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| Data gathering and processing requires access to data and has several inherent issues, including information overload and data errors. Once data is collected, it will need to be transformed into a format that can be effectively used by both human and computer analyzers. Manual or computer-generated visualizations tools may be mapped from the data, including network charts. Several algorithms exist to help with analysis of data – Dijkstra’s algorithm, breadth-first search, and depth-first search. | | Data gathering and processing requires access to data and has several inherent issues, including information overload and data errors. Once data is collected, it will need to be transformed into a format that can be effectively used by both human and computer analyzers. Manual or computer-generated visualizations tools may be mapped from the data, including network charts. Several algorithms exist to help with analysis of data – Dijkstra’s algorithm, breadth-first search, and depth-first search. |
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− | 在得到数据后,需进行数据的收集和处理,但此过程存在一些固有的问题,包括信息超载和数据错误。在数据被收集后,它将转换成一种人和计算机分析程序都能有效使用的格式。之后基于数据,人工或计算机生成的可视化工具可以进行作图,包括网络图。目前有几种算法可以帮助进行数据分析-Dijkstra算法,广度优先搜索和深度优先搜索。
| + | 在得到数据后,需进行数据的收集和处理,但此过程存在一些固有的问题,包括信息超载和数据错误等。在数据被收集后,它将转换成一种人和计算机分析程序都能有效使用的格式。之后基于数据,人工或计算机生成的可视化工具可以进行作图,如网络图。目前有几种算法可以帮助进行数据分析-Dijkstra算法,广度优先搜索和深度优先搜索。 |
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| Link analysis focuses on analysis of relationships among nodes through visualization methods (network charts, association matrix). Here is an example of the relationships that may be mapped for crime investigations: | | Link analysis focuses on analysis of relationships among nodes through visualization methods (network charts, association matrix). Here is an example of the relationships that may be mapped for crime investigations: |
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− | 链路分析主要通过可视化方法(网络图、关联矩阵)分析节点之间的关系。这里有一个关系的例子,可以为犯罪调查绘制地图: | + | 链路分析主要通过可视化方法(网络图、关联矩阵)分析节点之间的关系。这里有一个基于罪犯和社会各部分关系绘制网图的例子: |
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| | 1. Trust || Prior contacts in family, neighborhood, school, military, club or organization. Public and court records. Data may only be available in suspect's native country. | | | 1. Trust || Prior contacts in family, neighborhood, school, military, club or organization. Public and court records. Data may only be available in suspect's native country. |
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− | | 1.信任 | 在家庭、社区、学校、军队、俱乐部或组织中有先前的联系。公开及法庭纪录。数据可能只能在嫌疑人的本国获得。 | + | | 1.信任 | 嫌疑人在家庭、社区、学校、军队、俱乐部或组织中已有的联系。公开信息及法庭纪录。以及只能在嫌疑人本国获得的数据。 |
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| | 2. Task || Logs and records of phone calls, electronic mail, chat rooms, instant messages, Web site visits. Travel records. Human intelligence: observation of meetings and attendance at common events. | | | 2. Task || Logs and records of phone calls, electronic mail, chat rooms, instant messages, Web site visits. Travel records. Human intelligence: observation of meetings and attendance at common events. |
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− | | 2.任务 | | 电话、电子邮件、聊天室、即时消息、网站访问的日志和记录。出入境纪录。人类智慧: 观察会议和参加共同活动。 | + | | 2.任务 | | 电话、电子邮件、聊天室、即时消息、网站访问的日志和记录。出入境纪录。人类智能: 会议评论和公共活动的出席。 |
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| | 3. Money & Resources || Bank account and money transfer records. Pattern and location of credit card use. Prior court records. Human intelligence: observation of visits to alternate banking resources such as Hawala. | | | 3. Money & Resources || Bank account and money transfer records. Pattern and location of credit card use. Prior court records. Human intelligence: observation of visits to alternate banking resources such as Hawala. |
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− | | 3.资金和资源 | 银行账户和汇款记录。信用卡使用模式及地点。以前的法庭记录。人类智慧: 观察访问其他银行资源,如 Hawala。 | + | | 3.资金和资源 | 银行账户和汇款记录。信用卡使用地点及使用习惯。以前的法庭记录。人类智能: 访问其他银行资源的观察,如 Hawala。 |
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| | 4. Strategy & Goals || Web sites. Videos and encrypted disks delivered by courier. Travel records. Human intelligence: observation of meetings and attendance at common events. | | | 4. Strategy & Goals || Web sites. Videos and encrypted disks delivered by courier. Travel records. Human intelligence: observation of meetings and attendance at common events. |
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− | | 4.策略与目标 | 网站。视频和加密光盘由快递公司递送。出入境纪录。人类智慧: 观察会议和参加共同活动。 | + | | 4.策略与目标 | 网站。由快递公司递送的视频和加密光盘。出入境纪录。人类智能: 会议评论和公共活动的出席。 |
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| |} | | |} |
