Publication

After more than a decade of a virtual standstill, the adoption of temporal data management features has recently picked up speed, driven by customer demand and the inclusion of temporal expressions into SQL:2011. Most of the big commercial DBMS now include support for bitemporal data and operators. In this paper, we perform a thorough analysis of these commercial temporal DBMS: We investigate their architecture, determine their performance and study the impact of performance tuning. This analysis utilizes our recent (TPCTC 2013) benchmark proposal, which includes a comprehensive temporal workload definition. The results of our analysis show that the support for temporal data is still in its infancy: All systems store their data in regular, statically partitioned tables and rely on standard indexes as well as query rewrites for their operations. As shown by our measurements, this causes considerable performance variations on slight workload variations and a significant effort for performance tuning. In some cases, there is considerable overhead for temporal operations even after extensive tuning.

@inproceedings{abc,
	abstract = {After more than a decade of a virtual standstill, the adoption of temporal data management features has recently picked up speed, driven by customer demand and the inclusion of temporal expressions into SQL:2011. Most of the big commercial DBMS now include support for bitemporal data and operators. 
In this paper, we perform a thorough analysis of these commercial temporal DBMS: We investigate their architecture, determine their performance and study the impact of performance tuning. This analysis utilizes our recent (TPCTC 2013) benchmark proposal, which includes a comprehensive temporal workload definition.
The results of our analysis show that the support for temporal data is still in its infancy:  All systems store their data in regular, statically partitioned tables and rely on standard indexes as well as query rewrites for their operations. As shown by our measurements, this causes considerable performance variations on slight workload variations and a significant effort for performance tuning. In some cases, there is considerable overhead for temporal operations even after extensive tuning.},
	author = {Martin Kaufmann and Peter M. Fischer and Norman May and Donald Kossmann},
	booktitle = {EDBT: 17th International Conference on Extending Database Technology},
	title = {Benchmarking Bitemporal Database Systems:  Ready for the Future or Stuck in the Past?},
	year = {2014}
}