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A Platform Architecture for Sensor Data Processing and Verification in Buildings.

机译:用于建筑物中传感器数据处理和验证的平台架构。

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摘要

This thesis examines the state of the art of building information systems and evaluates their architecture in the context of emerging technologies and applications for deep analysis of the built environment. We observe that modern building information systems are difficult to extend, do not provide general services for application development, do not scale, and are difficult to set up and manage. We assert that a new architecture must be designed with four system properties --- extensibility, generalizability, scalability, ease of management --- in order to address these shortcomings. Our system, StreamFS, embodies these system properties through a filesystem abstraction and a set of data services.;Data services are made available to applications through an overloaded pipe abstraction. This allows for dataflow specification of processing streams to clean and analyze the streaming sensor data. We deploy StreamFS in seven different buildings and compose several applications on top of it. One of the driving applications is a phone application called the Mobile Energy Lens. The Energy Lens provides occupants with mechanisms for collecting building information in a unified platform and provides a way to view aggregate energy consumption data associated with the spatial deployment configuration of plug-load devices. We present a three-layer architecture, where one of the main layers is implemented entirely with the data management and processing services offered by StreamFS.;We introduce the notion of verification of physical relationships through empiricial data. We partition the verification problem into three sub problems: 1) functional verification, 2) spatial verification, and 3) categorical verification. We show how empirical mode decomposition, correlation, and standard machine learning techniques can give us information about how the sensors are related to each other, statistically and physically. We demonstate an extensible, generalizable, scalable, and easy-to-manage system for supporting the "appification" of the built environment.
机译:本文研究了建筑信息系统的最新技术,并在新兴技术和应用程序的背景下评估了其体系结构,以对建筑环境进行深入分析。我们发现,现代建筑信息系统很难扩展,不能为应用程序开发提供一般服务,不能扩展,并且难以建立和管理。我们断言,为了解决这些缺点,必须设计一种具有四个系统属性的新体系结构---可扩展性,通用性,可伸缩性,易于管理。我们的系统StreamFS通过文件系统抽象和一组数据服务体现了这些系统属性。数据服务通过重载管道抽象可用于应用程序。这允许处理流的数据流规范以清理和分析流传感器数据。我们在七个不同的建筑物中部署StreamFS,并在其之上组成几个应用程序。一种驱动应用是称为“移动能量透镜”的电话应用。能量透镜为乘员提供了在统一平台中收集建筑信息的机制,并提供了一种查看与即插即用设备的空间部署配置相关的总能耗数据的方法。我们提出了一个三层体系结构,其中主要层之一完全由StreamFS提供的数据管理和处理服务实现;我们引入了通过经验数据验证物理关系的概念。我们将验证问题分为三个子问题:1)功能验证,2)空间验证和3)分类验证。我们将展示经验模式分解,关联和标准机器学习技术如何为我们提供有关传感器如何在统计上和物理上相互关联的信息。我们演示了一个可扩展的,可推广的,可扩展的,易于管理的系统,用于支持构建环境的“应用”。

著录项

  • 作者

    Ortiz, Jorge Jose.;

  • 作者单位

    University of California, Berkeley.;

  • 授予单位 University of California, Berkeley.;
  • 学科 Computer Science.;Information Science.;Engineering System Science.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 185 p.
  • 总页数 185
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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