simple two-column LaTeX style for Occidental College's CS senior projects.
+% Based on latex8.sty by Paolo.Ienne@di.epfl.ch 
+
+\usepackage{times} % use Times as the default font
+% define bold 11pt Times font for second-order headings
+\font\elvbf = ptmb scaled 1100
+
+\usepackage[style=numeric,sorting=nyt]{biblatex} % format the bibliography nicely
+\usepackage{xpatch} % used to patch \textcite
+
+% change \textcite to do family-name (year)
+\xpatchbibmacro{textcite}
+  {\printnames{labelname}}
+  {\printnames{labelname} (\printfield{year})}
+  {}
+  {}
+% sort bibliography by last name
+\DeclareNameAlias{default}{family-given}
+
+\usepackage{amsfonts} % provides many math symbols/fonts
+\usepackage{amsmath} % provides many math environments
+\usepackage{amssymb} % provides many math symbols/fonts
+\usepackage{caption} % fixes caption spacing issues
+\usepackage[usenames,dvipsnames]{color} % allows for colored text
+\usepackage{enumitem} % allows adjustment of list spacing
+\usepackage{graphicx} % allows insertion of graphics
+\usepackage{hyperref} % creates links within the page and to URLs
+\usepackage{listings} % provides the lstlisting environment
+\usepackage{url} % formats URLs properly
+\usepackage{verbatim} % provides the comment environment
+
+% set dimensions of columns, gap between columns, and paragraph indent
+\setlength{\textheight}{8.875in}
+\setlength{\textwidth}{6.875in}
+\setlength{\columnsep}{0.3125in}
+\setlength{\topmargin}{0in}
+\setlength{\headheight}{0in}
+\setlength{\headsep}{0in}
+\setlength{\parindent}{1em}
+\setlength{\oddsidemargin}{-.304in}
+\setlength{\evensidemargin}{-.304in}
+
+% remove the space between list items
+\setlist{noitemsep}
+
+% style code listings
+\lstset{
+    basicstyle=\ttfamily\footnotesize,
+    breaklines=true,
+    showstringspaces=false
+}
+
+% style the title
+\def\@maketitle{
+   \newpage
+   \begin{center}
+      {\Large \bf \@title \par}
+      % add two empty lines at the end of the title
+      \vspace*{2\baselineskip}
+      {
+         \large
+         \begin{tabular}[t]{c}
+            \@author
+         \end{tabular}
+         \par
+      }
+      % add small space at the end of the author name
+      \vspace*{.5em}
+      {
+         \ifx \@empty \@email
+         \else
+            \texttt{\@email}
+            \par
+            \vspace*{.25em}
+         \fi
+         \ifx \@empty \@affiliation
+         \else
+            \@affiliation
+         \fi
+      }
+      % add empty line at the end of the title block
+      \vspace*{\baselineskip}
+   \end{center}
+}
+
+% style the abstract
+\def\abstract{%
+   \centerline{\large\bf Abstract}%
+   \vspace*{\baselineskip}%
+}
+
+% define email and affiliation
+\def\email#1{\gdef\@email{#1}}
+\gdef\@email{}
+\def\affiliation#1{\gdef\@affiliation{#1}}
+\gdef\@affiliation{}
+
+% correct heading spacing and type
+\def\section{\@startsection {section}{1}{\z@}
+   {14pt plus 2pt minus 2pt}{14pt plus 2pt minus 2pt} {\large\bf}}
+\def\subsection{\@startsection {subsection}{2}{\z@}
+   {13pt plus 2pt minus 2pt}{13pt plus 2pt minus 2pt} {\elvbf}}
diff --git a/paper/references.bib b/paper/references.bib
new file mode 100644
index 0000000..7cc93b2
--- /dev/null
+++ b/paper/references.bib
@@ -0,0 +1,307 @@
+// Introduction
+
+@misc{sqliteOnlyDatabase,
+  title={SQLite the only database you will ever need in most cases}, 
+  url={https://unixsheikh.com/articles/sqlite-the-only-database-you-will-ever-need-in-most-cases.html}, 
