Data centers are widely used for big data analytics, which often involve data-parallel jobs,
including query and web service. Meanwhile, cluster frameworks are rapidly developed for …

There has been much research devoted to improving the performance of data analytics
frameworks, but comparatively little effort has been spent systematically identifying the …

We present Stratosphere, an open-source software stack for parallel data analysis.
Stratosphere brings together a unique set of features that allow the expressive, easy, and …

Cluster computing applications--frameworks like MapReduce and user-facing applications
like search platforms--have application-level requirements and higher-level abstractions to …

Enterprises today acquire vast volumes of data from different sources and leverage this
information by means of data analysis to support effective decision-making and provide new …

MapReduce job parameter tuning is a daunting and time consuming task. The parameter
configuration space is huge; there are more than 70 parameters that impact job …

Modern data management applications involve complex processing tasks over large
volumes of data. Although this falls naturally within the scope of relational databases, many …

Real-world data-parallel programs commonly suffer from great memory pressure, especially
when they are executed to process large datasets. Memory problems lead to excessive GC …

The Map-Reduce model simplifies the large scale data handling on commodities group by
abusing parallel map & reduces assignments.. The use of this model is beneficial only when …

We introduce HadoopProv, a modified version of Hadoop that implements provenance
capture and analysis in MapReduce jobs. It is designed to minimise provenance capture …