Key points are not available for this paper at this time.
MapReduce has emerged as a promising architecture for large scale data analytics on commodity clusters. The rapid adoption of Hive, a SQL-like data processing language on Hadoop (an open source implementation of MapReduce), shows the increasing importance of processing structured data on MapReduce platforms. MapReduce offers several attractive properties such as the use of low-cost hardware, fault-tolerance, scalability, and elasticity. However, these advantages have required a substantial performance sacrifice.
Kaldewey et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: