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  1. Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '10)
  2. Modeling and prediction of moving region trajectories
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Microsoft framework for geospatial stream processing
Geostreaming at IBM: infosphere streams and intelligent traffic systems
The Oracle platform for real time streaming event driven architecture based solutions
Spatiotemporal summarization of traffic data streams
A data stream-based evaluation framework for traffic information systems
Framing the question: detecting and filling spatial-temporal windows
Modeling and prediction of moving region trajectories
AIMS: an SQL-based system for airspace monitoring
OpenSense: open community driven sensing of environment
Querying streaming point clusters as regions
A programming framework for integrating web-based spatiotemporal sensor data with MapReduce capabilities
Exploratory novelty identification in human activity data streams

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Modeling and prediction of moving region trajectories

Content Provider ACM Digital Library
Author Gertz, Michael Junghans, Conny
Abstract Data about moving objects is being collected in many different application domains with the help of sensor networks, GPS-enabled devices, and in particular airborne sensors and satellites. Such moving objects often represent not just point-based objects, but rather moving regions like hurricanes, oil-spills, or animal herds. One key application feature users are often interested in is the exploration and prediction of moving object trajectories. While there exist models and techniques that help to predict the movement of moving point objects, no such method for moving regions has been proposed yet. In this paper, we present an approach to model and predict the development of moving regions. Our method not only predicts the trajectory of regions, but also the evolution of a region's spatial extent and orientation. For this, moving regions are modelled using minimum enclosing boxes, and evolution patterns of regions are determined using linear regression and a recursive motion function. We demonstrate the functionality and effectiveness of the proposed technique using real-world sensor data from different application domains.
Starting Page 23
Ending Page 30
Page Count 8
File Format PDF
ISBN 9781450304313
DOI 10.1145/1878500.1878507
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-11-02
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Moving regions Prediction Stream processing Trajectories
Content Type Text
Resource Type Article
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