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GPS CaPPture: A system for GPS trajectory collection, processing, and destination prediction.

机译:GPS CaPPture:用于GPS轨迹收集,处理和目的地预测的系统。

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摘要

In the United States, smartphone ownership surpassed 69.5 million in February 2011 with a large portion of those users (20%) downloading applications (apps) that enhance the usability of a device by adding additional functionality. A large percentage of apps are written specifically to utilize the geographical position of a mobile device. One of the prime factors in developing location prediction models is the use of historical data to train such a model. With larger sets of training data, prediction algorithms become more accurate; however, the use of historical data can quickly become a downfall if the GPS stream is not collected or processed correctly. Inaccurate or incomplete or even improperly interpreted historical data can lead to the inability to develop accurately performing prediction algorithms. As GPS chipsets become the standard in the ever increasing number of mobile devices, the opportunity for the collection of GPS data increases remarkably.;The goal of this study is to build a comprehensive system that addresses the following challenges: (1) collection of GPS data streams in a manner such that the data is highly usable and has a reduction in errors; (2) processing and reduction of the collected data in order to prepare it and make it highly usable for the creation of prediction algorithms; (3) creation of prediction/labeling algorithms at such a level that they are viable for commercial use. This study identifies the key research problems toward building the CaPPture (collection, processing, prediction) system.
机译:在美国,截至2011年2月,智能手机拥有量已超过6950万,其中很大一部分用户(占20%)下载了通过添加其他功能来增强设备可用性的应用程序。大量应用专门为利用移动设备的地理位置而编写。开发位置预测模型的主要因素之一是使用历史数据来训练这种模型。随着训练数据集的增加,预测算法变得更加准确;但是,如果GPS数据流未正确收集或处理,则历史数据的使用会很快成为一种失败。错误或不完整的历史数据,甚至解释不正确的历史数据都可能导致无法开发准确执行的预测算法。随着GPS芯片组成为越来越多的移动设备中的标准,收集GPS数据的机会显着增加。;本研究的目的是建立一个能够应对以下挑战的综合系统:(1)GPS的收集数据流以使数据高度可用并减少错误的方式进行; (2)处理和减少所收集的数据,以便对其进行准备并使其在创建预测算法时非常有用; (3)创建预测/标记算法,使其在商业上可行。这项研究确定了构建CaPPture(收集,处理,预测)系统的关键研究问题。

著录项

  • 作者

    Griffin, Terry W.;

  • 作者单位

    University of North Texas.;

  • 授予单位 University of North Texas.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 125 p.
  • 总页数 125
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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