Mining spatio-temporal patterns in object mobility databases

Florian Verhein, Sanjay Chawla

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

With the increasing use of wireless communication devices and the ability to track people and objects cheaply and easily, the amount of spatio-temporal data is growing substantially. Many of these applications cannot easily locate the exact position of objects, but they can determine the region in which each object is contained. Furthermore, the regions are fixed and may vary greatly in size. Examples include mobile/cell phone networks, RFID tag readers and satellite tracking. This demands techniques to mine such data. These techniques must also correct for the bias produced by different sized regions. We provide a comprehensive definition of Spatio-Temporal Association Rules (STARs) that describe how objects move between regions over time. We also present other patterns that are useful for mobility data; stationary regions and high traffic regions. The latter consists of sources, sinks and thoroughfares. These patterns describe important temporal characteristics of regions and we show that they can be considered as special STARs. We define spatial support to effectively deal with the problem of different sized regions. We provide an efficient algorithm-STAR-Miner-to find these patterns by exploiting several pruning properties.

Original languageEnglish
Pages (from-to)5-38
Number of pages34
JournalData Mining and Knowledge Discovery
Volume16
Issue number1
DOIs
Publication statusPublished - Feb 2008
Externally publishedYes

Fingerprint

Spatio-temporal Patterns
Association rules
Mining
Association Rules
Miners
Radio frequency identification (RFID)
Communication
Spatio-temporal Data
Object
Radio Frequency Identification
Pruning
Wireless Communication
Efficient Algorithms
Traffic
Vary
Cell

Keywords

  • Sinks
  • Sources
  • Spatio-temporal association rules (STARs)
  • Spatio-temporal data mining
  • STAR-Miner
  • Stationary regions
  • Thoroughfares

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Information Systems
  • Computer Science (miscellaneous)
  • Mathematics (miscellaneous)

Cite this

Mining spatio-temporal patterns in object mobility databases. / Verhein, Florian; Chawla, Sanjay.

In: Data Mining and Knowledge Discovery, Vol. 16, No. 1, 02.2008, p. 5-38.

Research output: Contribution to journalArticle

@article{c5331b4a44d940879b3c706c4ca22ff8,
title = "Mining spatio-temporal patterns in object mobility databases",
abstract = "With the increasing use of wireless communication devices and the ability to track people and objects cheaply and easily, the amount of spatio-temporal data is growing substantially. Many of these applications cannot easily locate the exact position of objects, but they can determine the region in which each object is contained. Furthermore, the regions are fixed and may vary greatly in size. Examples include mobile/cell phone networks, RFID tag readers and satellite tracking. This demands techniques to mine such data. These techniques must also correct for the bias produced by different sized regions. We provide a comprehensive definition of Spatio-Temporal Association Rules (STARs) that describe how objects move between regions over time. We also present other patterns that are useful for mobility data; stationary regions and high traffic regions. The latter consists of sources, sinks and thoroughfares. These patterns describe important temporal characteristics of regions and we show that they can be considered as special STARs. We define spatial support to effectively deal with the problem of different sized regions. We provide an efficient algorithm-STAR-Miner-to find these patterns by exploiting several pruning properties.",
keywords = "Sinks, Sources, Spatio-temporal association rules (STARs), Spatio-temporal data mining, STAR-Miner, Stationary regions, Thoroughfares",
author = "Florian Verhein and Sanjay Chawla",
year = "2008",
month = "2",
doi = "10.1007/s10618-007-0079-5",
language = "English",
volume = "16",
pages = "5--38",
journal = "Data Mining and Knowledge Discovery",
issn = "1384-5810",
publisher = "Springer Netherlands",
number = "1",

}

TY - JOUR

T1 - Mining spatio-temporal patterns in object mobility databases

AU - Verhein, Florian

AU - Chawla, Sanjay

PY - 2008/2

Y1 - 2008/2

N2 - With the increasing use of wireless communication devices and the ability to track people and objects cheaply and easily, the amount of spatio-temporal data is growing substantially. Many of these applications cannot easily locate the exact position of objects, but they can determine the region in which each object is contained. Furthermore, the regions are fixed and may vary greatly in size. Examples include mobile/cell phone networks, RFID tag readers and satellite tracking. This demands techniques to mine such data. These techniques must also correct for the bias produced by different sized regions. We provide a comprehensive definition of Spatio-Temporal Association Rules (STARs) that describe how objects move between regions over time. We also present other patterns that are useful for mobility data; stationary regions and high traffic regions. The latter consists of sources, sinks and thoroughfares. These patterns describe important temporal characteristics of regions and we show that they can be considered as special STARs. We define spatial support to effectively deal with the problem of different sized regions. We provide an efficient algorithm-STAR-Miner-to find these patterns by exploiting several pruning properties.

AB - With the increasing use of wireless communication devices and the ability to track people and objects cheaply and easily, the amount of spatio-temporal data is growing substantially. Many of these applications cannot easily locate the exact position of objects, but they can determine the region in which each object is contained. Furthermore, the regions are fixed and may vary greatly in size. Examples include mobile/cell phone networks, RFID tag readers and satellite tracking. This demands techniques to mine such data. These techniques must also correct for the bias produced by different sized regions. We provide a comprehensive definition of Spatio-Temporal Association Rules (STARs) that describe how objects move between regions over time. We also present other patterns that are useful for mobility data; stationary regions and high traffic regions. The latter consists of sources, sinks and thoroughfares. These patterns describe important temporal characteristics of regions and we show that they can be considered as special STARs. We define spatial support to effectively deal with the problem of different sized regions. We provide an efficient algorithm-STAR-Miner-to find these patterns by exploiting several pruning properties.

KW - Sinks

KW - Sources

KW - Spatio-temporal association rules (STARs)

KW - Spatio-temporal data mining

KW - STAR-Miner

KW - Stationary regions

KW - Thoroughfares

UR - http://www.scopus.com/inward/record.url?scp=37649005978&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=37649005978&partnerID=8YFLogxK

U2 - 10.1007/s10618-007-0079-5

DO - 10.1007/s10618-007-0079-5

M3 - Article

AN - SCOPUS:37649005978

VL - 16

SP - 5

EP - 38

JO - Data Mining and Knowledge Discovery

JF - Data Mining and Knowledge Discovery

SN - 1384-5810

IS - 1

ER -