Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
A first course in database systems
A first course in database systems
A survey of logical models for OLAP databases
ACM SIGMOD Record
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Finding Your Way through Multidimensional Data Models
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
An efficient exception mining algorithm in multi-dimensional data cube
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
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Rollup, a key operation in OLAP queries, is to aggregate multidimensional data on dimensional hierarchies. While the operation can be efficiently implemented on regular hierarchies in data warehouse, its application to recursive hierarchies proved to be problematic. Due to the stratification restriction, aggregate is not permitted to be wrapped within SQL recursion. Representing rollup operations on recursive hierarchies as SQL recursive queries will cause considerable overhead and is thus inefficient. This paper proposes an iteration-based evaluation strategy that aims to solve this inefficiency problem in OLAP queries. In our solution, aggregation on recursive hierarchies is modeled as a binary operator tree that is stored in its postfix notation and executed by a push down stack. We also demonstrate how to seamlessly embed this novel strategy into data warehouse that is based on ORDBMS. Experiment results show that our proposed solution is quite efficient compared with the SQL recursive evaluation strategy.