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Temporal Benchmarks

  • Living reference work entry
  • First Online:
Encyclopedia of Database Systems

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Definition

Temporal benchmarks are used to compare temporal support for various databases. Benchmarks consist of data, a schema for that data, and workloads that perform computations on that data.

Historical Background

Benchmarks

Benchmarks consist of three parts, data, a schema for that data, and workloads that perform computations on that data. They have been used in order to provide of means of comparing the performance of various software systems, including databases. Among the most widely used benchmarks are those published by the Transaction Processing Performance Council including TPC-C and TPC-H [1, 2].

TPC-C provides an environment that simulates several uses interacting with a single database at one time. TPC-C models the data, schema, and workloads after those of a wholesale supplier. To replicate this process, TPC-C simulates users that enter new orders, record payments, check the status of an order, deliver an order, and view the available products in the warehouse.

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Recommended Reading

  1. Transaction Processing Performance Council. TPC benchmarks. http://www.tpc.org/information/benchmarks.asp. Accessed 26 Sept 2014.

  2. Gray J. Benchmark handbook: for database and transaction processing systems. San Francisco: Morgan Kaufmann Publishers Inc.; 1992.

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  3. Yao BB, Özsu MT, Khandelwal N. XBench benchmark and performance testing of XML DBMSs. In: Proceedings of International Conference on Data Engineering; 2004. p. 621–33.

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  4. Jensen CS. A consensus test suite of temporal database queries. Department of Mathematics and Computer Science, Institute for Electronic Systems, University of Aalborg, 1993.

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  5. Kaufmann M, Fischer PM, May N, Tonder A, Kossmann D. TPC-BiH: a Benchmark for bitemporal databases. In: Proceedings of TPC Technology Conference. Berlin/New York: Springer; 2014. p. 16–31.

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  6. Thomas SW, Snodgrass RT, Zhang R. Ï„Bench: extending XBench with time. Time Center technical report, University of Arizona, TR-92, 2010.

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  7. Currim F, Currim S, Dyreson C, Joshi S, Snodgrass RT, Thomas SW, Roeder E. Ï„XSchema: support for data- and schema-versioned XML documents. TimeCenter technical report, University of Arizona, TR-91, 2009.

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Author information

Authors and Affiliations

  1. University of Arizona, Tucson, AZ, USA

    Jennifer Dempsey & Richard T. Snodgrass

  2. Dataware Ventures, Tuscon, AZ, USA

    Stephen W. Thomas & Rui Zhang

Authors
  1. Jennifer Dempsey
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  2. Richard T. Snodgrass
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  3. Stephen W. Thomas
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  4. Rui Zhang
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Corresponding author

Correspondence to Jennifer Dempsey .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, Ontario, Canada

    M. Tamer Özsu

Section Editor information

  1. Department of Computer Science, University of Arizona, 711 Gould Simpson, P.O. Box 210077, Tucson, AZ, USA

    Richard T. Snodgrass

  2. Aalborg University, Selma Lagerlöfs Vej 300, DK-9220, Aalborg Øst, Denmark

    Christian S. Jensen PhD

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Dempsey, J., Snodgrass, R.T., Thomas, S.W., Zhang, R. (2017). Temporal Benchmarks. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_80730-1

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  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_80730-1

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4899-7993-3

  • Online ISBN: 978-1-4899-7993-3

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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