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The design of a cloud-based tracker platform based on system-of-systems service architecture

机译:基于系统服务架构的基于云的跟踪平台设计

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Devices embedded with position tracking facilities are now widely available, such as smartphones, smartwatches, vehicle location trackers, etc. However, data mining and advanced analytics are rarely bundled with these devices that limits their utility. In this paper, we present the design of a generic, programmable position tracking platform, namely CQtracker. In particular, this platform is incorporated with a cloud-based engine of advanced analytics. CQtracker is constructed based on a concept of system-of-systems service architecture to deliver data-system-as-a-service. It is designed for the consumption by a variety of spatio-temporal applications. Spatio-temporal data exhibit strong heterogeneous patterns, data sparseness and distribution skewness. Hence, they are difficult to analyze. CQtracker reveals relationships and structures from these data by self-regularized time-varying dynamic Bayesian networks. In addition, a Bayesian parameter estimation approach is applied to an epidemic model for outbreak predictions. Sample applications are presented in this paper, in which CQtracker successfully reveals the evolution of time-varying structures from traffic trajectories.
机译:嵌入了位置跟踪功能的设备现已广泛可用,例如智能手机,智能手表,车辆位置跟踪器等。但是,数据挖掘和高级分析很少与这些设备捆绑在一起,从而限制了它们的实用性。在本文中,我们介绍了通用的可编程位置跟踪平台CQtracker的设计。特别是,该平台与基于云的高级分析引擎结合在一起。 CQtracker是基于系统间服务体系结构的概念构建的,用于交付数据系统即服务。它是为各种时空应用程序而设计的。时空数据表现出很强的异构模式,数据稀疏性和分布偏斜性。因此,它们很难分析。 CQtracker通过自我调节的时变动态贝叶斯网络揭示了这些数据的关系和结构。另外,将贝叶斯参数估计方法应用于流行病模型以进行暴发预测。本文介绍了示例应用程序,其中CQtracker成功地揭示了交通轨迹随时间变化的结构的演变。

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