Mining spatio-temporal patterns in object mobility databases
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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.
KeywordsSpatio-temporal data mining Spatio-temporal association rules (STARs) Sources Sinks Thoroughfares Stationary regions STAR-Miner
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- Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of 20th international conference on very large data bases VLDB. Morgan Kaufmann, pp 487–499Google Scholar
- Ale JM, Rossi GH (2000) An approach to discovering temporal association rules. In: SAC ’00: proceedings of the 2000 ACM symposium on applied computing. ACM Press, pp 294–300Google Scholar
- Huang Y, Xiong H, Shekhar S, Pei J (2003) Mining confident co-location rules without a support threshold. In: Proceedings of the 18th ACM symposium on applied computing ACM SACGoogle Scholar
- Ishikawa Y, Tsukamoto Y, Kitagawa H (2004) Extracting mobility statistics from indexed spatio-temporal datasets. In: STDBM, pp 9–16Google Scholar
- Mamoulis N, Cao H, Kollios G, Hadjieleftheriou M, Tao Y, Cheung DW (2004) Mining, indexing, and querying historical spatiotemporal data. In: KDD ’04: proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, New York, NY, USA, pp 236–245Google Scholar
- Mennis J, Liu J (2003) Mining association rules in spatio-temporal data. In: Proceedings of the 7th international conference on geocomputationGoogle Scholar
- Shekhar S, Huang Y (2001) Discovering spatial co-location patterns:a summary of results. In: Proceedings of the 7th international symposium on spatial and temporal databases SSTD01Google Scholar
- Tao Y, Kollios G, Considine J, Li F, Papadias D (2004) Spatio-temporal aggregation using sketches. In: 20th international conference on data engineering. IEEE, pp 214–225Google Scholar
- Tsoukatos I, Gunopulos D (2001) Efficient mining of spatiotemporal patterns. In: SSTD ’01: proceedings of the 7th international symposium on advances in spatial and temporal databases. Springer-Verlag, London, UK, pp 425–442Google Scholar
- Verhein F, Chawla S (2006) Mining spatio-temporal association rules, sources, sinks, stationary regions and thoroughfares in object mobility databases. In: DASFAA: proceedings of database systems for advanced applications, 11th international conference, Singapore, April 12–15, 2006. Lecture Notes in Computer Science 3882. Springer, pp 187–201Google Scholar
- Wang J, Hsu W, Lee ML, Wang JTL (2004) Flowminer: finding flow patterns in spatio-temporal databases. In: ICTAI, pp 14–21Google Scholar