An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Star-cubing: computing iceberg cubes by top-down and bottom-up integration
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
Query recommendation in digital libraries using OLAP
Proceedings of the 2nd International Workshop on Keyword Search on Structured Data
Efficient algorithms based on relational queries to mine frequent graphs
PIKM '10 Proceedings of the 3rd workshop on Ph.D. students in information and knowledge management
Interactive exploration and visualization of OLAP cubes
Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP
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OLAP is a set of database exploratory techniques to efficiently retrieve multiple sets of aggregations from a large dataset. Generally, these techniques have either involved the use of an external OLAP server or required the dataset to be exported to a specialized OLAP tool for more efficient processing. In this work, we show that OLAP techniques can be performed within a modern DBMS without external servers or the exporting of datasets, using standard SQL queries and UDFs. The main challenge of such approach is that SQL and UDFs are not as flexible as the C language to explore the OLAP lattice and therefore it is more difficult to develop optimizations. We compare three different ways of performing OLAP exploration: plain SQL queries, a UDF implementing a lattice structure, and a UDF programming the star cube structure. We demonstrate how such methods can be used to efficiently explore typical OLAP datasets.