An integrated framework for managing sensor data uncertainty using cloud computing

  • Authors:

  • Byunggu Yu;Ranjan Sen;Dong H. Jeong

  • Affiliations:

  • Department of Computer Science and Information Technology, University of the District of Columbia, USA;Department of Computer Science and Information Technology, University of the District of Columbia, USA;Department of Computer Science and Information Technology, University of the District of Columbia, USA

  • Venue:
  • Information Systems
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

In recent years, an increasing number of data-intensive applications deal with continuously changing data objects (CCDOs), such as data streams from sensors and tracking devices. In these applications, the underlying data management system must support new types of spatiotemporal queries that refer to the spatiotemporal trajectories of the CCDOs. In contrast to traditional data objects, CCDOs have continuously changing attributes. Therefore, the spatiotemporal relation between any two CCDOs can change over time. This problem can be more complicated, since the CCDO trajectories are associated with a degree of uncertainty at every point in time. This is due to the fact that databases can only be discretely updated. The paper formally presents a comprehensive framework for managing CCDOs with insights into the spatiotemporal uncertainty problem and presents an original parallel-processing solution for efficiently managing the uncertainty using the map-reduce platform of cloud computing.