Research and application of the parallel of the coal Enterprise reserve quota algorithm on the Hadoop platform

Research and application of the parallel of the coal Enterprise reserve quota algorithm on the Hadoop platform

Zhang, Inner Mongolia University of Science and Technology

This paper presents a spare parts consumption forecasting system based on the Hadoop cloud computing platform. The system is divided into four parts: Data acquisition module, data storage module, data preprocessing module and spare parts consumption prediction module. The Data Acquisition module utilizes the ERP System Web service interface of a coal group company to obtain the user data, and the spare parts data obtained in the Data acquisition module is stored in the local Oracle database according to the design data format. The data preprocessing module uses the VS2010 development program to process the spare parts data in the database according to the request, obtains the data format data which we need, through the multi-level fuzzy comprehensive evaluation method, evaluates the spare parts importance from the key of spare parts and the key parts of the equipment. Quantify the importance of spare parts k; Spare parts consumption prediction module for spare parts consumption prediction method (Probabilistic statistical analysis method) to improve, based on classical matrix multiplication of classical algorithm, using MapReduce programming framework for MapReduce design, The MapReduce parallel processing algorithm is constructed and implemented on the MapReduce parallel programming model. The experimental results show that the algorithm has good performance in the experiments of the mapreduce design, such as the scalability of the processor, the scalability of the data and the performance of Speedup.

Research and application of the parallel of the coal Enterprise reserve quota algorithm on the Hadoop platform