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A Big Data Case Study in Digital Humanities

Creating a Performance Benchmark for Canonical Text Services

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Abstract

While the volume of primary data in the text oriented humanities is small in comparison to the terabytes that are nowadays standard in Big Data applications, secondary data that are the result of scholarly annotations require a fine-grained hierarchical structure based reference model for primary data. The paper provides an attempt for a reusable performance benchmark for Canonical Text Services, a service to access and retrieve text content and structural meta information for hierarchically structured texts, and shows how it can be used to evaluate the technical performance of such a system.

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Notes

  1. The result from MySQL’s AUTO_INCREMENT is not necessarily gap-free.

  2. SELECT urn WHERE urn LIKE BINARY “urn:cts:pbc:bible.parallel.eng.kingjames:2.1.%”.

  3. The dot . and the colon :.

  4. SELECT URN WHERE urn LIKE “[URN]%” AND urn LIKE BINARY “[URN]%”.

  5. The text column has been indexed due to the implementation of the full-text search described in [14], but this additional index is not required for the CTS index.

  6. JAVA was chosen because of its widespread support and its uncomplicated use as web applications(Servlets).

  7. The CTS URN urn:cts:pbc:bible.parallel.eng.kingjames:2.1.2 is 46 characters long.

  8. A B-Tree that is processed in such a way that input and output are equal to that of a trie.

  9. Including the speed of the network itself, possible internal proxy redirects, additional server traffic, and even the performance that a specific browser software provides.

  10. See https://developer.ted.com/.

  11. The incorrect document-level meta information in the TED subtitle transcripts cannot be repaired because the API is closed. This is not a problem for a performance benchmark because its purpose is not to validate the content.

  12. pbc:657,936, dta:16,438,119, ted:15,292,408.

  13. dta2, dta3, ted2, dta1, ted3, dta4, ted1, ted4, pbc.

  14. For example, Sentence 1 in Chap. 2 to Sentence 3 in Chap. 4.

  15. JAVA’s default StringTokenizer is used with space, tab, newline, carriage-return, and form-feed as hardcoded delimiters. If these are not applicable to a specific language, then the sub-passage is the non-tokenized text, which is also a correct request.

  16. AuthenticAMD Common KVM Processor.

  17. Debian 8.5 3.167-ckt25-2 /2016-04-08) x86_64, codename Jessie.

  18. AMD Opteron 6234.

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Acknowledgements

Part of this work was funded by the German Federal Ministry of Education and Research within the project ScaDS Dresden/Leipzig (BMBF 01IS14014B).

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Authors and Affiliations

  1. NLP Group, Leipzig University, Leipzig, Germany

    Gerhard Heyer & Jochen Tiepmar

  2. ScaDS, Leipzig University, Leipzig, Germany

    Jochen Tiepmar

Authors
  1. Gerhard Heyer
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  2. Jochen Tiepmar
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Correspondence to Jochen Tiepmar.

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Heyer, G., Tiepmar, J. A Big Data Case Study in Digital Humanities. Datenbank Spektrum 19, 41–49 (2019). https://doi.org/10.1007/s13222-018-00302-7

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