Parallel processing algorithm for railway signal fault diagnosis data based on cloud computing

Abstract To explore the data processing of high-speed railway fault signal diagnosis based on MapReduce algorithm, the partitioning strategy of data flow was improved, and Bias classification algorithm was used to model and classify data. In MapReduce parallelization process, the data partition matrix T k was stored in line segmentation, the computing load was distributed in every node of cluster, and the time consumption of mobile data matrix and the consumption of partitioned matrix were calculated. Results show that the algorithm proposed could reduce the amount of computation in the execution process, greatly reduce the memory space consumption, and improve the counting speed in railway signal system.

[1]  Yuan Cao,et al.  Local Fractional Functional Method for Solving Diffusion Equations on Cantor Sets , 2014 .

[2]  Francesco Marcelloni,et al.  A MapReduce solution for associative classification of big data , 2016, Inf. Sci..

[3]  Bin Guo,et al.  Cloud platform wireless sensor network detection system based on data sharing , 2018, Cluster Computing.

[4]  Lijun Chang,et al.  Scalable subgraph enumeration in MapReduce: a cost-oriented approach , 2017, The VLDB Journal.

[5]  Zhenlong Li,et al.  A spatiotemporal indexing approach for efficient processing of big array-based climate data with MapReduce , 2017, Int. J. Geogr. Inf. Sci..

[6]  Wei Li,et al.  Fuzzy logic based high speed data transmission algorithm of sensor networks for target tracking , 2017, J. Intell. Fuzzy Syst..

[7]  Zhenzhen Huang,et al.  Research on Library Recommendation Reading Service System Based on Adaptive Algorithm , 2018, Wirel. Pers. Commun..

[8]  Yuan Cao,et al.  Application of fuzzy predictive control technology in automatic train operation , 2018, Cluster Computing.

[9]  Jae-Gil Lee,et al.  Parallel community detection on large graphs with MapReduce and GraphChi , 2016, Data Knowl. Eng..

[10]  Xiao Zhang,et al.  MrHeter: improving MapReduce performance in heterogeneous environments , 2016, Cluster Computing.

[11]  Yuan Cao,et al.  Storage Aided System Property Enhancing and Hybrid Robust Smoothing for Large-Scale PV Systems , 2017, IEEE Transactions on Smart Grid.

[12]  Xia Zhang,et al.  Standard Analysis for Transfer Delay in CTCS-3 , 2017 .

[13]  Haibo Hu,et al.  MapReduce Parallel Programming Model: A State-of-the-Art Survey , 2015, International Journal of Parallel Programming.

[14]  Kiminori Matsuzaki,et al.  Parallel Tree Accumulations on MapReduce , 2015, International Journal of Parallel Programming.

[15]  Jifu Zhang,et al.  FiDoop: Parallel Mining of Frequent Itemsets Using MapReduce , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[16]  Shaofei Wu,et al.  A Traffic Motion Object Extraction Algorithm , 2015, Int. J. Bifurc. Chaos.

[17]  Mu Jian-cheng.  Application of formal methods in train control system , 2010 .

[18]  Chih-Fong Tsai,et al.  Big data mining with parallel computing: A comparison of distributed and MapReduce methodologies , 2016, J. Syst. Softw..

[19]  Zili Zhang,et al.  A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting , 2016, Neurocomputing.

[20]  Aws Albarghouthi,et al.  MapReduce program synthesis , 2016, PLDI.

[21]  Magdalena Balazinska,et al.  Estimating the progress of MapReduce pipelines , 2010, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010).