Abstract
Due to the enormous amount of data stored in spatial multidimensional databases (also called spatial datacubes) and the complexity of multidimensional structures, extracting interesting information by exploiting spatial data cubes becomes more and more difficult. Users might overlook what part of the cube contains the relevant information and what the next query should be. This could affect their exploitation of spatial datacubes.
In order to help users to better exploit their spatial datacubes, we propose to use a collaborative filtering recommendation approach. The approach is based on computing the similarity between the user’s behaviors in term of their spatial MDX queries launched on the system.
This paper introduces a new similarity measure for comparing spatial MDX queries. The proposed measure could directly support the development of spatial personalization and recommendation approaches. The presented measure takes into account both the semantic similarity as well as the basic components of spatial similarity assessment models: the topology, the direction and the distance.
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Aissi, S., Gouider, M.S., Sboui, T., Bensaid, L. (2014). Enhancing Spatial Datacube Exploitation: A Spatio-semantic Similarity Perspective. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2014. Communications in Computer and Information Science, vol 465. Springer, Cham. https://doi.org/10.1007/978-3-319-11958-8_10
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DOI: https://doi.org/10.1007/978-3-319-11958-8_10
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