Data Verification for Collective Adaptive Systems: Spatial Model-Checking of Vehicle Location Data

V. Ciancia, S. Gilmore, D. Latella, M. Loreti, M. Massink
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引用次数: 29

Abstract

In this paper we present the use of a novel spatial model-checker to detect problems in the data which an adaptive system gathers in order to inform future action. We categorise received data as being plausible, implausible, possible or problematic. Data correctness is essential to ensure correct functionality in systems which adapt in response to data and our categorisation influences the degree of caution which should be used in acting in response to this received data. We illustrate the theory with a concrete example of detecting errors in vehicle location data for buses in the city of Edinburgh. Vehicle location data is visualised symbolically on a street map, and categories of problems identified by the spatial model-checker are rendered by repainting the symbols for vehicles in different colours.
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集体自适应系统的数据验证:车辆位置数据的空间模型检验
在本文中,我们提出了一种新的空间模型检查器来检测自适应系统收集的数据中的问题,以便为未来的行动提供信息。我们将收到的数据分为可信、不可信、可能或有问题。数据正确性对于确保系统的正确功能至关重要,这些系统可以根据数据进行调整,我们的分类影响了在响应接收到的数据时应该使用的谨慎程度。我们用爱丁堡市公交车车辆位置数据误差检测的具体例子来说明这一理论。车辆位置数据在街道地图上以符号形式可视化,由空间模型检查器识别的问题类别通过重新绘制不同颜色的车辆符号来呈现。
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