Misson-relevant information management for integrated response (MIMIR)

现代军事面临着许多与信息时代直接相关的挑战。特别令人关切的是，与军事有关的信息源(例如，舰载传感器、商业数据库)的数量越来越多，它们提供的数据量也在不断增加。加拿大皇家海军特别受数据量问题的影响，因为与陆上网络相比，海上平台的通信能力更有限。加拿大国防研究发展部、洛克希德·马丁加拿大公司和达尔豪西大学提出了一个合作研究项目，旨在解决与动态变化的云状环境中有效和高效的数据采集、数据生产和数据服务相关的一些问题。我们的工作将集中在与以下场景相关的数据科学问题上：多个友好资产(海军舰艇、基站、地面单位的联盟)正在调查特定的船只。每项资产的决策者将根据他/她自己的能力对世界有一个观点，他/她只处理(和所有)从其传感器收集的相关数据，并通过与其他资产的可变连接访问获得额外的信息。资产之间的连接水平不同，从完全互联网式的连接到零连接。这一背景将推动我们在数据管理、数据分析、协作社区云和与任务相关的数据可视化方面的协作和应用研究。我们还将考虑在国际救灾工作中的信息管理中使用双重用途应用程序，当多个不同的合作伙伴在短时间内聚集在一起，需要跟踪关于人员、区域条件、基础设施、安全/不安全地区、天气等的大量数据(其中一些数据来自社交网络)时。总的来说，融合来自多个来源的智能/相关数据的能力将有助于将加拿大工业，特别是洛克希德·马丁公司，定位为在与各种任务的数据资源识别和开发相关的问题上处于国际领先地位。这项研究将有助于建立一个更灵活、更有效的加拿大皇家海军舰队，并适当地定位该舰队，以处理未来的数据量。

Today's modern military faces many challenges directly related to the information age. Of particular concern is the increasing number of military-relevant information sources (e.g., on-vessel sensors, commercial databases) that are providing ever increasing volumes of data. The Royal Canadian Navy is particularly influenced by data volume issues, as at-sea platforms have a more limited communication capacity as compared to land-based networks. Defence Research Development Canada, Lockheed Martin Canada and Dalhousie University propose a collaborative research project tackling some of the issues related to effective and efficient data harvesting, data production, and data serving in a dynamically changing, cloud-like environment. Our work will focus on the data science issues related to the following scenario: multiple friendly assets (a coalition of navy vessels, base stations, ground units) are investigating a particular vessel. The decision maker in each asset will have a point of view about the world based on his/her own capabilities to process only (and all) relevant data collected from its sensors, augmented with additional information available by variable connection access with other assets. Connectivity levels between assets differ, ranging from full internet-style connectivity to zero connectivity. This context will drive our collaborative, applied research in data management, data analytics, collaborative community cloud, and task-relevant data visualization. We will also consider a dual use application in information management in an international disaster relief effort, when multiple heterogeneous partners come together on short notice with a need to track significant quantities of data - some of it derived from social networks - about people, area conditions, infrastructure, safe/unsafe areas, weather, etc. In general, the capability to fuse intelligent/pertinent data from multiple sources will help position Canadian industry, and Lockheed Martin in particular, to be an international leader in issues related to data resource identification and exploitation for a varied mission set. This research will contribute to a more agile and effective RoyalCanadian Navy fleet and properly position the fleet to handle data volumes of the future.