+  journal={https://unixsheikh.com/}, 
+  publisher={https://unixsheikh.com/}, 
+  author={Sheikh, Unix}, 
+  year={2021}, 
+  month={Apr},
+}
+
+@misc{ enwiki:1181180757,
+  author = "{Wikipedia contributors}",
+  title = "Model–view–controller --- {Wikipedia}{,} The Free Encyclopedia",
+  year = "2023",
+  howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Model%E2%80%93view%E2%80%93controller&oldid=1181180757}",
+  note = "[Online; accessed 13-December-2023]"
+}
+
+@online{googlePerfTeam,
+  author = {{Google Performance Team}},
+  title = {System Performance},
+  month = {May},
+  year = {2023},
+  url = {https://research.google/teams/system-performance/},
+}
+
+// Applications of voxels
+
+@misc{ enwiki:1186283262,
+  author = "{Wikipedia contributors}",
+  title = "Voxel --- {Wikipedia}{,} The Free Encyclopedia",
+  year = "2023",
+  howpublished = "\url{https://en.wikipedia.org/w/index.php?title=Voxel&oldid=1186283262}",
+  note = "[Online; accessed 13-December-2023]"
+}
+
+@article{norman2006beyond,
+  title={Beyond mind-reading: multi-voxel pattern analysis of fMRI data},
+  author={Norman, Kenneth A and Polyn, Sean M and Detre, Greg J and Haxby, James V},
+  journal={Trends in cognitive sciences},
+  volume={10},
+  number={9},
+  pages={424--430},
+  year={2006},
+  publisher={Elsevier}
+}
+
+@article{museth2013vdb,
+  title={VDB: High-resolution sparse volumes with dynamic topology},
+  author={Museth, Ken},
+  journal={ACM transactions on graphics (TOG)},
+  volume={32},
+  number={3},
+  pages={1--22},
+  year={2013},
+  publisher={ACM New York, NY, USA}
+}
+
+@article{li2020deep,
+  title={Deep learning for lidar point clouds in autonomous driving: A review},
+  author={Li, Ying and Ma, Lingfei and Zhong, Zilong and Liu, Fei and Chapman, Michael A and Cao, Dongpu and Li, Jonathan},
+  journal={IEEE Transactions on Neural Networks and Learning Systems},
+  volume={32},
+  number={8},
+  pages={3412--3432},
+  year={2020},
+  publisher={IEEE}
+}
+
+// Literature Review
+
+@article{williams1992voxel,
+  title={Voxel databases: A paradigm for parallelism with spatial structure},
+  author={Williams, Roy D},
+  journal={Concurrency: Practice and Experience},
+  volume={4},
+  number={8},
+  pages={619--636},
+  year={1992},
+  publisher={Wiley Online Library}
+}
+
+@article{gorte2023analysis,
+  title={Analysis of very large voxel datasets},
+  author={Gorte, Ben},
+  journal={International Journal of Applied Earth Observation and Geoinformation},
+  volume={119},
+  pages={103316},
+  year={2023},
+  publisher={Elsevier}
+}
+
+@online{tigerbeetleDesign,
+  author = {{Tigerbeetle Developers}},
+  title = {Tigerbeetle Design Document},
+  month = {July},
+  year = {2020},
+  url = {https://github.com/tigerbeetledb/tigerbeetle/blob/main/docs/DESIGN.md},
+}
+
+@online{tigerbeetlePerf,
+  author = {{Tigerbeetle Developers}},
+  title = {Tigerbeetle Design Document},
+  month = {July},
+  year = {2020},
+  url = {https://github.com/tigerbeetledb/tigerbeetle/blob/main/docs/HISTORY.md},
+}
+
+@online{nomiSlowME,
+  author = {{Jokercortex}},
+  title = {Moron's Guide to Managing Mechanical Monstrosities},
+  month = {Feb},
+  year = {2020},
+  url = {https://github.com/Nomifactory/Guides/blob/latest/guides/AE2ForDummies.md},
+}
+
+@misc{btree,
+  author = "{Wikipedia contributors}",
+  title = "B-tree --- {Wikipedia}{,} The Free Encyclopedia",
+  year = "2023",
+  url = "https://en.wikipedia.org/w/index.php?title=B-tree&oldid=1146616935",
