Understanding the new SQL: a complete guide
Understanding the new SQL: a complete guide
The data warehouse toolkit: practical techniques for building dimensional data warehouses
The data warehouse toolkit: practical techniques for building dimensional data warehouses
New TPC benchmarks for decision support and web commerce
ACM SIGMOD Record
Management Information Systems: Conceptual Foundations, Structure and Development
Management Information Systems: Conceptual Foundations, Structure and Development
Massive Stochastic Testing of SQL
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
MUDD: a multi-dimensional data generator
WOSP '04 Proceedings of the 4th international workshop on Software and performance
Analyzing plan diagrams of database query optimizers
VLDB '05 Proceedings of the 31st international conference on Very large data bases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Generating thousand benchmark queries in seconds
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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The synthesis of increased global competitiveness and the acceptance of commercially available multi purpose database management systems (DBMS) for decision support applications requires an ever more critical system evaluation and selection to be completed in a progressively short period of time. Designers of standard benchmarks, individual customer benchmarks and system stress tests alike are struggling to mastermind queries that are both representative to the real world and execute in a reasonable time. Additionally, the enriched functionality of every new DBMS release amplifies the complexity of today's decision support systems calling for a novel approach in query generation for benchmarks. This paper proposes a framework of so called query evolution rules that can be applied to typical decision support queries, written in SQL92. Deployed in combination with QGEN2, the query generator developed by the TPC for TPC-DS ?[13], these rules quickly turn a small set of queries into a large set of semantically similar queries for ad-hoc benchmarking purposes or they can be used to generate thousands of queries quickly to stress test optimizers or query execution engines without much user intervention.