Contribution: Recently, real-time data warehousing (DWH) and big data streaming have
become ubiquitous due to the fact that a number of business organizations are gearing up to …

The current works about MapReduce task scheduling with deadline constraints neither take
the differences of Map and Reduce task, nor the cluster's heterogeneity into account. This …

With the popularity of big data and cloud computing, data parallel framework MapReduce
based data warehouse systems are used widely. Column store is a default data placement …

As the volume of data to be processed in a timely manner soars, the scale of computing and
storage systems has much trouble keeping up with such a rate of explosive data growth. A …

Combining powerful parallel frameworks and on-demand commodity hardware, cloud
computing has made both analytics and decision support systems canonical to enterprises …

During the last few years, we have been witnessing a significant increase in research about
the development and production of hardware and software components with low levels of …

Timely discovering and acquiring information from incremental data on the Internet is a hot
topic in a big data era. This paper presents a distributed incremental information acquisition …

摘要共性数学库PETSc (Portable, Extensible Toolkit for Scientific Computation)
是高性能计算的基础模块, 是超级计算机计算环境的基础算法库之一, 其性能直接影响调用数学 …

Due to the batch processing capability and distributed storage, MapReduce and HDFS have
always been the core parts of Hadoop. Nowadays, many studies still focus on improving and …

A data warehouse can store very large amounts of data that should be processed in parallel
in order to achieve reasonable query execution times. The MapReduce programming model …