III: Small: Discovering Hidden Semantics from Spatio-temporal Sensed Data

III：小：从时空感知数据中发现隐藏语义

• 批准号: 1527984
• 负责人: Vassilis Tsotras
• 金额: $ 50万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527984&HistoricalAwards=false
• 关键词: III; Small; Discovering; Hidden; Semantics

The increasing ubiquity of Global Positioning System devices and the widespread deployment of monitoring and tracking mechanisms, including video cameras, cellular phones, activity trackers and roadside sensors are creating very large spatio-temporal datasets that are extremely rich in semantics. Such data embed a great deal of high-level information on the behavior of moving objects as well their interactions with objects in the environment and with each other. Standard queries such as range queries and joins may be adequate for extracting the semantics of spatial data, but do scant justice to the inherent richness of spatio-temporal semantics. This project will develop approaches aimed specifically at extracting semantic and behavioral information from trajectory datasets. For instance, a moving object conducting surveillance on a region will remain in the vicinity of the region for the duration of surveillance. Detecting this behavior is difficult with standard approaches: a range query would require the user to specify the spatial and temporal ranges of surveillance, when the intent is, in fact, to determine both. The proposed work has many national security applications, including law enforcement, surveillance and security monitoring as well as social and commercial applications, such as in social networks, link analysis and epidemiology. The solutions developed will increase the utility of spatio-temporal data, specifically for tasks that require investigative exploration.This project addresses a novel class of problems in the spatio-temporal domain, which raise a novel set of technical challenges: First, it studies queries that elicit the semantics of interactions between moving objects and their environment. Second, it studies queries eliciting the semantics of interactions, such as potential meetings between moving objects themselves. Third, it explores such queries in the real-world contexts, where the available data is incomplete and imprecise. Examples of the first class of query include "dwell regions" queries, which explore spatio-temporal proximity between moving objects and objects in the environment, with applications to detection of surveillance of regions by moving objects. The project will also address "conclave" queries that illustrate the second and third challenges above. Such queries return possible meeting points between moving objects when their trajectories are not known fully or precisely. The proposed work also explores complex spatio-temporal "reachability" queries, which elicit possible interactions between objects via intermediaries. Finally, given the sheer magnitude of spatio-temporal datasets, this project aims to provide solutions that scale. A basic question is then how to efficiently index trajectory data. While there have been various past works in this domain, this project proposes a fourth challenge: can Hilbert Curves been used to index trajectories? Space filling curves (SFCs) have been shown to be advantageous in indexing spatial objects since they can reduce the space dimensionality. In past research, SFCs have been mainly used to index multidimensional points or order arbitrary objects; they have not though been directly used for indexing trajectories due to various challenges that this project aims to overcome. The project web site (http://www.cs.ucr.edu/~tsotras/semtraj/index.html) will provide access to the project results, including source code of the developed algorithms and relevant data.

全球定位系统装置的日益普及以及监测和跟踪机制的广泛部署，包括摄像机、移动电话、活动跟踪器和路边传感器，正在产生语义极其丰富的非常大的时空数据集。这些数据嵌入了大量关于移动对象的行为以及它们与环境中的对象以及彼此之间的交互的高级信息。标准查询，如范围查询和连接可能是足够的提取空间数据的语义，但不公正的时空语义的内在丰富性。该项目将开发专门针对从轨迹数据集中提取语义和行为信息的方法。举例来说，对一个区域进行监视的移动物体将在监视期间保持在该区域附近。使用标准方法检测这种行为是困难的：范围查询将要求用户指定监视的空间和时间范围，而实际上意图是确定两者。拟议的工作有许多国家安全应用，包括执法、监视和安全监测以及社会和商业应用，如社交网络、链接分析和流行病学。开发的解决方案将增加时空数据的实用性，特别是对于需要调查exploration.This项目的任务，解决了一类新的问题，在时空域，这提出了一系列新的技术挑战：首先，它研究查询，引起移动对象和他们的环境之间的交互的语义。其次，它研究查询引发的语义的相互作用，如移动对象本身之间的潜在会议。第三，它探讨了这样的查询在现实世界的背景下，可用的数据是不完整和不精确的。第一类查询的示例包括“驻留区域”查询，其探索移动对象与环境中的对象之间的时空接近度，应用于检测移动对象对区域的监视。该项目还将解决说明上述第二和第三个挑战的“秘密会议”问题。当移动对象的轨迹不完全或精确地知道时，这样的查询返回移动对象之间可能的会合点。拟议的工作还探讨了复杂的时空“可达性”查询，通过中介引起对象之间可能的交互。最后，鉴于时空数据集的庞大规模，该项目旨在提供可扩展的解决方案。一个基本的问题是如何有效地索引轨迹数据。虽然在这个领域已经有了各种过去的作品，这个项目提出了第四个挑战：希尔伯特曲线可以用来索引轨迹吗？空间填充曲线（SFC）已被证明是有利的索引空间对象，因为它们可以减少空间维数。在过去的研究中，SFC主要用于索引多维点或对任意对象进行排序;由于本项目旨在克服的各种挑战，它们尚未直接用于索引轨迹。该项目的网站（http：//www.cs.ucr.edu/semtsotras/semtraj/index.html）将提供项目结果的查阅途径，包括所开发算法的源代码和有关数据。