Conference
Quality of Context
About: Quality of Context is an academic conference. The conference publishes majorly in the area(s): Middleware & Web 2.0. Over the lifetime, 2 publications have been published by the conference receiving 17 citations.
Papers
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25 Jun 2009
TL;DR: Over the past two decades substantial progress has been made on the theory and methods of geospatial uncertainty, but hard problems remain in several areas, including uncertainty visualization and propagation.
Abstract: The location of an event or feature on the Earth's surface can be used to discover information about the location's surrroundings, and to gain insights into the conditions and processes that may affect or even cause the presence of the event or feature. Such reasoning lies at the heart of critical spatial thinking, and is increasingly implemented in tools such as geographic information systems and online Web mashups. But the quality of contextual information relies on accurate positions and descriptions. Over the past two decades substantial progress has been made on the theory and methods of geospatial uncertainty, but hard problems remain in several areas, including uncertainty visualization and propagation. Web 2.0 mechanisms are fostering the rapid growth of user-generated geospatial content, but raising issues of associated quality.
17 citations
25 Jun 2009
TL;DR: A middleware named UDS (Uninterruptible Data Supply System) is proposed, which compensates the missing data, creates virtually complete dataset and provides upper layer applications and created a robust model for both patterns utilizing Bayesian Network.
Abstract: Context mining algorithms from sensor data have been researched and successful results have been shown However, since these existing works are focused on improving the accuracy of context mining, they are established on the assumption that they can acquire a complete set of necessary data Therefore, the context mining algorithms do not work sufficiently since the data drops easily in the reality In this paper, to cope with this problem, we propose a middleware named UDS (Uninterruptible Data Supply System) The system compensates the missing data, creates virtually complete dataset and provides upper layer applications Applications operating over UDS can work sufficiently with some data actually missing We have defined two types of characteristic data deficit patterns and created a robust model for both patterns utilizing Bayesian Network In the evaluation, we show UDS can sustain the quality of context over 80% with 40% data missing