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Evolution Management of Multi-model Data

(Position Paper)

  • Conference paper
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Heterogeneous Data Management, Polystores, and Analytics for Healthcare (DMAH 2019, Poly 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11721))

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Abstract

The variety of data is one of the most challenging issues for the research and practice in data management. The so-called multi-model data are naturally organized in different, but mutually linked formats and models, including structured, semi-structured, and unstructured. In this position paper we discuss the so far neglected, but from the point of view of real-world applications important aspect of evolution management of multi-model data. We provide a motivation scenario and we discuss key related challenges, such as multi-model data modelling, intra vs. inter model changes, global and local evolution operations, eager vs. lazy migration, and schema inference .

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Notes

  1. 1.

    Note that the UPDATE command should be done for all the key/value records.

  2. 2.

    For a concrete implementation, the definitions of identifier and value must still be specified.

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Acknowledgements

This work was partly supported by the German Research Foundation (Deutsche Forschungsgemeinschaft (DFG)), grant number 385808805 (M. Klettke, U. Störl) and the Charles University project PROGRES Q48 (I. Holubová). We want to thank Stefanie Scherzinger and Mark Lukas Möller for numerous interesting and helpful discussions and several comments on this work.

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Authors and Affiliations

  1. Charles University, Prague, Czech Republic

    Irena Holubová

  2. University of Rostock, Rostock, Germany

    Meike Klettke

  3. University of Applied Sciences, Darmstadt, Germany

    Uta Störl

Authors
  1. Irena Holubová
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  2. Meike Klettke
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  3. Uta Störl
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Corresponding author

Correspondence to Irena Holubová .

Editor information

Editors and Affiliations

  1. Massachusetts Institute of Technology, Lexington, MA, USA

    Vijay Gadepally

  2. Intel Corporation, Portland, OR, USA

    Timothy Mattson

  3. Massachusetts Institute of Technology, Cambridge, MA, USA

    Michael Stonebraker

  4. Stony Brook University, Stony Brook, NY, USA

    Fusheng Wang

  5. University of Washington, Seattle, WA, USA

    Gang Luo

  6. Google, Mountain View, CA, USA

    Yanhui Laing

  7. Lucerne University of Applied Sciences, Rotkreuz, Switzerland

    Alevtina Dubovitskaya

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Holubová, I., Klettke, M., Störl, U. (2019). Evolution Management of Multi-model Data . In: Gadepally, V., et al. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2019 2019. Lecture Notes in Computer Science(), vol 11721. Springer, Cham. https://doi.org/10.1007/978-3-030-33752-0_10

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