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\title{Related Work} %\vfill gives us the black space at the top of the page
\author{
Ryan Verdon
}
\date{\today}


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\section{Introduction}
None of the papers I found while researching my topic were completely related to my topic. The vast majority covered only small pieces of my problem. In the sections below I will go over related work.

\section{Current state of biological databases}
The papers below examine the current state of biological databases.

\paragraph{Database survey}
In 2003, a survey was done to examine what types of databases were running.\cite{bry_computational_2003}  The survey found that of the 111 databases they sampled 40-44 were collections of flat files and 41-42 were implemented as relational databases.\cite{bry_computational_2003} Interestingly, the authors also noticed that the vast majority of databases had hypertext references to other databases.

\paragraph{Database list}
There is a journal that has been keeping a list of useful biological databases.\cite{galperin_molecular_2008} In the 2008 version there was over 1000 unique or interesting databases. Interestingly in 2001 there was only 281.

\paragraph{Biozon}
Biozon examined what biologists have to go through to do research.\cite{biozon} The paper looked at how data stored in a database can be highly related to data stored in different databases. The paper also examined how multiple queries of different databases have to be done to complete research.

\section{NoSQL databases}
A major portion of my thesis will revolve around distributed databases. This section contains all of the distributed databases I am considering.

\paragraph{Cassandra}
Cassandra is an open-source, highly available, eventually consistent  database modeled after Google's BigTable and Amazon's Dynamo.\cite{cassandra}
Cassandra is a mix between key-value store and column-oriented database.

\paragraph{HBase}
HBase is an open-source, distributed, versioned, column-oriented database modeled after Google's Bigtable.\cite{hbase} HBase ties in closely with Google's map-reduce framework. So close in fact that tables in HBase can be used as input and output of map-reduce jobs.

\paragraph{MongoDB}
MongoDB is a document-based NoSQL database created by 10gen. MongoDB supports ad hoc queries with nested fields, indexing, replication and map-reduce support..\cite{mongodb} 

\paragraph{SimpleDB}
Amazon SimpleDB is a highly available, flexible, and scalable non-relational data store that is sold as a service.\cite{simpledb} Amazon charges for resources consumed in storing the data and serving requests. SimpleDB provides excellent flexibility allowing schema changes on the fly. 

\section{MapReduce}
Data processing is a very big part of bioinformatics. The database I design will require an extremely fast implementation of BLAST (Basic Local Alignment Search Tool). To do that I researched the MapReduce framework.

\paragraph{MapReduce}
MapReduce is a framework Google created for processing large data sets in parallel.\cite{dean_mapreduce_2008} The idea originally came from the notion of mapping a function over a list of data. This is commonly found in functional languages.

\paragraph{CloudBlast}
CloudBlast is an implementation of BLAST that uses the MapReduce framework in combination with the Hadoop file system.\cite{cloudblast} The authors were able to get better performance than all of the other attempts at parallelizing BLAST.

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