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Application And System Improvement Of Big Data Analysis In The Investigation Of Multiple Cases Of Financial Assault

Posted on:2021-02-21Degree:MasterType:Thesis
Country:ChinaCandidate:J C WanFull Text:PDF
GTID:2416330629488323Subject:Law
Abstract/Summary:PDF Full Text Request
In the investigation work of public security organs,the common cases of money invasion are frequently happened,but the number of cases solved is not enough.In recent years,in the evolution of its own this kind of crime has formed some relatively stable characteristics,such as the obvious regional crime subject,crime range across regions and the crime is organized as a group,means of specialization,the investigation organ in combating such crimes are also facing some new difficulties,traditional detection methods can't meet the needs of the current strike such crimes.With the rapid development of the Internet,cloud computing and the Internet of Things,"big data" has quickly become the focus of people's attention,which has helped people better understand the world.However,the data involved in multiple financial crimes is rich with obvious regularity,and its characteristics coincide with those of big data.The application of big data in the investigation of multiple financial crimes has unparalleled advantages compared with traditional investigation measures,which can effectively improve the investigation efficiency and save the investigation cost.In the practice of investigation,data search,data mining,data collision and other analysis methods gradually come into people's view.Data search is relatively easy,mainly in the huge amount of data resources to find the data they want;Data collision refers to putting different databases together for collision comparison,so as to produce results that are not found by oneself.Data mining is a core technology,which USES intelligent means to discover the underlying laws behind the data.Only by introducing the above methods into the investigation practice and guiding the investigation practice can they play a great role in the aspects of crime early warning,investigation,case combination and crime digging,etc.It is believed that the application prospect will be more extensive in the future.However,while bringing convenience to people's lives,big data may also cause legal problems that have not been discovered before.The application of big data analysis in case investigation will inevitably cause damage to the personal information right that people already enjoy.For the investigation authorities,there is always anopposite side between the use of personal information and the protection of personal information.The investigation process is not open.For people,the investigation organs can collect data according to their needs,but they are not clear about it.For people,the investigation organs carry out some investigation means,but they are not clear about why they take such means.In the proceedings,the traditional judicial proof requires that there is a correlation between the evidence and the facts to be proved,and the "correlation" feature of big data will also have an impact on the causal relationship of the judicial proof.Whether big data technology can go further in the future investigation work depends on whether the existing problems brought by it can be solved.In particular,attention should be paid to ensure that it does not affect the recognized legal procedures and citizens' rights and interests.From the perspective of rights,it seeks the value balance between the application of big data in case investigation and personal information protection.From the procedural point of view,to ensure the interpretation of the parties in place,to ensure the basic rights of the parties,to ensure the integrity of the investigation procedure,to ensure the procedural impartiality;From the point of proof,it can be seamlessly connected with the traditional standard of judicial proof through the innovation of some methods.
Keywords/Search Tags:Frequent Property-Related Crimes, Big Data, Rights Protection, Proof Rules
PDF Full Text Request
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