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Complex, spatial and spatio-temporal data arise in a plethora of modern database and data mining appli- cations and complex information systems. Complex, spatial and spatio-temporal data require more and more for effective and efficient models, algorithms and techniques for representing, managing, query- ing, indexing and discovering useful knowledge beyond such kind of data. A successful solution to issues above consists in applying well-consolidated methodologies coming from the Data Warehousing and OLAP research area. This allows us to take advantages from several nice amenities supported by Data Warehousing and OLAP, such as multidimensional and multi-resolution representation and anal- ysis, multidimensional aggregations, hierarchy-based data representation and mining, complex query answering tools, and so forth. Application fields where Data Warehousing and OLAP over complex, spatial and spatio-temporal data have already demonstrated their success are many-fold. |
