2022

1.
Kossmann, J., Kastius, A., Schlosser, R.: SWIRL: Selection of Workload-aware Indexes using Reinforcement Learning. EDBT 2022. pp. 155–168 (2022).
[ BibTeX ]
 
@inproceedings{kossmann2022swirl, author = {Kossmann, Jan and Kastius, Alexander and Schlosser, Rainer}, booktitle = {EDBT 2022}, keywords = {epic}, pages = {155-168}, title = {SWIRL: Selection of Workload-aware Indexes using Reinforcement Learning}, year = 2022 }

2.
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).
[ BibTeX ] [ URL ]
 
@inproceedings{kossmann2022workloaddrivendatadepdiscovery, author = {Kossmann, Jan and Lindner, Daniel and Naumann, Felix and Papenbrock, Thorsten}, booktitle = {Proceedings of the Conference on Innovative Data Systems Research (CIDR)}, keywords = {myown optimization data query dependencies hyrise}, title = {Workload-driven, Lazy Discovery of Data Dependencies for Query Optimization}, year = 2022 }

2021

1.
Kossmann, J., Papenbrock, T., Naumann, F.: Data dependencies for query optimization: a survey. VLDB Journal. (2021).
[ BibTeX ]
 
@article{kossmann2021dependenciesepic, author = {Kossmann, Jan and Papenbrock, Thorsten and Naumann, Felix}, journal = {VLDB Journal}, keywords = {optimization data query dependencies hyrise}, title = {Data dependencies for query optimization: a survey}, year = 2021 }

2.
Lindner, D., Loeser, A., Kossmann, J.: Learned What-If Cost Models for Autonomous Clustering. New Trends in Database and Information Systems - ADBIS 2021 Short Papers, Doctoral Consortium and Workshops, Tartu, Estonia. pp. 3–13 (2021).
[ BibTeX ] [ DOI ] [ Download ]
 
@inproceedings{DBLP:conf/adbis/LindnerLK21, author = {Lindner, Daniel and Loeser, Alexander and Kossmann, Jan}, booktitle = {New Trends in Database and Information Systems - {ADBIS} 2021 Short Papers, Doctoral Consortium and Workshops, Tartu, Estonia}, keywords = {in-memory_database hyrise}, pages = {3--13}, title = {Learned What-If Cost Models for Autonomous Clustering}, year = 2021 }

3.
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. pp. 2685–2688 (2021).
[ Abstract ] [ BibTeX ] [ DOI ] [ Download ]
 
Databases are highly optimized complex systems with a multitude of configuration options. Especially in cloud scenarios with thousands of database deployments, determining optimized database configurations in an automated fashion is of increasing importance for database providers. At the same time, due to increased system complexity, it becomes more challenging to identify well-performing configurations. Therefore, research interest in autonomous or self-driving database systems has increased enormously in recent years. Such systems promise both performance improvements and cost reductions. In the literature, various fully or partially autonomous optimization mechanisms exist that optimize single aspects, e.g., index selection. However, database administrators and developers often distrust autonomous approaches, and there is a lack of practical experimentation opportunities that could create a better understanding. Moreover, the interplay of different autonomous mechanisms under complex workloads remains an open question. The presented cockpit enables an interactive assessment of the impact of autonomous components for database systems by comparing (autonomous) systems with different configurations side by side. Thereby, the cockpit enables users to build trust in autonomous solutions by experimenting with such technologies and observing their effects in practice.
@inproceedings{kossmann2021cockpit, abstract = {Databases are highly optimized complex systems with a multitude of configuration options. Especially in cloud scenarios with thousands of database deployments, determining optimized database configurations in an automated fashion is of increasing importance for database providers. At the same time, due to increased system complexity, it becomes more challenging to identify well-performing configurations. Therefore, research interest in autonomous or self-driving database systems has increased enormously in recent years. Such systems promise both performance improvements and cost reductions. In the literature, various fully or partially autonomous optimization mechanisms exist that optimize single aspects, e.g., index selection. However, database administrators and developers often distrust autonomous approaches, and there is a lack of practical experimentation opportunities that could create a better understanding. Moreover, the interplay of different autonomous mechanisms under complex workloads remains an open question. The presented cockpit enables an interactive assessment of the impact of autonomous components for database systems by comparing (autonomous) systems with different configurations side by side. Thereby, the cockpit enables users to build trust in autonomous solutions by experimenting with such technologies and observing their effects in practice.}, author = {Kossmann, Jan and Boissier, Martin and Dubrawski, Alexander and Heseding, Fabian and Mandel, Caterina and Pigorsch, Udo and Schneider, Max and Schniese, Til and Sobhani, Mona and Tsayun, Petr and Wille, Katharina and Perscheid, Michael and Uflacker, Matthias and Plattner, Hasso}, booktitle = {37th IEEE International Conference on Data Engineering, ICDE}, keywords = {in-memory_database myown database self-managing autonomous mboissierselected self-driving hyrise}, pages = {2685-2688}, title = {A Cockpit for the Development and Evaluation of Autonomous Database Systems}, year = 2021 }

