• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistics and computing >Composable models for online Bayesian analysis of streaming data
【24h】

Composable models for online Bayesian analysis of streaming data

机译：在线贝叶斯流数据分析的可组合模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Data is rapidly increasing in volume and velocity and the Internet of Things (IoT) is one important source of this data. The IoT is a collection of connected devices (things) which are constantly recording data from their surroundings using on-board sensors. These devices can record and stream data to the cloud at a very high rate, leading to high storage and analysis costs. In order to ameliorate these costs, the data is modelled as a stream and analysed online to learn about the underlying process, perform interpolation and smoothing and make forecasts and predictions. Conventional state space modelling tools assume the observations occur on a fixed regular time grid. However, many sensors change their sampling frequency, sometimes adaptively, or get interrupted and re-started out of sync with the previous sampling grid, or just generate event data at irregular times. It is therefore desirable to model the system as a partially and irregularly observed Markov process which evolves in continuous time. Both the process and the observation model are potentially non-linear. Particle filters therefore represent the simplest approach to online analysis. A functional Scala library of composable continuous time Markov process models has been developed in order to model the wide variety of data captured in the IoT.
机译：数据的数量和速度正在迅速增加，物联网（IoT）是该数据的重要来源之一。物联网是连接的设备（事物）的集合，这些设备不断使用车载传感器记录来自其周围环境的数据。这些设备可以以很高的速率记录数据并将其流式传输到云，从而导致高昂的存储和分析成本。为了改善这些成本，将数据建模为数据流，并在线进行分析以了解基础过程，执行插值和平滑以及进行预测和预测。传统的状态空间建模工具假定观察发生在固定的规则时间网格上。但是，许多传感器有时会自适应地更改其采样频率，或者被中断并与先前的采样网格不同步地重新启动，或者只是在不规则的时间生成事件数据。因此，期望将系统建模为部分地和不规则地观察到的马尔可夫过程，该过程在连续时间内演化。过程和观察模型都可能是非线性的。因此，粒子过滤器代表了最简单的在线分析方法。已经开发了可组合的连续时间马尔可夫过程模型的功能性Scala库，以对IoT中捕获的各种数据建模。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号