Event detection algorithms in decision support for animals health surveillance

能够快速识别和描述重大疾病事件的综合动物卫生监测系统是维持畜牧业安全、经济可行性和长期可持续性所必需的基本基础设施。如果不能及时发现外来疾病的入侵或以前未知的新疾病在广泛传播之前的突发事件，可能会导致灾难性的疾病暴发。有效发现和描述对动物卫生至关重要的事件的挑战在监测系统中是显而易见的。这些事件虽然十分罕见，但却极为重要。终端用户（社区、各级决策者）对事件检测的质量、其清晰的特征（描述）和与事件本身及其相关量化（例如，其严重性、传播等）相关的置信度有很高的期望。用户期望能够有效地访问和总结大量的时空数据，揭示和传达“有趣”的发现；N种易于理解的事件描述符形式，以便进行进一步的操作。这些对事件检测的期望构成了当今的挑战之一；智能监控系统的设计。这类系统以用户为中心是真正必要的，这样它们才能为利益相关者提供充分的好处。在与我们的工业合作伙伴AQL管理咨询公司（AQLMC）和我们的支持组织阿尔伯塔农业和农村发展的密切合作下，我们提出了利用计算智能范式的创新事件检测框架的设计和部署，该框架在这里作为智能系统的全面和统一的设计框架。所提出的事件检测方案将在数据流异构的情况下实现，其中每个数据流都有自己的强度（生成的数据量）、数据的性质（数字时间序列、图像、视频、文本信息）和底层信息的粒度。项目中要处理的另一个有趣且重要的设计方面涉及事件检测器聚合的有效方法的开发。

Comprehensive animal health surveillance systems that can quickly identify and characterize important disease events are essential infrastructure necessary to maintain the security, economic viability and long term sustainability of livestock industries. Failing to detect incursions of foreign diseases or emergencies of new, previously unknown diseases before they are widely dispersed can result in catastrophic disease outbreaks. The challenges to effectively detect and describe events essential to animal health are inherently visible in surveillance systems. These events, albeit being quite rare, are of paramount importance. There are high expectations of the end-user (community, decision-makers at various levels) as to the quality of the detection of the event, its clear characterization (description) and confidence level associated with the event itself and its associated quantification (e.g., its severity, spread, etc.). Users anticipate that voluminous spatial-temporal data can be effectively accessed and summarized, with "interesting" findings revealed and communicated;n forms of event descriptors that are easily understood so that one can proceed with further actions. These expectations regarding event detection constitute one of today's challenges ;n the design of intelligent surveillance systems. User-centricity of such systems becomes a genuine necessity so that they offer full benefits to stakeholders. In close collaboration with our industrial partner, AQL Management Consulting, Inc. (AQLMC), and our supporting organization, Alberta Agriculture and Rural Development, we propose the design and deployment of an innovative event detection framework exploiting the paradigm of Computational Intelligence which serves here as a comprehensive and unified design framework for intelligent systems. The proposed event detection schemes will be realized in the presence of heterogeneity of data streams where each of them come with its own intensity (amount of data being generated), nature of data (numeric time series, images, video, textual information), and -granularity of underlying information. Another interesting and important design aspect to be dealt with in the project concerns the development of efficient ways of aggregation of event detectors.