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IDEAS Factory - Detecting Terrorist Activities: Making Sense

IDEAS Factory - 检测恐怖活动：有意义

基本信息

批准号：
EP/H023135/1
负责人：
Chris Hankin
金额：
$ 278.43万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FH023135%2F1
关键词：
IDEAS Factory Detecting Terrorist Activities

项目摘要

The key challenge that this proposal addresses is the analysis and visualization of multiple sources of multi-modal data that may be partial, unreliable and contradictory. In addressing this challenge we expect to create an interactive visualization-based decision support assistant which collects data, fuses it, analyses it and visualizes the results in a way which can be shared by analysts. Each aspect of the assistant poses significant new scientific challenges. We envisage a systems architecture which has four main components:Collection of data encompasses automated approaches to gist-ing multimedia content (extraction of the gist of the information -- a process of abstraction which might, for example, summarise a phone message by the caller phone number, the phone number of the recipient, its duration, key words and phrases which stand out as most significant, and potentially suspicious words and phrases) and data management and resource allocation issues. Fusion and inference involves the integration of different modalities of data of variable reliability, the estimation of missing data for use in scenario development, methods for the resolution of contradictions, and psychological studies of how analysts relate information in this setting. We will investigate whether work on GIS which has developed meta-languages such as UnCertML can be used in this process.Analysis involves further summarizing of the fused data; we will build on existing machine learning techniques but anticipate that the characteristics of the data (which include temporal and spatial information as well as the uncertainty aspects discussed above) will pose significant new challenges. A particular challenge which will require psychological input is the drawing of relevant connections that have arisen in the fused data. Visualization must be informed by the operational model(s) of the data analyst(s), risk assessment and by legal considerations. The key challenge is to find a flexible, interactive way of visualising the data that allows the analyst to query the data and focus attention in a natural way. A key aspect of the visualization system is that it forms both the input and output of the system allowing the complexity of the data and underlying system to be hidden beneath an intuitive interface.Visual analytics is the emerging science of making sense of large data sets, that through the use of interactive visualization and query, supports the analytic reasoning process. The interactive visualization interface will support sense-making, query formulation, and information search by showing in visual representations associations and relationships between large, mixed-format and loosely-coupled data sets, such as un-structured reports, news feeds, photos, and structured databases. Very importantly as well, there is also a need for an intermediate layer that may draw on a variety of computing technologies e.g. Latent Semantic Analysis, ontologies) to enable the extraction of semantically meaningful relationships between data sets. In addition, by enabling changes in viewing perspectives (e.g. rotation, re-ordering, re-collating) it will facilitate the chance discovery of un-anticipated associations and resources. One of the original features of this proposal is that the system developed will be based on an analysis of user requirements undertaken by psychologists in the team. Psychological research into the behaviour of analysts has been undertaken elsewhere, but we believe this is the first research project that directly integrates psychological findings from research with analysts, into the development of a decision support system.

该提案所针对的主要挑战是分析和可视化可能不完整、不可靠和相互矛盾的多模态数据的多个来源。在应对这一挑战时，我们希望创建一个基于交互式可视化的决策支持助手，该助手收集数据，融合数据，分析数据，并以分析师可以共享的方式将结果可视化。助理的每个方面都提出了重大的新科学挑战。我们设想了一个系统架构，它有四个主要组成部分：收集数据包括自动化的方法gist-ing多媒体内容（提取的要点的信息-一个抽象的过程，例如，可能会总结一个电话信息的来电者的电话号码，电话号码的收件人，其持续时间，关键词和短语，突出为最重要的，和潜在的可疑的单词和短语）和数据管理和资源分配问题。融合和推理涉及不同形式的可变可靠性数据的整合，用于情景开发的缺失数据的估计，解决矛盾的方法，以及分析师如何在这种情况下将信息联系起来的心理学研究。我们将研究是否可以在这个过程中使用GIS的工作，开发了元语言，如UnCertML。分析涉及融合数据的进一步总结;我们将建立在现有的机器学习技术，但预计数据的特性（包括时间和空间信息以及上面讨论的不确定性方面）将带来重大的新挑战。需要心理投入的一个特殊挑战是绘制融合数据中出现的相关联系。可视化必须由数据分析师的操作模型、风险评估和法律的考虑因素提供信息。关键的挑战是找到一种灵活的、交互式的数据可视化方式，使分析师能够以自然的方式查询数据并集中注意力。可视化系统的一个关键方面是它形成了系统的输入和输出，允许数据和底层系统的复杂性隐藏在直观的界面之下。可视化分析是一门新兴的科学，它通过使用交互式可视化和查询来理解大型数据集，支持分析推理过程。交互式可视化界面将支持意义的形成，查询公式，和信息搜索通过显示在视觉表示的关联和大型，混合格式和松散耦合的数据集，如非结构化的报告，新闻提要，照片和结构化的数据库之间的关系。同样非常重要的是，还需要一个中间层，该中间层可以利用各种计算技术（例如潜在语义分析、本体）来实现数据集之间语义上有意义的关系的提取。此外，通过改变观看视角（例如旋转、重新排序、重新整理），它将促进意外关联和资源的偶然发现。这项建议的一个原始特点是，所开发的系统将以小组中的心理学家对用户需求进行的分析为基础。对分析师行为的心理学研究已经在其他地方进行，但我们相信这是第一个直接将心理学研究结果与分析师整合到决策支持系统开发中的研究项目。