With the advantages of MapReduce programming model in parallel computing and processing of data and tasks on large-scale clusters, a Dataaware partitioning schema in MapReduce for large-scale high-dimensional data is ...With the advantages of MapReduce programming model in parallel computing and processing of data and tasks on large-scale clusters, a Dataaware partitioning schema in MapReduce for large-scale high-dimensional data is proposed. It optimizes partition method of data blocks with the same contribution to computation in MapReduce. Using a two-stage data partitioning strategy, the data are uniformly distributed into data blocks by clustering and partitioning. The experiments show that the data-aware partitioning schema is very effective and extensible for improving the query efficiency of highdimensional data.展开更多
文摘With the advantages of MapReduce programming model in parallel computing and processing of data and tasks on large-scale clusters, a Dataaware partitioning schema in MapReduce for large-scale high-dimensional data is proposed. It optimizes partition method of data blocks with the same contribution to computation in MapReduce. Using a two-stage data partitioning strategy, the data are uniformly distributed into data blocks by clustering and partitioning. The experiments show that the data-aware partitioning schema is very effective and extensible for improving the query efficiency of highdimensional data.
