Hasso-Plattner-Institut
Prof. Dr. h.c. mult. Hasso Plattner
 

Autonomous Data Management

The trend of enterprises storing more and more data in cloud-based database systems requires providers to focus on self-optimization and cost-efficiency to stay competitive. We investigate these topics in two connected lines of research.

First, we work on in-memory data management concepts. With our in-memory research database Hyrise, we study how high-performance database systems can optimize themselves with the eventual goal of an autonomous operation without the need for human interaction. To advance in this direction, we are working on various topics, such as data tiering on modern storage technologies, data encoding, index selection, and data dependencies.

The second line of research explores a database system architecture that exploits modern, serverless cloud infrastructure such as Function-as-a-Service (FaaS) platforms to achieve cost-efficiency. With our Skyrise database system, we target the current sweet spot of interactive in-situ analytics on cold data in cloud storage. Skyrise incorporates a number of components from Hyrise and introduces new capabilities to compile and execute queries elastically with cloud functions.

Teaching

We offer lectures, seminars and projects on data management for enterprise applications. For HPI Master's students, we also provide a varity of thesis topics.

Job Offers

We always welcome applications of prospective Ph.D. students, who are interested in working with us. In this context, we are able to offer Ph.D. scholarships directly by the research group or for one of the two HPI Research Schools, Service-Oriented Systems Engineering or Data Science and Engineering.

Additionally, we are looking for HPI students who are interested in a part-time job as a student research assistant.

More Information can be found on our Job Offer page.

Contact: Dr. Michael Perscheid

Publications

Our research has been published in leading database systems conferences (VLDB, EDBT, ICDE, CIDR, and SIGMOD).

  • 1.
    Lindner, D., Schumann, F., Alder, N., Bleifuss, T., Bornemann, L., Naumann, F.: Mining Change Rules. Proceedings of the 25th International Conference on Extending Database Technology, EDBT 2022, Edinburgh, UK, March 29 - April 1, 2022. bll. 2:91–2:103 (2022).
     
  • 2.
    Justen, D.: Cost-efficiency and Performance Robustness in Serverless Data Exchange. Proceedings of the 2022 International Conference on Management of Data (SIGMOD ’22), Student Research Competition. bll. 2506–2508. Association for Computing Machinery, New York, NY, USA (2022).
     
  • 3.
    Boissier, M.: Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems. Proceedings of the VLDB Endowment. bll. 780–793 (2022).
     
  • 4.
    Bodner, T., Pietz, T., Bollmeier, L.J., Ritter, D.: Doppler: Understanding Serverless Query Execution. Proceedings of the SIGMOD Workshop on Big Data in Emergent Distributed Environments. (2022).
     
  • 5.
    Kossmann, J., Kastius, A., Schlosser, R.: SWIRL: Selection of Workload-aware Indexes using Reinforcement Learning. EDBT 2022. bll. 155–168 (2022).
     
  • 6.
    Kossmann, J., Lindner, D., Naumann, F., Papenbrock, T.: Workload-driven, Lazy Discovery of Data Dependencies for Query Optimization. Proceedings of the Conference on Innovative Data Systems Research (CIDR) (2022).
     
  • 7.
    Richly, K., Schlosser, R., Boissier, M.: Joint Index, Sorting, and Compression Optimization for Memory-Efficient Spatio-Temporal Data Management. 37th IEEE International Conference on Data Engineering, ICDE 2021, Chania, Greece, April 19-22, 2021. bll. 1901–1906 (2021).
     
  • 8.
    Halfpap, S., Schlosser, R.: Memory-Efficient Database Fragment Allocation for Robust Load Balancing when Nodes Fail. 37th IEEE International Conference on Data Engineering, ICDE 2021, Chania, Greece, April 19-22, 2021. bll. 1811–1816 (2021).
     
  • 9.
    Kossmann, J., Boissier, M., Dubrawski, A., Heseding, F., Mandel, C., Pigorsch, U., Schneider, M., Schniese, T., Sobhani, M., Tsayun, P., Wille, K., Perscheid, M., Uflacker, M., Plattner, H.: A Cockpit for the Development and Evaluation of Autonomous Database Systems. 37th IEEE International Conference on Data Engineering, ICDE. bll. 2685–2688 (2021).
     
  • 10.
    Kossmann, J., Papenbrock, T., Naumann, F.: Data dependencies for query optimization: a survey. VLDB Journal. (2021).
     
  • 11.
    Bodner, T.: Elastic Query Processing on Function as a Service Platforms. Proceedings of the VLDB PhD Workshop. (2020).
     
  • 12.
    Kossmann, J., Halfpap, S., Jankrift, M., Schlosser, R.: Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms. Proceedings of the VLDB Endowment. bll. 2382–2395 (2020).
     
  • 13.
    Dreseler, M., Boissier, M., Rabl, T., Uflacker, M.: Quantifying TPC-H Choke Points and Their Optimizations. Proceedings of the VLDB Endowment. bll. 1206–1220 (2020).
     
  • 14.
    Halfpap, S.: Efficient Scale-Out Using Query-Driven Workload Distribution and Fragment Allocation. Proceedings of the VLDB 2019 PhD Workshop co-located with the 45th International Conference on Very Large Databases (VLDB 2019) (2019).
     
  • 15.
    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). bll. 313–324 (2019).
     
  • 16.
    Kossmann, J.: Self-Driving: From General Purpose to Specialized DBMSs. Proceedings of the VLDB 2018 PhD Workshop co-located with the 44th International Conference on Very Large Databases (VLDB 2018), Rio de Janeiro, Brasil, Aug 27-31, 2018 (2018).
     
  • 17.
    Boissier, M.: Reducing the Footprint of Main Memory HTAP Systems: Removing, Compressing, Tiering, and Ignoring Data. PhD Workshop at VLDB. CEUR-WS.org (2018).