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| Find matches in data for known patterns of interest; | | Find matches in data for known patterns of interest; |
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− | 在已知的兴趣模式的数据中寻找匹配项;
| + | 在数据中寻找有意义的已知模式; |
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| # Find anomalies where known patterns are violated; | | # Find anomalies where known patterns are violated; |
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| Find anomalies where known patterns are violated; | | Find anomalies where known patterns are violated; |
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− | 发现违反已知模式的异常;
| + | 发现与已知模式不符合的异常情况; |
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| # Discover new patterns of interest (social network analysis, [[data mining]]). | | # Discover new patterns of interest (social network analysis, [[data mining]]). |
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| Discover new patterns of interest (social network analysis, data mining). | | Discover new patterns of interest (social network analysis, data mining). |
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− | 发现感兴趣的新模式(社会网络分析、数据挖掘)。
| + | 发现有意义的新模式(社会网络分析、数据挖掘)。 |
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| Klerks categorized link analysis tools into 3 generations.<ref>{{cite journal | last = Klerks | first = P. | year = 2001 | title = The network paradigm applied to criminal organizations: Theoretical nitpicking or a relevant doctrine for investigators? Recent developments in the Netherlands | citeseerx = 10.1.1.129.4720 | journal = Connections | volume = 24 | pages = 53–65 }}</ref> The first generation was introduced in 1975 as the Anacpapa Chart of Harper and Harris.<ref>Harper and Harris, The Analysis of Criminal Intelligence, Human Factors and Ergonomics Society Annual Meeting Proceedings, 19(2), 1975, pp. 232-238.</ref> This method requires that a domain expert review data files, identify associations by constructing an association matrix, create a link chart for visualization and finally analyze the network chart to identify patterns of interest. This method requires extensive domain knowledge and is extremely time-consuming when reviewing vast amounts of data.[[File:Association Matrix.png|thumb|Association Matrix]] | | Klerks categorized link analysis tools into 3 generations.<ref>{{cite journal | last = Klerks | first = P. | year = 2001 | title = The network paradigm applied to criminal organizations: Theoretical nitpicking or a relevant doctrine for investigators? Recent developments in the Netherlands | citeseerx = 10.1.1.129.4720 | journal = Connections | volume = 24 | pages = 53–65 }}</ref> The first generation was introduced in 1975 as the Anacpapa Chart of Harper and Harris.<ref>Harper and Harris, The Analysis of Criminal Intelligence, Human Factors and Ergonomics Society Annual Meeting Proceedings, 19(2), 1975, pp. 232-238.</ref> This method requires that a domain expert review data files, identify associations by constructing an association matrix, create a link chart for visualization and finally analyze the network chart to identify patterns of interest. This method requires extensive domain knowledge and is extremely time-consuming when reviewing vast amounts of data.[[File:Association Matrix.png|thumb|Association Matrix]] |
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− | Klerks categorized link analysis tools into 3 generations. The first generation was introduced in 1975 as the Anacpapa Chart of Harper and Harris. This method requires that a domain expert review data files, identify associations by constructing an association matrix, create a link chart for visualization and finally analyze the network chart to identify patterns of interest. This method requires extensive domain knowledge and is extremely time-consuming when reviewing vast amounts of data.Association Matrix | + | Klerks categorized link analysis tools into 3 generations. The first generation was introduced in 1975 as the Anacpapa Chart of Harper and Harris. This method requires that a domain expert review data files, identify associations by constructing an association matrix, create a link chart for visualization and finally analyze the network chart to identify patterns of interest. This method requires extensive domain knowledge and is extremely time-consuming when reviewing vast amounts of data. |
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− | 把链接分析工具分为三代。第一代是在1975年被引入的,名字是哈珀和哈里斯的 Anacpapa Chart。这种方法需要一个领域专家审查数据文件,通过构造一个关联矩阵来识别关联,创建一个可视化的链接图,最后通过分析网络图来识别感兴趣的模式。这种方法需要广泛的领域知识,并且在审查大量数据时非常耗时
| + | Klerks把链接分析工具分为三代。第一代是由哈珀和哈里斯在1975年引入的,阿纳卡帕图。这种方法需要一个领域内的专家来审查数据文件,通过构造一个关联矩阵来识别关联,然后创建一个可视化的链路图,最后通过分析网络图来识别有意义的模式。这种方法需要广泛的领域知识,但因为要人工审查大量数据,所以非常耗时。 |
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