Abstract

In this paper we propose the use of compact data structures to represent and process Data Warehouses (DWs) into main memory. Compact data structures are data structures that allow compacting the data without losing the capacity of querying the data in their compact form. A DW is a data repository to store historical data for decision support, and consists of dimensions and facts. The dimensions are abstract concepts that groups data with a similar meaning, usually, they are modelled as hierarchies of levels, which contain elements. The facts are quantitative data associated to dimensions. A data cube is a way to retrieve facts at different levels of granularity, which is achieved by navigation on dimensions hierarchies. Since a DW can store terabytes of data, the efficient processing of data cubes is key in OLAP (On-line Analytical Processing). We show that by using a compact representation of DWs we can improve the use of space in main memory, and achieve better performance for query processing. In this paper we extend a previous work to process aggregate queries with aggregate functions MAX, MIN, COUNT and AVG.