Data Visualization Technology And Collaborative Filtering Algorithm For Base Station Location System

In the context of the rapid development of computer science and big data,a great number of software systems are facing many new challenges.Such as enable system users to obtain useful information intuitively and efficiently in the face of massive data,and construct a reasonable algorithm model to help system users make better decisions,and combine new algorithms with engineering and so orn.Data visualization,collaborative filtering and other technologies can solve the above problems well,thereby improving the efficiency of the system.Aiming at the base station system in the communication field,this thesis studies the workflow model and designs a new data visualization scheme based on the data attributes.For the problem of the collaborative filtering algorithm applied in the system,it proposed an algorithm to improve the matrix sparseness.Finally,these programs have been engineered.The main research contents of this thesis are as follows:Firstly,this thesis studies the workflow data in the base station location system,defines the visualization model,and designs a visualization scheme that visualizes and combines the geographic data and the time series data according to the attributes.In the visualization of geographic data,this thesis proposes a spatial deformation algorithm combining point radius attributes for the problem of point overlap caused by large data volume.The position of data points is rearranged with the smallest overlap distance.The validity of the algorithm is proved by experiments.Then,a new visualization form is designed based on the Sankey diagram to visualize the time series data part and combine with the geographic data visualization part to complete the visualization of the workflow data.Secondly,for the matrix sparseness problem of the collaborative filtering algorithm when recommending system's processors,this thesis proposes a predictive filling algorithm combined with task attributes to solve the sparseness problem of the algorithm.The state attribute and the time series attribute of the processed person are comprehensively considered,quantized into the influence factor,the similarity between the tasks is calculated,and then the matrix score is predicted,and the matrix is filled iteratively,thereby reducing the matrix sparseness and improving the recommendation accuracy.Experimental results demonstrate the effectiveness of the algorithm.Finally,this thesis designs and implements the base station location system that combines the above visualization and collaborative filtering algorithms.