Publication result detail

Mining Moving Object Data

ZENDULKA, J.; PEŠEK, M.

Original Title

Mining Moving Object Data

English Title

Mining Moving Object Data

Type

Peer-reviewed article not indexed in WoS or Scopus

Original Abstract

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.

English abstract

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.

Keywords

data mining, moving object data, trajectory, moving object patterns mining, trajectory outlier detection

Key words in English

data mining, moving object data, trajectory, moving object patterns mining, trajectory outlier detection

Authors

ZENDULKA, J.; PEŠEK, M.

RIV year

2013

Released

01.10.2012

ISBN

1896-1533

Periodical

Central European Journal of Computer Science

Volume

2

Number

3

State

Kingdom of the Netherlands

Pages from

183

Pages to

193

Pages count

11

URL

BibTex

@article{BUT96931,
  author="Jaroslav {Zendulka} and Martin {Pešek}",
  title="Mining Moving Object Data",
  journal="Central European Journal of Computer Science",
  year="2012",
  volume="2",
  number="3",
  pages="183--193",
  doi="10.2478/s13537-012-0018-4",
  issn="1896-1533",
  url="http://link.springer.com/article/10.2478/s13537-012-0018-4"
}