High performance data mining using data cubes on parallel computers

Online analytical processing techniques are used for data analysis and decision support systems. The multidimensionality of the underlying data is well represented by multidimensional databases. For data mining in knowledge discovery, OLAP calculations can be effectively used. For these, high performance parallel systems are required to provide interactive analysis. Precomputed aggregate calculations in a data cube can provide efficient query processing for OLAP applications. We present parallel data cube construction on distributed-memory parallel computers from a relational database. The data cube is used for data mining of associations using attribute focusing. Results are presented for these on the IBM-SP2, which show that our algorithms and techniques are scalable to a large number of processors, providing a high performance platform for such applications