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Martin Boissier

Dr. Martin Boissier

Postdoc

Mail: martin.boissier@hpi.de

More

Postdoctoral Researcher
Profiles: DBLP - personal website

Research

Main Memory Footprint Reduction of In-Memory Database Systems

Database systems that keep their data primarily in main memory provide high query performance but also incur high costs. We have analyzed various real-world enterprise systems and their workload and data characteristics. We found that the main memory footprint can be efficiently reduced by (i) data encoding and (ii) tiering without degrading performance significantly.
To encode and compress a database instance, we use learned cost models to predict runtimes of various data encodings. We use linear programming models to determine optimal encoding configurations within a given memory budget. For the applicability in real-world scenarios, the models incorporate robustness measures that mitigate unexpected performance degradations. To efficiently tier data to secondary storage, we extended the hybrid data layout of the first version of Hyrise and evict infrequently accessed columns in a row-major format.

 

Reviewing

  • VLDB 2026 (Demo)
  • SIGMOD 2026, 2027
    • Distinguished Reviewer SIGMOD 2026
  • ICDE 2026
  • EDBT 2026 (Industrial)
  • TKDE 2023
  • DOLAP Workshop 2025
  • ADMS 2022, 2023
  • Datenbank-Spektrum 2025 - Issue von Cloud-Native Database Management Systems

Selected Publications

  • SmartRabbit: An Interactive Query Processor
    Das, P., Boissier, M., Kim, K., Mehrotra, S. and Rabl, T.
    Proceedings of the ACM on Management of Data, PACMMOD, SIGMOD 4(3) (2026), 235:1–235:25 
    [ doi ] [ Download ]
  • A Case for Ecological Efficiency in Database Server Lifecycles
    Bodner, T., Boissier, M., Rabl, T., Salazar-Díaz, R., Schmeller, F., Strassenburg, N., Tolovski, I., Weisgut, M. and Yue, W. 
    Conference on Innovative Data Systems Research, CIDR (2025)
    [ URL ] [ Download ]
  • Workload-Driven Data Placement for Tierless In-Memory Database Systems
    Hurdelhey, B., Weisgut, M. and Boissier, M.
    Datenbanksysteme für Business, Technologie und Web, BTW (2023), 47–70
    [ URL ] [ Download ]
  • Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications
    Richly, K., Schlosser, R. and Boissier, M.
    Proceedings of the VLDB Endowment 15(13) (2022), 4079–4092
    [ URL ] [ Download ]
  • Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems
    Boissier, M.
    Proceedings of the VLDB Endowment 15(4) (2022), 780–793
    [ URL ] [ Download ]
  • Joint Index, Sorting, and Compression Optimization for Memory-Efficient Spatio-Temporal Data Management
    Richly, K., Schlosser, R. and Boissier, M.
    IEEE International Conference on Data Engineering (ICDE) (2021), 1901–1906
    [ URL ] [ Download ]
  • Quantifying TPC-H Choke Points and Their Optimizations
    Dreseler, M., Boissier, M., Rabl, T. and Uflacker, M.
    Proceedings of the VLDB Endowment 13(8) (2020), 1206–1220
    [ URL ] [ Download ]
  • Efficient Scalable Multi-Attribute Index Selection Using Recursive Strategies
    Schlosser, R., Kossmann, J. and Boissier, M.
    IEEE International Conference on Data Engineering, ICDE (2019), 1238–1249
    [ URL ] [ Download ]
  • Hyrise Re-engineered: An Extensible Database System for Research in Relational In-Memory Data Management
    Dreseler, M., Kossmann, J., Boissier, M., Klauck, S., Uflacker, M. and Plattner, H.
    International Conference on Extending Database Technology, EDBT (2019), 313–324
    [ URL ] [ Download ]
  • Hybrid Data Layouts for Tiered HTAP Databases with Pareto-Optimal Data Placements
    Boissier, M., Schlosser, R. and Uflacker, M.
    IEEE International Conference on Data Engineering, ICDE (2018), 209–220
    [ URL ] [ Download ]

Teaching

Lectures and Seminars:

Supervised Master Theses:

  • “Optimizing String Joins for Real-World Workloads” (ongoing)
  • “Improving Cardinality Estimation with Data Dependencies” (ongoing)
  • “Using Coordinated Probabilistic Filters to Reduce Intermediate Query Results” (March 2026)
  • “Hardware-Conscious SIMD-Accelerated Sort-Merge Joins in Multi-Core In-Memory Database Systems” (June 2025)
  • “Efficient Partial Data Generation for Benchmarking of Columnar In-Memory Databases” (April 2025)
  • “Workload-Driven Smooth Index and Filter Selection for In-Memory Database Scan Acceleration” (November 2022)
  • “Cost-aware Filtering in Query Processing on Serverless Cloud Infrastructure” (October 2022)
  • “Automatic Tiering in Hyrise” (September 2022)
  • “Automatic Clustering in Hyrise” (October 2020)
  • “Learned Cost Models for Query Optimization” (March 2019)
  • “Cardinality Estimation and Access Avoidance in Horizontally Partitioned Databases” (November 2018)
  • “Data-Driven Ordering and Dynamic Pricing Competition on Online Marketplaces” (May 2018)
  • “Probabilistic Data Structures for In-Memory Databases” (May 2018)
  • “Maintainable and Self-Adapting Column Compression Schemes for HTAP Databases” (April 2018)
  • “Optimizing Database Scan Performance through Access Avoidance in Chunk-Based Databases using Multi-Dimensional Filters” (August 2017)
  • “Predicting movie success before release – Using individualized econometric models to predict box office performance” (January 2017)
  • “Workload-Aware Partitioning and Query Pruning for Mixed Workloads on In-Memory Databases” (January 2016)