EAGER-DynamicData: Real-time Discovery and Timely Event Detection from Dynamic and Multi-Modal Data Streams

紧急响应人员(警察、消防、救护车服务)可以接触到越来越多的数据流：传感器读数、安全摄像头、个人报告(通过手机、短信、推文)、GPS数据等。这些数据流的可用性带来了巨大的机会-但也带来了根本的挑战：*数据流来自广泛的来源，包含许多不同的特征；这使得从数据流中提取信息变得困难，尤其是从不同的数据流中集成信息。*从过去事件中学到的知识必须转化为关于现在(和未来)事件的知识。因为没有两个事件是完全相同的，所以从过去事件中学到的知识必须被转移到关于当前事件的知识中，这些事件不是完全相同的，而只是“相似的”--而且是以事先可能不知道的方式，因此必须被发现。*学习和检测--学习和检测之后的行动？必须以及时的方式进行：只有在紧急情况过去很长一段时间后才学习如何应对紧急情况是没有什么用处的。为了实现这一点，拟议的工作依赖于新的方法来发现每个单独的数据流和跨数据流的相关内容，并学习和利用过去和现在之间的相似之处。这项工作具有变革性，该项目的成功有可能极大地加强、甚至挽救生命，以应对各种紧急情况。现有方法通过利用信号的特定物理特性来处理单个数据流，并以自组织方式处理多个数据流。这些方法忽略了这样一个事实，即重要的不是信号的物理特征，而是信号中的(语义)信息，以及不同数据流中的信息之间存在联系。这个项目通过关注每个数据流、跨数据流和时间的信息的相关性，转变了从多个(多模式)数据流中学习的问题。对于不同的事件和不同的目的，相关信息通常是不同的，而且不会事先知道，因此必须了解相关性。为此，该项目按照编码外生元数据(例如，何时、何地和由谁收集数据)和内生元数据(例如，从数据中提取的特征和统计数据)的上下文来组织每个时刻可用的信息。一般而言，有大量和各种各样的上下文，但最相关的信息只嵌入到少数几个上下文中。由于这些最相关的上下文一般不会预先知道，并且在不同的场景中会有所不同，因此该项目将开发一类新的方法和算法，从多个动态、多模式和高维数据流中发现相关上下文，并使用发现的内容来及时学习、检测和响应。由于没有两个事件是完全相同的，这个项目将开发一类新的方法和算法，用于发现相关的语义相似性及其应用，从而有可能将从过去事件中学到的知识转化为关于当前事件的知识。这项工作需要开发高度创新的方法和技术，这些方法和技术远远超出现有工作(高风险)，并具有潜在的变革性，适用于从事件检测到可操作情报的各种应用。

Emergency responders (police, fire, ambulance services) have more and more access to more and more data stream: sensor readings, security cameras, personal reports (via cellphone, texts, tweets), GPS data etc. The availability of these data streams presents enormous opportunities - but also poses fundamental challenges:* Data streams arrive from a wide variety of sources and contain many diverse features; this makes it difficult to extract information from the streams, and especially, to integrate information from different streams. * Knowledge learned from past events must be transferred to knowledge about present (and future) events. Because no two events are ever identical, the knowledge learned from past events must be transferred to knowledge about present events that are not identical but only "similar" - and in ways that may not be known in advance and so must be discovered. * Learning and detection - and the actions that follow learning and detection ? must take place in a timely fashion: it is of little use to learn how to respond to an emergency only long after the emergency has passed. To accomplish this, the proposed work relies on new methods to discover what is relevant both in each individual data stream and across data streams, and to learn and exploit the similarities between the past and the present. This work is transformative and success in this project has the potential to lead to enormously enhanced, even life-saving, responses to emergencies of many sorts. Existing approaches treat individual data streams by exploiting particular physical characteristics of the signal, and treat multiple data streams in an ad-hoc fashion. These approaches miss the fact that it is not the physical characteristics of the signal that are important but rather the (semantic) information in the signal, and that there are connections between the information in different data streams. This project transforms the problem of learning from multiple (multi-modal) data streams by focusing on the relevance of information in each data stream, across data streams, and through time. The relevant information will generally be different for different events and different purposes and will not be known in advance, so relevance must be learned. To do this, this project organizes the information available at each moment in time in terms of contexts which encode exogenous metadata (e.g., when, where and by whom data was gathered) and endogenous metadata (e.g., features and statistics extracted from the data). In general, there are an enormous number and variety of contexts, but the most relevant information is embedded in only a few contexts. Because these most relevant contexts will not generally be known in advance and will be different in different scenarios, this project will develop a new class of methods and algorithms to discover the relevant contexts from multiple dynamic, multi-modal and high-dimensional data streams, and to use what is discovered to learn, detect and respond in a timely fashion. Because no two events are exactly the same, this project will develop of a new class of methods and algorithms for the discovery of relevant semantic similarities and their application, making it possible to transfer knowledge learned from past events to knowledge about present events. This work requires the development of highly innovative methodology and techniques that go far beyond existing work (high risk) and are potentially transformative for a wide variety of applications ranging from event detection to actionable intelligence.