Data Mining and Knowledge Discovery

, Volume 16, Issue 1, pp 5–38 | Cite as

Mining spatio-temporal patterns in object mobility databases



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.


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


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.School of Information TechnologiesUniversity of SydneySydneyAustralia

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