Abstract
Geographic Information System (GIS) is a subject that studies spatial information science and computer system for collecting, storing, retrieving, modeling, analyzing and representing geospatial data. GIS not only manages attribute information based on numbers and text, but also manages spatial information based on visual graphics and images. With the rapid development of computer technology, space technology and information technology, the expression form of map is gradually changing from two-dimensional to three-dimensional. 3D GIS can express 3D spatial information in detail and has powerful multi-dimensional spatial analysis function. However, 3D visualization technology can not be immersed in geographic information. The process of requesting information by each end user is energy intensive. If big data processing is adopted, it will greatly increase the computing pressure of big data center. The paper presents a 3D GIS construction method based on Virtual Reality (VR) and edge computing (gis-ve). Specifically, firstly, virtual reality equipment is deployed at the user terminal to realize the organic integration of real scene and virtual object in three-dimensional space, and build a real and perfect three-dimensional real world space. Secondly, under the edge computing architecture, virtual reality applications are deployed on edge computing nodes to reduce the number of device hops transmitted between virtual reality application data and end users, reduce network processing delay and reduce the computing pressure of big data processing center. Then, the deployment problem of GIS is transformed into a performance optimization problem, and the delay and cost are used as evaluation indexes. Finally, an improved artificial bee colony algorithm is proposed to solve the optimal GIS construction scheme. In order to verify the feasibility and effectiveness of this method, the paper compares this method with two other advanced 3D-GIS construction methods, and experiments show that this method performs well in terms of delay and cost.
Performance Optimization of Big Data Processing using Clustering Technique in Map Reduces Programming Model