Construction method for big-data acceleration structure
A technology for accelerating structures and construction methods, applied in electrical digital data processing, special data processing applications, instruments, etc., to solve problems such as increased limitations, unsuitable algorithms, and inability to perform well, to speed up processing and data loading. Speed, simplicity of the build process, overcoming limitations and the effect of platform limitations
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Publication Date
- 2018-01-19
Smart Images

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Abstract
Description
technical field
[0001] The invention relates to a data acceleration processing method, in particular to a method for building a large data acceleration structure. Background technique
[0002] Big data technology has become the most effective and common technology for processing massive data. Police big data, as one of the most representative scenarios in big data processing scenarios, has attracted more and more attention. In the process of mass data analysis, the processing speed and processing performance of the general big data platform is one of the urgent problems to be solved. In big data analysis scenarios, especially in police big data, the most common analysis method is correlation analysis. Comprehensive correlation analysis of relevant factors involved in the analysis object can effectively improve the accuracy of police analysis. The commonly used big data correlation analysis The algorithm model is an association analysis algorithm model. Association analysis...
Examples
Embodiment Construction
[0040] Such as figure 1The construction method of the large data acceleration structure of the present invention shown includes:
[0041] A. Data preprocessing: Data cleaning, data integration and data conversion are performed on the original data to form a data set that conforms to the operation process.
[0042] Data cleaning described therein includes deletion of raw data, filling of missing values and smoothing / filtering of noisy data.
[0043] In deleting original data and filling in missing values, the missing data is judged by the attribute weight coverage p of the original data, and the attribute weight coverage p is:
[0044]
[0045] Where A is the data attribute, ε is the attribute weight, indicating the importance of the attribute, and ε 1 +ε 2 +...+ε k =1, α indicates whether the attribute value is missing, α∈{0,1}, set the threshold ω, the value range of the threshold ω is (0,1), if p≥ω, fill in the missing value for the missing data, otherwise, then de...