2020

1.
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. pp. 2382–2395 (2020).
[ BibTeX ] [ URL ] [ Download ]
 
@inproceedings{DBLP:journals/pvldb/Kossmann, author = {Kossmann, Jan and Halfpap, Stefan and Jankrift, Marcel and Schlosser, Rainer}, booktitle = {Proceedings of the VLDB Endowment}, keywords = {ddds index_selection self-driving hyrise}, number = 11, pages = {2382-2395}, title = {Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms}, volume = 13, year = 2020 }

2.
Kossmann, J., Schlosser, R.: Self-driving database systems: a conceptual approach. Distributed and Parallel Databases. 38, 795–817 (2020).
[ BibTeX ] [ URL ]
 
@article{DBLP:journals/dpd/kossmann, author = {Kossmann, Jan and Schlosser, Rainer}, journal = {Distributed and Parallel Databases}, keywords = {ddds self-driving hyrise}, number = 4, pages = {795-817}, title = {Self-driving database systems: a conceptual approach}, volume = 38, year = 2020 }

2019

1.
Kossmann, J., Schlosser, R.: A Framework for Self-Managing Database Systems. 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW). pp. 100–106 (2019).
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ]
 
Database systems that autonomously manage their configuration and physical database design face numerous challenges: They need to anticipate future workloads, find satisfactory and robust configurations efficiently, and learn from recent actions. We describe a component-based framework for self-managed database systems to facilitate development and database integration with low overhead by relying on a clear separation of concerns. Our framework results in exchangeable and reusable components, which simplify experiments and promote further research. Furthermore, we propose an LP-based algorithm to find an efficient order to tune multiple dependent features in a recursive way.
@inproceedings{8750957, abstract = {Database systems that autonomously manage their configuration and physical database design face numerous challenges: They need to anticipate future workloads, find satisfactory and robust configurations efficiently, and learn from recent actions. We describe a component-based framework for self-managed database systems to facilitate development and database integration with low overhead by relying on a clear separation of concerns. Our framework results in exchangeable and reusable components, which simplify experiments and promote further research. Furthermore, we propose an LP-based algorithm to find an efficient order to tune multiple dependent features in a recursive way.}, author = {Kossmann, Jan and Schlosser, Rainer}, booktitle = {2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)}, keywords = {ddds systems database self-managing self-driving}, month = {April}, pages = {100-106}, title = {A Framework for Self-Managing Database Systems}, year = 2019 }

2.
Schlosser, R., Kossmann, J., Boissier, M.: Efficient Scalable Multi-Attribute Index Selection Using Recursive Strategies. IEEE 35th International Conference on Data Engineering (ICDE 2019). pp. 1238–1249. IEEE (2019).
[ BibTeX ] [ DOI ] [ Download ]
 
