Abstract: Among all the data we collect these days, spatial data represents a very significant portion. Nearly 80% of the data we collect has an element of space, which is not only extremely large but it also need a very special focus on the mining and analytics techniques for analysing it. These spatial (or geographical) datasets store a large amount of space-related data, such as maps, remote sensing, medical imaging data, environment images, etc. They carry topological and/or distance information, usually organised by sophisticated, multidimensional spatial indexing structures that are accessed by spatial data access methods and often require reasoning, geometric computation, and spatial knowledge representation techniques.
Spatial data analytics refers to the extraction of knowledge, spatial relationships, or other interesting patterns not explicitly stored in spatial databases. Such mining demands an integration of data mining with spatial database technologies. It can be used for understanding spatial data, discovering spatial relationships and relationships between spatial and non-spatial data, constructing spatial knowledge bases, reorganising spatial databases, and optimising spatial queries. It is expected to have wide applications in geographic information systems, remote sensing, image database exploration, medical imaging, navigation, traffic control, environmental studies, and many other areas where spatial data are used. A crucial challenge to spatial data mining is the exploration of efficient spatial data mining techniques due to the huge amount of spatial data and the complexity of spatial data types and spatial access methods.
On the other hand this needs huge computing power which can be provided by the constantly rising technology such as cloud computing. Cloud computing has experienced massive growth in the scale for both computing and storage and it constitutes an ideal solution to address the challenges of big data such as spatial data analysis. The goal of this lecture series is to look at the cloud solutions (HDFS, Hadoop and MapReduce) and other cloud tools and see how they can be applied to handle big data analyses.