+  note = "[Online; accessed 13-May-2023]"
+}
+
+@misc{kvdatabase,
+  author = "{Wikipedia contributors}",
+  title = "Key–value database --- {Wikipedia}{,} The Free Encyclopedia",
+  year = "2023",
+  url = "https://en.wikipedia.org/w/index.php?title=Key%E2%80%93value_database&oldid=1135560734",
+  note = "[Online; accessed 13-May-2023]"
+}
+
+@online{latencyKnow,
+  author = "Jeff Dean",
+  title = "Latency Numbers Every Programmer Should Know",
+  year = "2018",
+  url = "https://gist.github.com/jboner/2841832",
+  note = "[Online; accessed 12-Dec-2023]"
+}
+
+@online{cockroachData,
+  author = {{CockroachDB Developers}},
+  title = {Structured data encoding in CockroachDB SQL},
+  year = {2017},
+  month = Mar,
+  url = {https://github.com/cockroachdb/cockroach/blob/master/docs/tech-notes/encoding.md},
+}
+
+@article{dong2021rocksdb,
+  title={Rocksdb: Evolution of development priorities in a key-value store serving large-scale applications},
+  author={Dong, Siying and Kryczka, Andrew and Jin, Yanqin and Stumm, Michael},
+  journal={ACM Transactions on Storage (TOS)},
+  volume={17},
+  number={4},
+  pages={1--32},
+  year={2021},
+  publisher={ACM New York, NY}
+}
+
+@misc{lsm,
+  author = "{Wikipedia contributors}",
+  title = "Log-structured merge-tree --- {Wikipedia}{,} The Free Encyclopedia",
+  year = "2023",
+  url = "https://en.wikipedia.org/w/index.php?title=Log-structured_merge-tree&oldid=1153046573",
+  note = "[Online; accessed 13-May-2023]"
+}
+
+@online{lsmUses,
+  author = {{Braden Groom}},
+  title = {Understanding LSM Trees: What Powers Write-Heavy Databases},
+  month = Jun,
+  year = {2020},
+  url = {https://yetanotherdevblog.com/lsm/},
+}
+
+@article{chang2008bigtable,
+  title={Bigtable: A distributed storage system for structured data},
+  author={Chang, Fay and Dean, Jeffrey and Ghemawat, Sanjay and Hsieh, Wilson C and Wallach, Deborah A and Burrows, Mike and Chandra, Tushar and Fikes, Andrew and Gruber, Robert E},
+  journal={ACM Transactions on Computer Systems (TOCS)},
+  volume={26},
+  number={2},
+  pages={1--26},
+  year={2008},
+  publisher={ACM New York, NY, USA}
+}
+
+@inproceedings{abadi2008column,
+  title={Column-stores vs. row-stores: how different are they really?},
+  author={Abadi, Daniel J and Madden, Samuel R and Hachem, Nabil},
+  booktitle={Proceedings of the 2008 ACM SIGMOD international conference on Management of data},
+  pages={967--980},
+  year={2008}
+}
+
+@article{athanassoulis2019optimal,
+  title={Optimal column layout for hybrid workloads},
+  author={Athanassoulis, Manos and B{\o}gh, Kenneth S and Idreos, Stratos},
+  journal={Proceedings of the VLDB Endowment},
+  volume={12},
+  number={13},
+  pages={2393--2407},
+  year={2019},
+  publisher={VLDB Endowment}
+}
+
+@inproceedings{armbrust2021lakehouse,
+  title={Lakehouse: a new generation of open platforms that unify data warehousing and advanced analytics},
+  author={Armbrust, Michael and Ghodsi, Ali and Xin, Reynold and Zaharia, Matei},
+  booktitle={Proceedings of CIDR},
+  volume={8},
+  year={2021}
+}
+
+@article{dean2013tail,
+  title={The tail at scale},
+  author={Dean, Jeffrey and Barroso, Luiz Andr{\'e}},
+  journal={Communications of the ACM},
+  volume={56},
+  number={2},
+  pages={74--80},
+  year={2013},
+  publisher={ACM New York, NY, USA}
+}
+
+https://github.blog/2021-09-27-partitioning-githubs-relational-databases-scale/#:~:text=Yet%20at%20its%20core%2C%20GitHub,%2C%20issues%2C%20and%20pull%20requests.
