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This framework is different from transactional data as the datasets contain huge volumes of historicized and aggregated data defined over a set of dimensions that can be arranged through multiple levels of granularities. Many tools have been proposed to query the data and navigate through the levels of granularity. However, automatic tools are still missing to mine this type of data in order to discover regular specific patterns. In this article, we present a method for mining sequential patterns from multidimensional databases, at the same time taking advantage of the different dimensions and levels of granularity, which is original compared to existing work. The necessary definitions and algorithms are extended from regular sequential patterns to this particular case. Experiments are reported, showing the significance of this approach.<\/jats:p>","DOI":"10.1145\/1644873.1644877","type":"journal-article","created":{"date-parts":[[2010,1,12]],"date-time":"2010-01-12T20:23:07Z","timestamp":1263327787000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":39,"title":["Mining multidimensional and multilevel sequential patterns"],"prefix":"10.1145","volume":"4","author":[{"given":"Marc","family":"Plantevit","sequence":"first","affiliation":[{"name":"Universit\u00e9 Lyon 1, France"}]},{"given":"Anne","family":"Laurent","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Montpellier 2, France"}]},{"given":"Dominique","family":"Laurent","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Cergy Pontoise, France"}]},{"given":"Maguelonne","family":"Teisseire","sequence":"additional","affiliation":[{"name":"CEMAGREF Montpellier, France"}]},{"given":"Yeow WEI","family":"Choong","sequence":"additional","affiliation":[{"name":"HELP University College, Malaysia"}]}],"member":"320","published-online":{"date-parts":[[2010,1,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the International Conference on Data Engineering (ICDE). 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MAFIA: a maximal frequent itemset algorithm for transactional databases . In Proceedings of the International Conference on Data Engineering (ICDE). IEEE Computer Society, 443--452 . Burdick, D., Calimlim, M., and Gehrke, J. 2001. MAFIA: a maximal frequent itemset algorithm for transactional databases. In Proceedings of the International Conference on Data Engineering (ICDE). IEEE Computer Society, 443--452."},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the European Conference on the Principles and Practice of Knowledge Discovery in Databases (PKDD). Lecture Notes in Computer Science","volume":"2431","author":"Calders T.","unstructured":"Calders , T. and Goethals , B . 2002. Mining all non-derivable frequent itemsets . In Proceedings of the European Conference on the Principles and Practice of Knowledge Discovery in Databases (PKDD). Lecture Notes in Computer Science , vol. 2431 . Springer Verlag, 74--85. Calders, T. and Goethals, B. 2002. 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