Abstract
Knowledge reduction is one of the important research issues in rough set theory, which applies knowledge reduction theory to reduction the massive task sets in Gloud storage. At first, an equivalence class evolved from subviews will be obtained after task update, Then, a paral lel running strategy is designed for large-scale data , and calculate the optimal attributes based o n the task set with minimal time overhead, to this end, delete redundant views according to the opti mal attribute sets. Finally, the optimized task combination views are obtained. Simulation results shows it has better overall performance in time span, runtime, speed-up ratio and scalability when compared with the original algorithm that under same conditions, the actual examples used in ana lysis indicate the effectiveness of this method. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4282 Full Text: PDF
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