Event detection algorithms in decision support for animals health surveillance

全面的动物健康监测系统能够快速识别和确定重要疾病事件的特征，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础设施。在新的、以前未知的疾病广泛传播之前，未能发现外来疾病的入侵或紧急情况，可能会导致灾难性的疾病暴发。在监测系统中，有效地检测和描述对动物健康至关重要的事件本身就存在挑战。这些事件虽然非常罕见，但具有极其重要的意义。终端用户(社区、不同级别的决策者)对事件的检测质量、其与事件本身相关联的清晰表征(描述)和置信度及其相关量化(例如，其严重性、扩散等)有很高的期望。用户期望能够有效地访问和汇总大量的时空数据，并揭示和传达“有趣的”发现；以便于理解的事件描述符的形式，以便能够继续进行进一步的行动。对事件检测的这些期望构成了当今的挑战之一；在智能监视系统的设计方面。这种系统以用户为中心成为一种真正的必要性，这样它们才能为利益攸关方提供充分的利益。通过与我们的工业合作伙伴AQL管理咨询公司(AQLMC)和我们的支持组织艾伯塔省农业和农村发展公司的密切合作，我们建议设计和部署一个创新的事件检测框架，利用计算智能的范式，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的异构性存在的情况下实现，其中每个数据流具有其自己的强度(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。该项目要处理的另一个有趣和重要的设计方面涉及到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.