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@@ -123,7 +123,7 @@ core engine of the Metamarkets data analytics platform. In this paper, we detail
 \section{Introduction}
 Enterprises routinely collect diverse data sets that can contain up to terabytes of information per day. Companies are increasingly realizing the importance of efficiently storing and analyzing this data in order to increase both productivity and profitability. Numerous database systems (e.g., IBM’s Netezza \cite{singh2011introduction}, HP's Vertica \cite{bear2012vertica}, EMC’s Greenplum \cite{miner2012unified}) and several research papers \cite{barroso2009datacenter, chaudhuri1997overview, dewitt1992parallel} offer solutions for how to store and extract information from large data sets. However, many of these Relational Database Management Systems (RDBMS) and NoSQL architectures do not support interactive queries and real-time data ingestion.
 
-Metamarkets built Druid to directly address the need for a real-time analytical data store in the big-data ecosystem. Druid shares some similarities with main-memory databases \cite{farber2012sap} and interactive query systems such as Dremel \cite{melnik2010dremel} and PowerDrill \cite{hall2012processing}. Druid's focus is fast aggregations, arbitrarily deep data exploration, and low-latency data ingestion. Furthermore, Druid is highly configurable and allows users to easily adjust fault tolerance and performance properties. Queries on in-memory data typically complete in millseconds, and real-time data ingestion means that new events are immediately available for analysis.
+Metamarkets built Druid to directly address the need for a real-time analytical data store in the big-data ecosystem. Druid shares some similarities with main-memory databases \cite{farber2012sap} and interactive query systems such as Dremel \cite{melnik2010dremel} and PowerDrill \cite{hall2012processing}. Druid's focus is fast aggregations, arbitrarily deep data exploration, and low-latency data ingestion. Furthermore, Druid is highly configurable and allows users to easily adjust fault tolerance and performance properties. Queries on in-memory data typically complete in milliseconds, and real-time data ingestion means that new events are immediately available for analysis.
 
 In this paper, we make the following contributions:
 \begin{itemize}