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diff --git a/research.md b/research.md index 41841a1c7..ec7dd54d8 100644 --- a/research.md +++ b/research.md @@ -27,7 +27,7 @@ Traditional MapReduce and DAG engines are suboptimal for these applications beca </p> <p> -Spark offers an abstraction called <a href="http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf"><em>resilient distributed datasets (RDDs)</em></a> to support these applications efficiently. RDDs can be stored in memory between queries <em>without</em> requiring replication. Instead, they rebuild lost data on failure using <em>lineage</em>: each RDD remembers how it was built from other datasets (by transformations like <code>map</code>, <code>join</code> or <code>groupBy</code>) to rebuild itself. RDDs allow Spark to outperform existing models by up to 100x in multi-pass analytics. We showed that RDDs can support a wide variety of iterative algorithms, as well as interactive data mining and a highly efficient SQL engine (<a href="http://shark.cs.berkeley.edu">Shark</a>). +Spark offers an abstraction called <a href="http://people.csail.mit.edu/matei/papers/2012/nsdi_spark.pdf"><em>resilient distributed datasets (RDDs)</em></a> to support these applications efficiently. RDDs can be stored in memory between queries <em>without</em> requiring replication. Instead, they rebuild lost data on failure using <em>lineage</em>: each RDD remembers how it was built from other datasets (by transformations like <code>map</code>, <code>join</code> or <code>groupBy</code>) to rebuild itself. RDDs allow Spark to outperform existing models by up to 100x in multi-pass analytics. We showed that RDDs can support a wide variety of iterative algorithms, as well as interactive data mining and a highly efficient SQL engine (<a href="http://shark.cs.berkeley.edu">Shark</a>). </p> <p class="noskip">You can find more about the research behind Spark in the following papers:</p> |