Optimizing OLAP Queries by Mapping Data Cube to Two Dimensional Space

Data warehouse and OLAP are essential elements of decision support systems (DSS) and have been studied in database issues extensively. The requirements of decision support systems are different from on-line transactional processing systems. Query optimization and efficient data cube computation have primary roles in improving functionality of DSS. This paper presents a new method for query processing in data warehouses and computing data cubes using bottom-up cube computation techniques. Results of implementation show that the proposed algorithm outperforms two best known algorithms (based on time criterion), and is much faster than them in answering to monotonic query with large volume of data. Furthermore, 2-dimensional view of ex-cube and transforming the data cube to a hyper graph structure, reduce the required space of the algorithm when we aggregate subsets of cube's dimension.