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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References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Aoidh, E.M., McArdle, E., Petit, M., Ray, C., Bertolotto, M., Claramunt, C., Wilson, D.C.: Personalization in adaptive and interactive GIS. Annals of GIS 15(1), 23–33 (2009)
Agarwal, N., Rao, M., Mantha, S., Gokhale, J.A.: Annotation of Geospatial Data Based on Semantics forAgriculture:Case Study for India. In: 3rd International Conference onComputer Research and Development (ICCRD), pp. 139–142 (2011)
Beel, J., Langer, S., Genzmehr, M., Gipp, B.: Research Paper Recommender System Evaluation: A Quantitative Literature Survey. In: Proceedings of the Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys) at the ACM Recommender System Conference (RecSys) (October 2013)
Bellatore, A., McArdle, G., Kelly, C., Bertolotto, M.: RecoMap: An interactive and adaptive map-based recommender. In: SAC 2010: Symposium on Applied Computing. ACM (2010)
Biondi, P., Golfarelli, M., Rizzi, S.: Preference-based datacube analysis with MYOLAP. In: ICDE, Hannover, pp. 1328–1331 (2011)
Bruns, H.T., Egenhover, M.J.: Similarity of Spatial Scenes. In: Seventh International Symposium on Spatial Data Handling, Delft, The Netherlands, pp. 4A.31–4A.42 (1996)
Jerbi, H., Pujolle, G., Ravat, F., Teste, O.: Recommandation de requêtes dans les bases de données multidimensionnelles annotées. Ingénierie des Systèmes d’Information 16(1), 113–138 (2011)
Garrigós, I., Pardillo, J., Mazón, J.-N., Trujillo, J.: A Conceptual Modeling Approach for OLAP Personalization. In: Laender, A.H.F., Castano, S., Dayal, U., Casati, F., de Oliveira, J.P.M. (eds.) ER 2009. LNCS, vol. 5829, pp. 401–414. Springer, Heidelberg (2009)
Giacometti, A., Marcel, P., Negre, E.: Recommending Multidimensional Queries. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 453–466. Springer, Heidelberg (2009)
Giacometti, A., Marcel, P., Negre, E., Soulet, A.: Query Recommendations for OLAP Discovery-Driven Analysis. In: IJDWM, pp. 1–25 (2011)
Glorio, O., Mazón, J., Garrigós, I., Trujillo, J.: Using web-based personalization on spatial data warehouses. In: EDBT/ICDT Workshop, Lausanne (2010)
Glorio, O., Mazón, J., Garrigós, I., Trujillo, J.: A personalization process for spatial data warehouse development. Decision Support Systems 52, 884–898 (2012)
Golfarelli, M., Rizzi, S.: Expressing OLAP Preferences. In: SSDBM, Louisiana, pp. 83–91 (2009)
Holt, A.: Spatial Similarity and Gis: The Grouping of Spatial Kinds. In: The 11th Annual Colloquium of the Spatial Information Research Centre (1999)
Holt, A., Benwell, G.L.: Using Spatial Similarity for Exploratory Spatial Data Analysis: Some Directions. In: Proceedings of the 2rd International Conference on GeoComputation, University of Otago, New Zealand (1997)
Khemiri, R., Bentayeb, F.: Interactive Query Recommendation Assistant. In:Â DEXA Workshops (2012)
Li, B., Fonseca, F.T.: TDD - A Comprehensive Model for Qualitative Spatial SimilarityAssessment. Spatial Cognition and Computation 6, 31–62 (2006)
Rada, A., Mili, A.H., Bicknell, E., Blettener, C.: Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics 19(1), 17–30 (1989)
Rezgui, K., Mhiri, H., Ghédira, K.: Theoretical Formulas of Semantic Measure: A Survey. Journal OfEnerging Technologie. In: Web Intelligence 5(4) (November 2013)
RodrÃguez, A., Egenhofer, M.: Determining Semantic Similarity among Entity Classes from Different Ontologies. IEEE Transactions on Knowledge and Data Engineering 15, 442–456 (2003)
Spearman, C.: The proof and measurement of association between two things. The American Journal of Psychology 15(1), 72–101 (1904)
Srivastava, J., Cooley, R., Deshpande, M., Tan, P.-N.: Web usage mining: Discoveryand applications of usage patterns from web data. SIGKDD Explorations 1(2), 12–23 (2000)
Wilson, D.C., Bertolotto, M., Weakliam, J.: Personalizing map content to improve task completion efficiency. International Journal of Geographical Information Science 24(5), 741–760 (2010)
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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
Publisher Name: Springer, Cham
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