Knowledge Discovery by Mining Association Rules and Temporal-Spatial Information from Large-Scale Geospatial Image Databases

Discovering relevant knowledge from large-scale geospatial image databases is challenging because of the complexity of describing visual semantics, the computational cost of processing petabytes of data, and the difficulty in summarizing and presenting knowledge. In this paper, we revisit a selective set of core data mining algorithms, namely association rules mining, spatial mining, and temporal mining. We then customize these algorithms using visual content and potential objects extracted from geospatial image databases with other relevant information, such as text-based annotations. Queries utilizing the mining results are also discussed in this paper. These mining and query processing algorithms play an important role in GeoIRIS- Geospatial Information Retrieval and Indexing System.