Mining Moving Object Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F12%3APU101756" target="_blank" >RIV/00216305:26230/12:PU101756 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/article/10.2478/s13537-012-0018-4" target="_blank" >http://link.springer.com/article/10.2478/s13537-012-0018-4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.2478/s13537-012-0018-4" target="_blank" >10.2478/s13537-012-0018-4</a>

Result language
angličtina
Original language name
Mining Moving Object Data
Original language description
Currently there is a lot of devices that provide information about moving objects and location-based services that accumulate huge volume of moving object data, including trajectories. This paper deals with two useful analysis tasks - mining moving object patterns and trajectory outlier detection. We also present our experience with the TOP-EYE trajectory outlier detection algorithm when we applied it on two real-world data sets.
Czech name
—
Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JC - Computer hardware and software
OECD FORD branch
—

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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