Association Rules Analysis Method of Spatial Data Under MapReduce Framework

doi:10.16182/j.issn1004731x.joss.201803009

Abstract

Abstract: Spatial data has the characteristic of extensity, timeliness, multidimensional, large amount of data and complex relations. Some non-conventional data screening tool for analysis and mining is required to find out the patterns, rules and characteristics knowledge in the spatial big data for battlefield situation awareness and battle space management. In view that the existing Apriori algorithm scans the database too frequently, the Apriori algorithm is improved on the basis of working principle of Map Reduce .The fast analysis ideas and technologyframework of spatial data is proposed. An elementary validate prototype is built for the key technology experimentation.Experimental results show that, the technical route and framework can improve the speed of massive spatial data analysis and processing.

Key words: spatial data, big data, association rules, analysis method, parallel computing

CLC Number:

TP393

Zhang Mingzhi, Li Yi. Association Rules Analysis Method of Spatial Data Under MapReduce Framework[J]. Journal of System Simulation, 2018, 30(3): 840-845.

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