Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Rethinking Database System Architecture: Towards a Self-Tuning RISC-Style Database System
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
To tune or not to tune?: a lightweight physical design alerter
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
QUIET: continuous query-driven index tuning
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
DB2 design advisor: integrated automatic physical database design
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Autonomous Management of Soft Indexes
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
The NOX OLAP query model: from algebra to execution
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
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OLAP servers based on relational backends typically exploit materialized aggregate tables to improve response times of complex analytical queries. One of the key problems in this context is the view selection problem: choosing the optimal set of aggregation tables (called configuration) for a given workload. In this paper, we present a system that continuously monitors the workload and raises a quantified alert, when a better configuration is available. We address the tasks of query monitoring and view selection at the OLAP level instead of the SQL level, which simplifies the containment checks as well as rewriting and in this way helps to reduce the complexity of the backend system. At the demo we plan to show how our system works, i.e., how the system reacts upon arbitrary (interactive) workloads and how the user is alerted that a better configuration is available.