Areas of Interest
Michael's research is situated in the area of efficient query processing on hot and cold data. His main research interests are memory footprint reduction and hardware cost savings of database management systems without sacrificing performance. For example, Michael demonstrated with the table partitioning advisor SAHARA a substantial reduction of the memory footprint of cloud databases. In addition, he showed how workload execution statistics are collected with high precision, low memory consumption, and low runtime overhead. Accurate workload execution statistics are essential for physical database design advisors to be effective. Currently, Michael is researching a framework for workload prediction, such that the current physical database design is adapted timely to workload changes. This is crucial since whenever workload changes are not addressed timely, the current physical database design may no longer be optimal, leading to a significant increase in memory consumption or hardware costs.
Michael studied Information and Computer Science at the University of Konstanz. In 2015, he received a Bachelor's Degree in Information Engineering with a specialization in databases and information systems. He received the 'VEUK award' in recognition of academic excellence for one of the best Bachelor's Degrees of 2015. He acquired a Master's Degree in Computer and Information Science in 2018. In his Master's Thesis "A Robustness Metric for Relational Query Execution Plans", Michael demonstrated that robust query execution plans are superior to optimal ones by defining three novel metrics for the robustness of relational query execution plans w.r.t. cardinality estimation errors and a novel plan selection strategy that takes both, estimated cost and estimated robustness into account when choosing an execution plan. He received the 'VEUK award' in recognition of academic excellence for one of the best Master's Degrees of 2018. In 2018, Michael was one of the five finalists of the ACM SIGMOD Programming Contest. He joined the Database and Information Systems group as a PhD student in 2018.
- Michael Brendle, Nick Weber, Mahammad Valiyev, Norman May, Robert Schulze, Alexander Böhm, Guido Moerkotte, and Michael Grossniklaus: SAHARA: Memory Footprint Reduction of Cloud Databases with Automated Table Partitioning. EDBT 2022.
- Michael Brendle, Nick Weber, Mahammad Valiyev, Norman May, Robert Schulze, Alexander Böhm, Guido Moerkotte, and Michael Grossniklaus: Precise, Compact, and Fast Data Access Counters for Automated Physical Database Design. BTW 2021.
- Florian Wolf, Michael Brendle, Norman May, Paul R. Willems, Kai-Uwe Sattler, and Michael Grossniklaus: Robustness Metrics for Relational Query Execution Plans. PVLDB 2018
- Florian Wolf, Michael Brendle, and Georgios Psaropoulos: Top-5 Finalist in the ACM SIGMOD Programming Contest. 2018
- Michael Brendle, Nick Weber, Mahammad Valiyev, Norman May, Robert Schulze, Alexander Böhm, Guido Moerkotte, and Michael Grossniklaus: Workload Aware Data Partitioning. 2022
- Florian Wolf, Michael Brendle, Norman May, Paul R. Willems, and Kai-Uwe Sattler: Robustness Metrics for Optimization of Query Execution Plans. 2019