The state of the art in distributed query processing
ACM Computing Surveys (CSUR)
Approximate Query Answering Using Data Warehouse Striping
Journal of Intelligent Information Systems - Special issue on data warehousing and knowledge discovery
Efficient OLAP query processing in distributed data warehouses
Information Systems - Special issue: Best papers from EDBT 2002
SAP Business Information Warehouse - From Data Warehousing to an E-business Platform
Proceedings of the 17th International Conference on Data Engineering
Experimental Evaluation of a New Distributed Partitioning Technique for Data Warehouses
IDEAS '01 Proceedings of the International Database Engineering & Applications Symposium
Modeling and Maintaining Multi-View Data Warehouses
ER '99 Proceedings of the 18th International Conference on Conceptual Modeling
Parallel SQL execution in Oracle 10g
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Balancing redundancy and query costs in distributed data warehouses
APCCM '05 Proceedings of the 2nd Asia-Pacific conference on Conceptual modelling - Volume 43
Parallelizing query optimization
Proceedings of the VLDB Endowment
Optimizing Star Join Queries for Data Warehousing in Microsoft SQL Server
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
The Data Warehouse Lifecycle Toolkit
The Data Warehouse Lifecycle Toolkit
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On-line analytical processing against data warehouse databases is a common form of getting decision making information for almost every business field. Decision support information oftenly concerns periodic values based on regular attributes, such as sales amounts, percentages, most transactioned items, etc. This means that many similar OLAP instructions are periodically repeated, and simultaneously, between the several decision makers. Our Query Cache Tool takes advantage of previously executed queries, storing their results and the current state of the data which was accessed. Future queries only need to execute against the new data, inserted since the queries were last executed, and join these results with the previous ones. This makes query execution much faster, because we only need to process the most recent data. Our tool also minimizes the execution time and resource consumption for similar queries simultaneously executed by different users, putting the most recent ones on hold until the first finish and returns the results for all of them. The stored query results are held until they are considered outdated, then automatically erased. We present an experimental evaluation of our tool using a data warehouse based on a real-world business dataset and use a set of typical decision support queries to discuss the results, showing a very high gain in query execution time.