@conference{schlosser2019efficient, author = {Schlosser, Rainer and Kossmann, Jan and Boissier, Martin}, booktitle = {IEEE 35th International Conference on Data Engineering (ICDE 2019)}, keywords = {ddds myown mboissierselected}, month = {apr}, pages = {1238-1249}, publisher = {IEEE}, title = {Efficient Scalable Multi-Attribute Index Selection Using Recursive Strategies}, year = 2019 }

3.
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).
[ BibTeX ] [ DOI ] [ Download ]
 
@inproceedings{dreseler2018, author = {Dreseler, Markus and Kossmann, Jan and Boissier, Martin and Klauck, Stefan and Uflacker, Matthias and Plattner, Hasso}, booktitle = {22nd International Conference on Extending Database Technology (EDBT)}, keywords = {myown mboissierselected hyrise}, month = 3, pages = {313-324}, title = {Hyrise Re-engineered: An Extensible Database System for Research in Relational In-Memory Data Management}, year = 2019 }

2018

1.
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).
[ Abstract ] [ BibTeX ] [ URL ]
 
Large data sets, variable workloads of high complexity, and flexible cloud infrastructure make the administration of database systems at the same time more challenging and more important. In the future, self-driving database systems will utilize workload-driven optimization and machine learning techniques to generate forecasts of future workloads, decide upon which actions to take to process workloads most efficiently and to incorporate knowledge from past decisions into future ones without human intervention. But database systems are typically not designed having such capabilities in mind. We propose the architecture of a generalized framework that enables seamless integration of self-driving capabilities into database systems. Thereby, general-purpose database systems can transform themselves into systems tailored to a specific use case. Furthermore, we present our scalable approach to finding solutions for large problem instances of a physical design challenge, i.e., the index selection problem. Both concepts have been implemented and partly evaluated with real-world data on the research database system Hyrise.
@inproceedings{DBLP:conf/vldb/Kossmann18, abstract = {Large data sets, variable workloads of high complexity, and flexible cloud infrastructure make the administration of database systems at the same time more challenging and more important. In the future, self-driving database systems will utilize workload-driven optimization and machine learning techniques to generate forecasts of future workloads, decide upon which actions to take to process workloads most efficiently and to incorporate knowledge from past decisions into future ones without human intervention. But database systems are typically not designed having such capabilities in mind. We propose the architecture of a generalized framework that enables seamless integration of self-driving capabilities into database systems. Thereby, general-purpose database systems can transform themselves into systems tailored to a specific use case. Furthermore, we present our scalable approach to finding solutions for large problem instances of a physical design challenge, i.e., the index selection problem. Both concepts have been implemented and partly evaluated with real-world data on the research database system Hyrise.}, author = {Kossmann, Jan}, booktitle = {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.}, keywords = {myown}, title = {Self-Driving: From General Purpose to Specialized DBMSs}, year = 2018 }

2.
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).
[ BibTeX ] [ DOI ] [ Download ]
 
@conference{dreseler2018fused, author = {Dreseler, Markus and Kossmann, Jan and Frohnhofen, Johannes and Uflacker, Matthias and Plattner, Hasso}, booktitle = {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}, keywords = {hyrise}, title = {Fused Table Scans: Combining AVX-512 and JIT to Double the Performance of Multi-Predicate Scans}, year = 2018 }

3.
Kossmann, J., Dreseler, M., Gasda, T., Uflacker, M., Plattner, H.: Visual Evaluation of SQL Plan Cache Algorithms. Australasian Database Conference (ADC) (2018).
[ BibTeX ] [ DOI ] [ Download ]
 
@conference{kossmann2018visual, author = {Kossmann, Jan and Dreseler, Markus and Gasda, Timo and Uflacker, Matthias and Plattner, Hasso}, booktitle = {Australasian Database Conference (ADC)}, keywords = {hyrise}, title = {Visual Evaluation of SQL Plan Cache Algorithms}, year = 2018 }

