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A Benchmarking Framework for Stream Processors

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Knowledge Engineering and Knowledge Management (EKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10180))

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Abstract

Stream Processing/Reasoning, an active research topic [5], has been picked up by different communities which developed a diversity of stream processors/reasoners. This however makes empirical evaluation and comparison of these engines a non-trivial task [4]. Different classes of those engines work on different formats of input data, use different languages to formulate queries, evaluate these queries using different semantics and produce different formats of output. To be able to compare such engines, a benchmarking framework that can cope with this wide diversity is needed.

This research has been supported by the Austrian Science Fund (FWF) projects P24090, P26471, and W1255-N23.

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Notes

  1. 1.

    http://spark.apache.org/.

  2. 2.

    https://github.com/potassco/clingo.

References

  1. General transit feed specification. https://developers.google.com/transit/. Accessed 15 Sep 2016

  2. sr_data_generator github repository. https://github.com/mosimos/sr_data_generator/. Accessed 15 Sep 2016

  3. Barbieri, D.F., Braga, D., Ceri, S., Valle, E.D., Grossniklaus, M.: C-SPARQL: a continuous query language for RDF data streams. Int. J. Semant. Comput. 4(1), 3–25 (2010)

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  5. Valle, D.E., Ceri, S., van Harmelen, F., Fensel, D.: It’s a streaming world! reasoning upon rapidly changing information. IEEE Intell. Syst. 24, 83–89 (2009)

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  7. Le-Phuoc, D., Dao-Tran, M., Xavier Parreira, J., Hauswirth, M.: A native and adaptive approach for unified processing of linked streams and linked data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 370–388. Springer, Heidelberg (2011). doi:10.1007/978-3-642-25073-6_24

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Author information

Authors and Affiliations

  1. Institute of Information Systems, Vienna University of Technology, Favoritenstraße 9-11, 1040, Vienna, Austria

    Andreas Moßburger, Harald Beck, Minh Dao-Tran & Thomas Eiter

Authors
  1. Andreas Moßburger
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  2. Harald Beck
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  3. Minh Dao-Tran
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  4. Thomas Eiter
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Corresponding author

Correspondence to Minh Dao-Tran .

Editor information

Editors and Affiliations

  1. University of Bologna , Bologna, Italy

    Paolo Ciancarini

  2. University of Bologna , Bologna, Italy

    Francesco Poggi

  3. Stanford University , Palo Alto, California, USA

    Matthew Horridge

  4. Lancaster University , Lancaster, United Kingdom

    Jun Zhao

  5. Garvan Institute of Medical Research , Darlinghurst, New South Wales, Australia

    Tudor Groza

  6. Universidad Politecnica de Madrid , Boadilla del Monte, Spain

    Mari Carmen Suarez-Figueroa

  7. The Open University , Milton Keynes, United Kingdom

    Mathieu d'Aquin

  8. STLab ISTC-CNR , Rome, Italy

    Valentina Presutti

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Moßburger, A., Beck, H., Dao-Tran, M., Eiter, T. (2017). A Benchmarking Framework for Stream Processors. In: Ciancarini, P., et al. Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10180. Springer, Cham. https://doi.org/10.1007/978-3-319-58694-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-58694-6_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58693-9

  • Online ISBN: 978-3-319-58694-6

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