+@misc{githubSingleSQL, 
+  title={Partitioning github’s relational databases to handle scale}, 
+  url={https://github.blog/2021-09-27-partitioning-githubs-relational-databases-scale/}, 
+  journal={The GitHub Blog}, 
+  publisher={GitHub}, 
+  author={Maurer, Thomas}, 
+  year={2021}, 
+  month={Sep},
+} 
+
+@inproceedings{bronson2013tao,
+  title={$\{$TAO$\}$: Facebook’s distributed data store for the social graph},
+  author={Bronson, Nathan and Amsden, Zach and Cabrera, George and Chakka, Prasad and Dimov, Peter and Ding, Hui and Ferris, Jack and Giardullo, Anthony and Kulkarni, Sachin and Li, Harry and others},
+  booktitle={2013 $\{$USENIX$\}$ Annual Technical Conference ($\{$USENIX$\}$$\{$ATC$\}$ 13)},
+  pages={49--60},
+  year={2013}
+}
+
+@inproceedings{chatterji2002complexity,
+  title={On the complexity of approximate query optimization},
+  author={Chatterji, Sourav and Evani, Sai Surya Kiran and Ganguly, Sumit and Yemmanuru, Mahesh Datt},
+  booktitle={Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems},
+  pages={282--292},
+  year={2002}
+}
+
+@inproceedings{gjengset2018noria,
+  title={Noria: dynamic, partially-stateful data-flow for high-performance web applications.},
+  author={Gjengset, Jon and Schwarzkopf, Malte and Behrens, Jonathan and Ara{\'u}jo, Lara Timb{\'o} and Ek, Martin and Kohler, Eddie and Kaashoek, M Frans and Morris, Robert Tappan},
+  booktitle={OSDI},
+  volume={18},
+  pages={213--231},
+  year={2018}
+}
+
+How storage works in database systems, and the evolution of how data is stored
+@article{stonebraker2005goes,
+  title={What goes around comes around},
+  author={Stonebraker, Michael and Hellerstein, Joey},
+  journal={Readings in database systems},
+  volume={4},
+  pages={1},
+  year={2005}
+}
+
+@article{vohra2016apache,
+  title={Apache parquet},
+  author={Vohra, Deepak and Vohra, Deepak},
+  journal={Practical Hadoop Ecosystem: A Definitive Guide to Hadoop-Related Frameworks and Tools},
+  pages={325--335},
+  year={2016},
+  publisher={Springer}
+}
+
+@inproceedings{nishtala2013scaling,
+  title={Scaling memcache at facebook},
+  author={Nishtala, Rajesh and Fugal, Hans and Grimm, Steven and Kwiatkowski, Marc and Lee, Herman and Li, Harry C and McElroy, Ryan and Paleczny, Mike and Peek, Daniel and Saab, Paul and others},
+  booktitle={Presented as part of the 10th $\{$USENIX$\}$ Symposium on Networked Systems Design and Implementation ($\{$NSDI$\}$ 13)},
+  pages={385--398},
+  year={2013}
+}
+
+@inproceedings{zhou2021foundationdb,
+  title={Foundationdb: A distributed unbundled transactional key value store},
+  author={Zhou, Jingyu and Xu, Meng and Shraer, Alexander and Namasivayam, Bala and Miller, Alex and Tschannen, Evan and Atherton, Steve and Beamon, Andrew J and Sears, Rusty and Leach, John and others},
+  booktitle={Proceedings of the 2021 International Conference on Management of Data},
+  pages={2653--2666},
+  year={2021}
+}
+
+@article{lakshman2010cassandra,
+  title={Cassandra: a decentralized structured storage system},
+  author={Lakshman, Avinash and Malik, Prashant},
+  journal={ACM SIGOPS operating systems review},
+  volume={44},
+  number={2},
+  pages={35--40},
+  year={2010},
+  publisher={ACM New York, NY, USA}
+}