Interactive ROLAP on large datasets: A case study with UB-Trees

Abstract

Online Analytical Processing (OLAP) requires query response times within the range of a few seconds in order to allow for interactive drilling, slicing, or dicing through an OLAP cube. While small OLAP applications use multidimensional database systems, large OLAP applications like the SAP BW rely on relational (ROLAP) databases for efficient data storage and retrieval. ROLAP databases use specialized data models like star or snowflake schemata for data storage and create a large set of indexes or materialized views in order to answer queries efficiently. In our case study, we show the performance benefits of TransBase HyperCube, a commercial RDBMS, whose kernel fully integrates the UB-Tree, a multi-dimensional extension of the B-Tree. With this newly developed access structure, TransBase HyperCube enables interactive OLAP without the need of storing a large set of materialized views or creating a large set of indexes. We compare not only the query performance, but also consider index size and maintenanc e costs. For the case study we use a 42 million record ROLAP database of GfK, the largest German market research company.