Abstract
A federated data warehouse is a logical integration of data warehouses applicable when physical integration is impossible due to privacy policy or legal restrictions. In order to enable the translation of queries in a federated approach, schemas of the federated and the local warehouses must be matched. In this paper we present a procedure that enables the matching process for schema structures specific to the multidimensional model of data warehouses: facts, measures, dimensions, aggregation levels and dimensional attributes. Similarities between warehouse-specific structures are computed by using linguistic and structural comparison, where calculated values are used to create necessary mappings. We present restriction rules and recommendations for aggregation level matching, which builds the most complex part of the process. A software implementation of the entire process is provided in order to perform its verification, as well as to determine the proper selection metric for mapping different multidimensional structures.
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References
Bergamaschi, S., Castano, S., Vincini, M.: Semantic Integration of Semistructured and Structured Data Sources. SIGMOD Record 28, 54–59 (1999)
Berger, S., Schrefl, M.: Analysing Multi-dimensional Data accross Autonomous Data Warehouses. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 120–133. Springer, Heidelberg (2006)
Banek, M., Tjoa, A.M., Stolba, N.: Integrating Different Grain Levels in a Medical Data Warehouse Federation. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 185–194. Springer, Heidelberg (2006)
Cabibbo, L., Torlone, R.: Integrating Heterogeneous Multidimensional Databases. In: Proc. Int. Conf. Scientific and Stat. Database Management 2005, pp. 205–214. IEEE Comp. Soc., Los Alamitos (2005)
Dhamankar, R., Lee, Y., Doan, A.-H., Halevy, A.Y., Domingos, P.: iMAP: Discovering Complex Mappings between Database Schemas. In: Proc. SIGMOD Conf. 2004, pp. 383–394. ACM Press, New York (2004)
Kim, W., Seo, J.: Classifying Semantic and Data Heterogeneity in Multidatabase Systems. IEEE Computer 24(12), 12–18 (1991)
Madhavan, J., Bernstein, P.A., Rahm, E.: Generic Schema Matching with Cupid. In: Proc. Int. Conf. on Very Large Data Bases 2001, pp. 49–58. Morgan Kaufmann, San Francisco (2001)
Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching. In: Proc. Int. Conf. on Data Engineering 2002, pp. 117–128. IEEE Computer Society, Los Alamitos (2002)
Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity Flooding: A Versatile Graph Matching Algorithm. Technical Report (2001), http://dbpubs.stanford.edu/pub/2001-25
Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB J. 10, 334–350 (2001)
Rodríguez, M.A., Egenhofer, M.J.: Determining Semantic Similarity among Entity Classes from Different Ontologies. IEEE Trans. Knowl. Data Eng. 15, 442–456 (2003)
Stolba, N., Banek, M., Tjoa, A.M.: The Security Issue of Federated Data Warehouses in the Area of Evidence-Based Medicine. In: Proc. Conf. Availability, Reliability and Security 2006, pp. 329–339. IEEE Computer Society, Los Alamitos (2006)
Sheth, A.P., Larson, J.A.: Federated Database Systems for Managing Distributed, Heterogeneous, and Autonomous Databases. ACM Computing Surveys 22, 183–236 (1990)
Princeton University Cognitive Science Laboratory: WordNet, a lexical database for English Language (last access March 25, 2007), http://wordnet.princeton.edu
Yang, D., Powers, D.M.W.: Measuring Semantic Similarity in the Taxonomy of WordNet. In: CRPIT 38, pp. 315–322, Australian Computer Society (2005)
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Banek, M., Vrdoljak, B., Tjoa, A.M., Skočir, Z. (2007). Automating the Schema Matching Process for Heterogeneous Data Warehouses. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2007. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74553-2_5
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DOI: https://doi.org/10.1007/978-3-540-74553-2_5
Publisher Name: Springer, Berlin, Heidelberg
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