Towards Mining Frequent Queries in Star Schemes

  • Tao-Yuan Jen
  • Dominique Laurent
  • Nicolas Spyratos
  • Oumar Sy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3933)


The problem of mining all frequent queries in a database is intractable, even if we consider conjunctive queries only. In this paper, we study this problem under reasonable restrictions on the database, namely: (i) the database scheme is a star scheme; (ii) the data in the database satisfies a set of functional dependencies and a set of referential constraints.

We note that star schemes are considered to be the most appropriate schemes for data warehouses, while functional dependencies and referential constraints are the most common constraints that one encounters in real databases. Our approach is based on the weak instance semantics of databases and considers the class of selection-projection queries over weak instances. In such a context, we show that frequent queries can be mined using level-wise algorithms such as Apriori.


Association Rule Functional Dependency Selection Condition Relation Scheme Dimension Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Advances in Knowledge Discovery and Data Mining, pp. 309–328. AAAI-MIT Press (1996)Google Scholar
  2. 2.
    Armstrong, W.W.: Dependency structures of data base relationships. In: IFIP Congress, pp. 580–583. North-Holland, Amsterdam (1974)Google Scholar
  3. 3.
    Casali, A., Cichetti, R., Lakhal, L.: Extracting semantics from data cubes using cube transversals and closures. In: ACM KDD, pp. 69–78 (2003)Google Scholar
  4. 4.
    Dehaspe, L., De Raedt, L.: Mining association rules in multiple relations. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 125–132. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  5. 5.
    Diop, C.T.: Etude et mise en oeuvre des aspects itratifs de l’extraction de rgles d’association dans une base de donnes. PhD thesis, Universit de Tours, France (2003)Google Scholar
  6. 6.
    Diop, C.T., Giacometti, A., Laurent, D., Spyratos, N.: Composition of mining contexts for efficient extraction of association rules. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 106–123. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Faye, A., Giacometti, A., Laurent, D., Spyratos, N.: Mining rules in databases with multiple tables: Problems and perspectives. In: 3rd International Conference on Computing Anticipatory Systems (CASYS) (1999)Google Scholar
  8. 8.
    Giacometti, A., Laurent, D., Diop, C.T., Spyratos, N.: Mining from views: An incremental approach. International Journal Information Theories & Applications 9 (See also RR LI/E3i, Univ. de Tours) (2002)Google Scholar
  9. 9.
    Goethals, B.: Mining queries (unpublished paper). In: Workshop on inductive databases and constraint based mining (2004), Available at
  10. 10.
    Goethals, B., Van den Bussche, J.: Relational association rules: getting warmer. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds.) Pattern Detection and Discovery. LNCS (LNAI), vol. 2447, pp. 125–139. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Han, J., Fu, Y., Wang, W., Koperski, K., Zaiane, O.: Dmql: A data mining query language for relational databases. In: SIGMOD-DMKD 1996, pp. 27–34 (1996)Google Scholar
  12. 12.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8, 53–87 (2004)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Laurent, D., Luong, V.P., Spyratos, N.: Querying weak instances under extension chase semantics. Intl. Journal of Comp. Mathematics 80(5), 591–613 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Levene, M., Loizou, G.: Why is the snowflake schema a good data warehouse design? Information Systems 28(3), 225–240 (2003)CrossRefGoogle Scholar
  15. 15.
    Meo, R., Psaila, G., Ceri, S.: An extension to sql for mining association rules. Data Mining and Knowledge Discovery 9, 275–300 (1997)Google Scholar
  16. 16.
    Turmeaux, T., Salleb, A., Vrain, C., Cassard, D.: Learning characteristic rules relying on quantified paths. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 471–482. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  17. 17.
    Ullman, J.D.: Principles of Databases and Knowledge-Base Systems, vol. 1. Computer Science Press (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tao-Yuan Jen
    • 1
  • Dominique Laurent
    • 1
  • Nicolas Spyratos
    • 2
  • Oumar Sy
    • 1
    • 3
  1. 1.LICP, Université de Cergy-PontoiseCergy-PontoiseFrance
  2. 2.LRIOrsayFrance
  3. 3.Université Gaston BergerSaint-LouisSenegal

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