Prof. Dr. h.c. Hasso Plattner

Markus Dreseler

Research Assistant, PhD Candidate

Phone: +49 (331) 5509 - 1310
Fax: +49 (331) 97992 - 579
E-Mail: markus.dreseler(at)hpi.de
Room: Hasso-Plattner-Villa, V 2.05 

Research Topics

  • In-Memory Databases
  • Upcoming Hardware Technologies (including our Hardware Updates)
  • Low-Latency Networks
  • Non-Volatile Memories


  • Dreseler, M., Kossmann, J., Boissier, M., Klauck, S., Uflacker, M., Plattner, H.: Hyrise Re-engineered: An Extensible Database System for Research in Relational In-Memory Data Management. 22nd International Conference on Extending Database Technology (EDBT). pp. 313-324 (2019).
  • Schmidt, C., Dreseler, M., Akin, B., Roy, A.: A Case for Hardware-Supported Sub-Cache Line Accesses. Data Management on New Hardware (DaMoN), in conjunction with SIGMOD (2018).
  • Dreseler, M., Kossmann, J., Frohnhofen, J., Uflacker, M., Plattner, H.: Fused Table Scans: Combining AVX-512 and JIT to Double the Performance of Multi-Predicate Scans. Joint Workshop of HardBD (International Workshop on Big Data Management on Emerging Hardware) and Active (Workshop on Data Management on Virtualized Active Systems), in conjunction with ICDE (2018).
  • Kossmann, J., Dreseler, M., Gasda, T., Uflacker, M., Plattner, H.: Visual Evaluation of SQL Plan Cache Algorithms. Australasian Database Conference (ADC) (2018).
  • Dreseler, M., Gasda, T., Kossmann, J., Uflacker, M., Plattner, H.: Adaptive Access Path Selection for Hardware-Accelerated DRAM Loads. Australasian Database Conference (ADC) (2018).
  • Dreseler, M., Kissinger, T., Djürken, T., Eric, L., Uflacker, M., Habich, D., Plattner, H., Lehner, W.: Hardware-Accelerated Memory Operations on Large-Scale NUMA Systems. Eighth International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures (ADMS), in conjunction with VLDB (2017).
  • Schwalb, D., Faust, M., Dreseler, M., Flemming, P., Plattner, H.: Hyrise-NV: Leveraging Non-Volatile Memory for Instant Restarts of In-Memory Database Systems. International Conference on Data Engineering (ICDE) (2016).
  • Schwalb, D., Bk, G.K., Dreseler, M., S, A., Faust, M., Hohl, A., Berning, T., Makkar, G., Plattner, H., Deshmukh, P.: Hyrise-NV: Instant Recovery for In-Memory Databases using Non-Volatile Memory. International Conference on Database Systems for Advanced Applications (DASFAA) (2016).
  • Schwalb, D., Berning, T., Faust, M., Dreseler, M., Plattner, H.: nvm_malloc: Memory Allocation for NVRAM. International Workshop on Accelerating Data Management Systems Using Modern Processor and Storage Architectures (ADMS), in Conjuction with VLDB (2015).
  • Schwalb, D., BK, G., Faust, M., S, A., Dreseler, M., Hohl, A., Berning, T., Makkar, G., Deshmukh, P.: Using non-volatile RAM for inherent persistence and fast recovery of an in-memory database. ACM Symposium on Cloud Computing (SoCC) (2015).
  • Schwalb, D., Dreseler, M., Uflacker, M., Plattner, H.: NVC-Hashmap: A Persistent and Concurrent Hashmap For Non-Volatile Memories. In-Memory Data Management Workshop (IMDM), in conjunction with VLDB (2015).
  • Schwalb, D., Dreseler, M., Faust, M., Wust, J., Plattner, H.: Split Dictionaries for In-Memory Column Stores in Mixed Workload Environments. Australasian Database Conference (ADC) (2014).
  • Alsubaiee, S., Behm, A., Borkar, V., Heilbron, Z., Kim, Y.-S., Carey, M.J., Dreseler, M., Li, C.: Storage Management in AsterixDB. Proceedings of the VLDB Endowment (2014).
  • Gumienny, R., Gericke, L., Dreseler, M., Meyer, S., Meinel, C.: User-centered Development of Social Collaboration Software. Collaborative Communities for Social Сomputing (CollaborateCom) (2011).

Master's Thesis

  • Leveraging NVRAM for the In-Memory Database HYRISE

Supervised Master's Theses


  • Multicore Optimizations for Hyrise
  • Improving the JIT Pipeline of Hyrise


  • Optimizing Memory Transfer on Large Scale-Up Machines
  • Optimizing OLTP Performance in In-Memory Databases on NUMA System
  • Building an SQL Interface and Leveraging Query Plan Caching for a Relational Database
  • Just-in-Time Compilation for Efficient Query Plan Execution of OLAP Workloads in Column Stores
  • Adaptive Query Optimization for In-Memory Databases
  • Logging and Recovery on In-Memory Databases