“One size fits all” database architectures do not work for DSS
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Developing quality complex database systems: practices, techniques and technologies
Developing quality complex database systems: practices, techniques and technologies
Benchmark Handbook: For Database and Transaction Processing Systems
Benchmark Handbook: For Database and Transaction Processing Systems
TPC-W: A Benchmark for E-Commerce
IEEE Internet Computing
Pro Scalable .NET 2.0 Application Design (Pro)
Pro Scalable .NET 2.0 Application Design (Pro)
A common database approach for OLTP and OLAP using an in-memory column database
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Event-based applications and enabling technologies
Proceedings of the Third ACM International Conference on Distributed Event-Based Systems
A mixed transaction processing and operational reporting benchmark
Information Systems Frontiers
The mixed workload CH-benCHmark
Proceedings of the Fourth International Workshop on Testing Database Systems
Interactive performance monitoring of a composite OLTP and OLAP workload
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Metrics for measuring the performance of the mixed workload CH-benCHmark
TPCTC'11 Proceedings of the Third TPC Technology conference on Topics in Performance Evaluation, Measurement and Characterization
Normalization in a mixed OLTP and OLAP workload scenario
TPCTC'11 Proceedings of the Third TPC Technology conference on Topics in Performance Evaluation, Measurement and Characterization
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Current database benchmarks are either focusing on online transaction processing (OLTP) or on online analytical pro- cessing (OLAP) systems. This traditional separation has to be reevaluated to reflect current trends in the design of database systems. We see a need for a realistic benchmark workload taking both aspects into account. Therefore, we defined a mixed workload and illustrate ways to apply our workload to evaluate the influence of database design on system performance.