NETE Compact Data Structures to Represent and Query Data Warehouses into Main Memory

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.