4.
Dreseler, M., Gasda, T., Kossmann, J., Uflacker, M., Plattner, H.: Adaptive Access Path Selection for Hardware-Accelerated DRAM Loads. Australasian Database Conference (ADC) (2018).
[ BibTeX ] [ DOI ] [ Download ]
 
@conference{dreseler2018adaptive, author = {Dreseler, Markus and Gasda, Timo and Kossmann, Jan and Uflacker, Matthias and Plattner, Hasso}, booktitle = {Australasian Database Conference (ADC)}, keywords = {hyrise}, title = {Adaptive Access Path Selection for Hardware-Accelerated DRAM Loads}, year = 2018 }

2015

1.
Mueller, S., Fritzsche, M., Kossmann, J., Schneider, M., Striebel, J., Baudisch, P.: Scotty: Relocating Physical Objects Across Distances Using Destructive Scanning, Encryption, and 3D Printing. TEI ’15 Proceedings of the Ninth International Conference on Tangible, Embedded, and Embodied Interaction. pp. 233–240 (2015).
[ BibTeX ] [ DOI ]
 
@inproceedings{Stefanie2015a, author = {Mueller, Stefanie and Fritzsche, Martin and Kossmann, Jan and Schneider, Maximilian and Striebel, Jonathan and Baudisch, Patrick}, journal = {TEI '15 Proceedings of the Ninth International Conference on Tangible, Embedded, and Embodied Interaction}, keywords = {imported}, pages = {233-240}, title = {Scotty: Relocating Physical Objects Across Distances Using Destructive Scanning, Encryption, and 3D Printing}, year = 2015 }

2.
Schwalb, D., Kossmann, J., Faust, M., Klauck, S., Uflacker, M., Plattner, H.: Hyrise-R: Scale-out and Hot-Standby through Lazy Master Replication for Enterprise Applications. Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics (IMDM), in conjunction with VLDB 2015 Kohala Coast, Hawaii (2015).
[ Abstract ] [ BibTeX ] [ URL ]
 
In-memory database systems are well-suited for enterprise workloads, consisting of transactional and analytical queries. A growing number of users and an increasing demand for enterprise applications can saturate or even overload single- node database systems at peak times. Better performance can be achieved by improving a single machine’s hardware but it is often cheaper and more practicable to follow a scale-out approach and replicate data by using additional machines. In this paper we present Hyrise-R, a lazy master replication system for the in-memory database Hyrise. By setting up a snapshot-based Hyrise cluster, we increase both performance by distributing queries over multiple instances and availability by utilizing the redundancy of the cluster structure. This paper describes the architecture of Hyrise- R and details of the implemented replication mechanisms. We set up Hyrise-R on instances of Amazon’s Elastic Compute Cloud and present a detailed performance evaluation of our system, including a linear query throughput increase for enterprise workloads.
@inproceedings{David2015d, abstract = {In-memory database systems are well-suited for enterprise workloads, consisting of transactional and analytical queries. A growing number of users and an increasing demand for enterprise applications can saturate or even overload single- node database systems at peak times. Better performance can be achieved by improving a single machine’s hardware but it is often cheaper and more practicable to follow a scale-out approach and replicate data by using additional machines. In this paper we present Hyrise-R, a lazy master replication system for the in-memory database Hyrise. By setting up a snapshot-based Hyrise cluster, we increase both performance by distributing queries over multiple instances and availability by utilizing the redundancy of the cluster structure. This paper describes the architecture of Hyrise- R and details of the implemented replication mechanisms. We set up Hyrise-R on instances of Amazon’s Elastic Compute Cloud and present a detailed performance evaluation of our system, including a linear query throughput increase for enterprise workloads.}, author = {Schwalb, David and Kossmann, Jan and Faust, Martin and Klauck, Stefan and Uflacker, Matthias and Plattner, Hasso}, booktitle = {Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics (IMDM), in conjunction with VLDB 2015 Kohala Coast, Hawaii}, keywords = {HYRISE imported}, title = {Hyrise-R: Scale-out and Hot-Standby through Lazy Master Replication for Enterprise Applications}, year = 2015 }