Name
Abstract | ||
|---|---|---|
This paper aims to initiate a discussion around benchmarking data management systems with machine-learned components. Traditional benchmarks such as TPC or YCSB are insufficient to analyze and understand these learned systems because they evaluate the performance under a stable workload and data distribution. Learned systems automatically specialize and adapt database components to a changing work... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICDEW53142.2021.00029 | 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW) |
Keywords | DocType | ISSN |
benchmark,learned system,instance-optimized system,data management,machine learning,metrics,cost of ownership | Conference | 1943-2895 |
ISBN | Citations | PageRank |
978-1-6654-4890-1 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laurent Bindschaedler | 1 | 83 | 4.09 |
| Andreas Kipf | 2 | 32 | 11.03 |
| Tim Kraska | 3 | 2226 | 133.57 |
| Ryan Marcus | 4 | 64 | 11.51 |
| Umar Farooq Minhas | 5 | 352 | 23.16 |