Summary information from data in large databases is used to answer queries in On-Line Analytical Processing (OLAP) systems and to build decision support systems over them. The Data Cube is used to calculate and store summary information on a variety of dimensions, which is computed only partially if the number of dimensions is large. Queries posed on such systems are quite complex and require different views of data. These may either be answered from a materialized cube in the data cube or calculated on the fly. Further, data mining for associations can be performed on the data cube. Analytical models need to capture the multi-dimensionality of the underlying data, a task for which multidimensional databases are well suited. Multidimensional databases store data in multidimensional structure on which analytical operations are performed. A challenge for these systems is how to handle large data sets in a large number of dimensions. This paper presents q parallel OLAP infrastructure for multidimensional databases integrated with association rule mining. Scheduling optimizations for parallel computation of complete data cubes are presented. We propose left and right schedules for partial data cubes for m-way mining of association rules. Our implementation on the IBM SP-2, a shared-nothing parallel machine, can handle large data sets and a large number of dimensions by using disk I/O in our algorithms.