TPC-BiH: A Benchmark for Bitemporal Databases

  • Martin Kaufmann
  • Peter M. Fischer
  • Norman May
  • Andreas Tonder
  • Donald Kossmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8391)


An increasing number of applications such as risk evaluation in banking or inventory management require support for temporal data. After more than a decade of standstill, the recent adoption of some bitemporal features in SQL:2011 has reinvigorated the support among commercial database vendors, who incorporate an increasing number of relevant bitemporal features. Naturally, assessing the performance and scalability of temporal data storage and operations is of great concern for potential users. The cost of keeping and querying history with novel operations (such as time travel, temporal joins or temporal aggregations) is not adequately reflected in any existing benchmark. In this paper, we present a benchmark proposal which provides comprehensive coverage of the bitemporal data management. It builds on the solid foundations of TPC-H but extends it with a rich set of queries and update scenarios. This workload stems both from real-life temporal applications from SAP’s customer base and a systematic coverage of temporal operators proposed in the academic literature. We present preliminary results of our benchmark on a number of temporal database systems, also highlighting the need for certain language extensions.


Bitemporal Databases Benchmark Data Generator 


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  1. 1.
    Al-Kateb, M., Crolotte, A., Ghazal, A., Rose, L.: Adding a Temporal Dimension to the TPC-H Benchmark. In: Nambiar, R., Poess, M. (eds.) TPCTC 2012. LNCS, vol. 7755, pp. 51–59. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Cole, R., et al.: The Mixed Workload CH-benCHmark. In: DBTest, p. 8 (2011)Google Scholar
  3. 3.
    Gray, J.: Benchmark Handbook: For Database and Transaction Processing Systems. Morgan Kaufmann Publishers Inc., San Francisco (1992)Google Scholar
  4. 4.
    Jensen, C.S., et al.: A consensus test suite of temporal database queries. Tech. rep., Department of Computer Science, Aarhus University (1993)Google Scholar
  5. 5.
    Kalua, P.P., Robertson, E.L.: Benchmarking Temporal Databases - A Research Agenda. Tech. rep., Indiana University, Computer Science Department (1995)Google Scholar
  6. 6.
    Kaufmann, M., Fischer, P.M., Kossmann, D., May, N.: A Generic Database Benchmarking Service. In: ICDE (2013)Google Scholar
  7. 7.
    Kaufmann, M., Kossmann, D., May, N., Tonder, A.: Benchmarking Databases with History Support. Tech. report. ETH Zurich and SAP AG (2013)Google Scholar
  8. 8.
    Kaufmann, M., Manjili, A., Vagenas, P., Fischer, P., Kossmann, D., Faerber, F., May, N.: Timeline index: A unified data structure for processing queries on temporal data in SAP HANA. In: SIGMOD (2013)Google Scholar
  9. 9.
    Kulkarni, K.G., Michels, J.E.: Temporal Features in SQL: 2011. SIGMOD Record 41(3) (2012)Google Scholar
  10. 10.
    Salzberg, B., Tsotras, V.J.: Comparison of access methods for time-evolving data. ACM Comput. Surv. 31(2), 158–221 (1999)CrossRefGoogle Scholar
  11. 11.
    Snodgrass, R.T.: Developing Time-Oriented Database Applications in SQL. Morgan Kaufmann (1999)Google Scholar
  12. 12.
    Snodgrass, R.T., et al.: TSQL2 language specification. SIGMOD Record 23(1) (1994)Google Scholar
  13. 13.
    Werstein, P.: A Performance Benchmark for Spatiotemporal Databases. In: Proc. of the 10th Annual Colloquium of the Spatial Information Research Centre, pp. 365–373 (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Martin Kaufmann
    • 1
    • 2
  • Peter M. Fischer
    • 3
  • Norman May
    • 1
  • Andreas Tonder
    • 1
  • Donald Kossmann
    • 2
  1. 1.SAP AGWalldorfGermany
  2. 2.ETH ZurichZurichSwitzerland
  3. 3.Albert-Ludwigs-UniversitätFreiburgGermany

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