A Data Integration Framework for Urban Systems Analysis Based on Geo-Relationship Learning
A Data Integration Framework for Urban Systems Analysis Based on Geo-Relationship Learning
复制标题
基于地理关系学习的城市系统分析数据集成框架
DOI：
10.1061/9780784480823.056
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Jain, Rishee K.
中科院分区：
文献类型：
--
作者：
Yang, Zheng;Gupta, Karan;Gupta, Archana;Jain, Rishee K.
世界正在迅速城市化，有史以来第一次超过50%的世界人口居住在城市地区。这种快速的城市化在治理、基础设施和环境的交叉点上带来了巨大的挑战。先进的传感和数据分析技术已经在所谓的“智能城市”的背景下发展起来，其目标是提供关于如何更有效地设计和管理城市系统的见解。然而，来自不同来源的数据的扩散使得这种城市数据流的互操作性和挖掘变得困难。为了促进提取支持基于数据的决策和计划建议的见解，需要整合此类异构数据流的框架。在本文中，我们介绍了一种新的数据集成框架，利用RDF（资源描述框架）模型集成不同的城市数据流的基础上，反复学习的语义信息和关系数据库的结构的地理关系。我们框架的开发是由负责管理和整合城市数据的城市官员的采访和观察以及对部门数据库，传感器和众包等来源生成的各种类型的不同数据集的审查驱动的。最后，我们将我们提出的框架应用到城市数据场景中，以证明该框架的适用性和实用性。
The world is rapidly urbanizing, and for the first time in history over 50% of the world’s population reside in urban areas. This rapid urbanization brings about tremendous challenges at the intersection of governance, infrastructure and the environment. Advanced sensing and data analytics techniques have been developed in the context of so called “smart cities” with the goal of providing insights on how urban systems could be designed and managed more effectively. However, the proliferation of data from heterogeneous sources makes interoperability and mining of such urban data streams difficult. Facilitating the extraction of insights that support data-informed policymaking and program recommendations will require frameworks to integrate such heterogeneous data streams. In this paper, we introduce a novel data integration framework that utilizes an RDF (resource description framework) model to integrate disparate urban data streams based on geo-relationships that are iteratively learned from semantic information and the structure of relational databases. The development of our framework was driven by interviews and observations of city officials responsible for managing and integrating urban data and a review of the various types of disparate datasets generated from sources like departmental databases, sensors, and crowdsourcing. Finally, we apply our proposed framework to an urban data scenario in order to demonstrate the applicability and usefulness of the framework.
挖掘关系数据库的内容以学习具有更深入分类的本体
DOI：
10.1109/wiiat.2008.382
发表时间：
2008
期刊：
2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
影响因子：
--
作者：
Farid Cerbah
通讯作者：
Farid Cerbah
从关系数据库学习高度结构化的语义存储库：
影响因子：
3.9
作者：
Farid Cerbah
通讯作者：
Farid Cerbah
三十年的数据集成 - 所有问题都解决了吗？
影响因子：
3.9
作者：
P. Ziegler;K. Dittrich
通讯作者：
K. Dittrich