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首页> 外文会议>International Symposium on Software Engineering for Adaptive and Self-Managing Systems >Using Unstructured Data to Improve the Continuous Planning of Critical Processes Involving Humans
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Using Unstructured Data to Improve the Continuous Planning of Critical Processes Involving Humans

机译：使用非结构化数据改进涉及人类的关键过程的连续计划

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The success of processes executed in uncertain and changing environments is reliant on the dependable use of relevant information to support continuous planning at runtime. At the core of this planning is a model which, if incorrect, can lead to failures and, in critical processes such as evacuation and disaster relief operations, to harm to humans. Obtaining reliable and timely estimations of model parameters is often difficult, and considerable research effort has been expended to derive methods for updating models at run-time. Typically, these methods use data sources such as system logs, run-time events and sensor readings, which are well structured. However, in many critical processes, the most relevant data are produced by human participants to, and observers of, the process and its environment (e.g., through social media) and is unstructured. For such scenarios we propose COPE, a work-in-progress method for the continuous planning of critical processes involving humans and carried out in uncertain, changing environments. COPE uses a combination of runtime natural-language processing (to update a stochastic model of the target process based on unstructured data) and stochastic model synthesis (to generate Pareto-optimal plans for the process). Preliminary experiments indicate that COPE can support continuous planning effectively for a simulated evacuation operation after a natural disaster.
机译：在不确定和变化的环境中执行的过程的成功取决于对相关信息的可靠使用，以支持运行时的连续计划。该计划的核心是一个模型，如果模型不正确，可能会导致失败，并在诸如疏散和救灾行动等关键过程中造成对人类的伤害。获得模型参数的可靠和及时的估计通常很困难，并且已经花费了大量的研究工作来得出在运行时更新模型的方法。通常，这些方法使用结构良好的数据源，例如系统日志，运行时事件和传感器读数。但是，在许多关键过程中，最相关的数据是由人类参与者向过程及其环境（例如，通过社交媒体）提供的，并且是该过程及其环境的观察者，并且是无结构的。对于这种情况，我们提出了COPE，这是一种进行中的方法，用于持续规划涉及人类的关键过程，并且是在不确定，变化的环境中进行的。 COPE使用运行时自然语言处理（基于非结构化数据更新目标流程的随机模型）和随机模型综合（生成流程的帕累托最优计划）的组合。初步实验表明，COPE可以有效支持连续计划，以应对自然灾害后的模拟疏散行动。

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