Discovering Multidimensional Structure in Relational Data

On-Line Analytical Processing (OLAP) systems based on multidimensional databases are essential elements of decision support. However, most existing data is stored in “ordinary” relational OLTP databases, i.e., data has to be (re-) modeled as multidimensional cubes before the advantages of OLAP tools are available.

[1]  Rosine Cicchetti,et al.  FUN: An Efficient Algorithm for Mining Functional and Embedded Dependencies , 2001, ICDT.

[2]  Torben Bach Pedersen,et al.  Specifying OLAP Cubes on XML Data , 2004, Journal of Intelligent Information Systems.

[3]  Heikki Mannila,et al.  Discovering functional and inclusion dependencies in relational databases , 1992, Int. J. Intell. Syst..

[4]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[5]  Boris Vrdoljak,et al.  Data warehouse design from XML sources , 2001, DOLAP '01.

[6]  Jean-Marc Petit,et al.  Efficient Algorithms for Mining Inclusion Dependencies , 2002, EDBT.

[7]  Peter Thanisch,et al.  Constructing OLAP cubes based on queries , 2001, DOLAP '01.

[8]  Patrick A. V. Hall,et al.  Approximate String Matching , 1994, Encyclopedia of Algorithms.

[9]  Karen C. Davis,et al.  Automating data warehouse conceptual schema design and evaluation , 2002, DMDW.

[10]  Hannu Toivonen,et al.  Efficient discovery of functional and approximate dependencies using partitions , 1998, Proceedings 14th International Conference on Data Engineering.

[11]  Heikki Mannila,et al.  Algorithms for Inferring Functional Dependencies from Relations , 1994, Data Knowl. Eng..