Efficient spatio-temporal data mining with GenSpace graphs

✍ Scribed by Howard J. Hamilton; Liqiang Geng; Leah Findlater; Dee Jay Randall

• Book ID: 104020103
• Publisher: Elsevier Science
• Year: 2006
• Tongue: English
• Weight: 551 KB
• Volume: 4
• Category: Article
• ISSN: 1570-8683
• DOI: 10.1016/j.jal.2005.06.005

✦ Synopsis

We describe a method for spatio-temporal data mining based on GenSpace graphs. Using familiar calendar and geographical concepts, such as workdays, weeks, climatic regions, and countries, spatio-temporal data can be aggregated into summaries in many ways. We automatically search for a summary with a distribution that is anomalous, i.e., far from user expectations. We repeatedly ranking possible summaries according to current expectations, and then allow the user to adjust these expectations. We also choose a propagation path in the GenSpace subgraph that reduces the storage and time costs of the mining process.