大数据法律监督建模的定位、流程与方法

The Principles,Processes,and Methods on the Modeling of Big Data Legal Supervision

大数据分析与建模技术为数字检察改革提供了坚实的技术支撑,提高了检察工作的质效。然而,当前大数据法律监督建模存在理论与实践的双重困境。大数据法律监督建模是创建供检察人员履行法律监督职权所使用的具有重复利用、实时性、可视化操作平台的技术活动。业务理解、数据理解与特征提取、检察数据挖掘、评估与验证、部署与优化构成了大数据法律监督建模的基本流程,其中检察数据挖掘是大数据法律监督建模的关键技术环节。未来在厘清大数据法律监督建模概念的基础上,可从拓展检察数据的来源并加强质量监管,推进对大数据法律监督建模软件的引入、使用和研发工作,制定完善的建模操作指南,加强复合型检察人才队伍建设等四个方面优化。

Big data analysis and modeling technologies have provided solid technical support for digital prosecution reforms and improved the efficiency and quality of prosecutorial work.However,there are theoretical and practical dilemmas in the current modeling of big data legal supervision.Big da-ta legal supervision modeling is a technical activity to create a reusable,timely and visual operation plat-form for procurators to perform their legal supervision duties.Business understanding,data understand-ing and feature extraction,procuratorial data mining,evaluation and verification,deployment and opti-mization are the basic processes for modeling big data legal supervision,among which procuratorial data mining is the key technical link of modeling.In the future,on the basis of clarifying the concept of big data legal supervision modeling,it can be optimized from four aspects:expanding the sources of procura-torial data and strengthening quality supervision,promoting the introduction,use and research and de-velopment of big data legal supervision modeling software,formulating perfect modeling operation guide-lines,and strengthening the construction of compound procuratorial talent teams.