Research On Optimization Of Line Graphs Of Large-scale Data Source In Web Pages

In the era of big data,data visualization plays an important role in the process of information mining.At bottom,the mastery of ability to handle large-scale data is fundamental.At present,the visualization tools in web pages will create certain performance bottlenecks in different degrees when the data size is too large,and therefore greatly lowers the rate of productivity.However,the current methods of front-end visualization optimization are still limited,which include reducing the file size,using the cache,increasing the number of DOM nodes and other traditional technical optimization technologies.In other words,there is still no clear solution to the problem of information loss after technical optimization.This paper selects the line graph,a visualization tool with representativeness and universality to be the research object,and puts forward a comprehensive optimization method for large-scale data line graph.As a result,both aspects of performance and expression have elevated greatly.The method is based on D3?and uses the SVG vector map as the underlying mechanism,to retain the strong interaction of the modern line graph.Learning from the tile map,the use of hierarchical aggregation reduces the data size when browser renders,and thus to reduce the browser rendering pressure.Those partial data characteristics which are lost due to aggregation can be expressed by specific colors,gradations,and shapes under suitable conditions.The width of the lines can be used to imply the level of aggregation,corresponding to the data size of a unit.It is finally applied to the actual optimization of a line graph of one hundred thousand level data source.The result of this case proves that the integrated optimization method proposed in this paper has played an effective role in both performance optimization and expression optimization.