A relational perspective on spatial data mining

A relational perspective on spatial data mining

Remote sensing and mobile devices nowadays collect a huge amount of spatial data, which have to be analysed in order to discover interesting information about economic, social and scientific problems. However, the presence of a spatial dimension adds some problems to data mining tasks. The geometrical representation and relative positioning of spatial objects implicitly define spatial relations...

Mining Spatial Data: Opportunities and Challenges of a Relational Approach

Remote sensing and mobile devices nowadays collect a huge amount of spatial data which have to be analysed in order to discover interesting knowledge about economical, social and scientific problems. However, the presence of a spatial dimension adds some problems to data mining tasks. The geometrical representation and relative positioning of spatial objects implicitly define spatial relationsh...

Role Mining on Relational Data

Fine-grained access control for relational data defines user authorizations at the tuple level. Role Based Access Control (RBAC) has been proposed for relational data where roles are allowed access to tuples based on the authorized view defined by a selection predicate. During the last few years, extensive research has been conducted in the area of role en­ gineering. The existing approaches fo...

A Perspective on Data Mining

Multi-Relational Data Mining

An important aspect of data mining algorithms and systems is that they should scale well to large databases. A consequence of this is that most data mining tools are based on machine learning algorithms that work on data in attribute-value format. Experience has proven that such ’single-table’ mining algorithms indeed scale well. The downside of this format is, however, that more complex patter...