The Forelem framework was first introduced as a means to optimize database queries using optimization techniques developed for compilers. Since its introduction, Forelem has proven to be more versatile and to be applicable beyond database applications. In this paper we show that the original Forelem framework can be used to express and optimize Big Data applications, more specifically: k-Means clustering and PageRank, resulting in automatically generated implementations of these applications. These implementations are more efficient than state-of-the-art, hand-written MPI C/C++ implementations of k-Means and PageRank, as well as significantly outperform state-of-the-art Hadoop implementations.

翻译：Foreem框架最初是作为利用为汇编者开发的优化技术优化数据库查询的手段而引入的,自其引入以来,Foreem已证明更具有多功能性,并且可适用于数据库应用程序之外。在本文件中,我们表明最初的Foreem框架可用于表达和优化大数据应用程序,更具体地说:K-Means集群和PageRank,从而自动产生这些应用程序的实施。这些实施比最先进的手写MPI C/C++执行K-Means和PageRank的效率更高,以及显著超过先进的Hadoop执行状态。