Join processing in database systems with large main memories
ACM Transactions on Database Systems (TODS)
Join processing in relational databases
ACM Computing Surveys (CSUR)
Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
OLAP, relational, and multidimensional database systems
ACM SIGMOD Record
An alternative storage organization for ROLAP aggregate views based on cubetrees
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Implementation techniques for main memory database systems
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Modelling Large Scale OLAP Scenarios
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
A Logical Approach to Multidimensional Databases
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Data Warehousing and OLAP for Decision Support
DOOD '97 Proceedings of the 5th International Conference on Deductive and Object-Oriented Databases
Globally Order Preserving Multidimensional Linear Hashing
Proceedings of the Fourth International Conference on Data Engineering
Storage and access in relational data bases
IBM Systems Journal
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Star schema is often used in dimensional approaches applied to OLAP applications. The fact table in the star schema typically contains a huge amount of data. When some of the dimension tables are also very large, it may take too much time and storage to join the fact table with these dimension tables. The performance of join algorithm becomes critical under such a condition. The fluent join is a join algorithm that operates on relations organized as multidimensional linear hash files. Like a merge join on relations which are already sorted on the joining key, its execution reads each page in the operand relations no more than once and does not create intermediate result files. Unlike sorting, the multi- dimensional linear hash can cluster records in several keys symmetrically. In this paper, the concept of the fluent join is applied to an OLAP system to cluster records in each table on the joining keys. As a result, the algorithm yields symmetric performances on joins with different dimension tables.