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Unified Management of Multi-model Data

(Vision Paper)

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Conceptual Modeling (ER 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11788))

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Abstract

The variety of data is one of the most challenging issues for research and practice in data management. The so-called multi-model data are naturally organized in different and mutually interlinked data formats and logical models, including structured, semi-structured, and unstructured. In this vision paper, we discuss the so far neglected, but for correct and efficient management of multi-model data critical issues and challenges: conceptual modeling of multi-model data, inference of multi-model schemas, unified and conceptual querying, evolution management, and, last but not least, autonomous multi-model data management.

This work was partially supported by the Charles University project PROGRES Q48 and the Academy of Finland project number 310321.

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Authors and Affiliations

  1. Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic

    Irena Holubová & Martin Svoboda

  2. University of Helsinki, Helsinki, Finland

    Jiaheng Lu

Authors
  1. Irena Holubová
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  2. Martin Svoboda
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  3. Jiaheng Lu
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Corresponding author

Correspondence to Martin Svoboda .

Editor information

Editors and Affiliations

  1. Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Alberto H. F. Laender

  2. Politecnico di Milano, Milan, Italy

    Barbara Pernici

  3. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  4. Univ Federal do Rio Grande do Sul, Porto Alegre, Brazil

    José Palazzo M. de Oliveira

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Holubová, I., Svoboda, M., Lu, J. (2019). Unified Management of Multi-model Data. In: Laender, A., Pernici, B., Lim, EP., de Oliveira, J. (eds) Conceptual Modeling. ER 2019. Lecture Notes in Computer Science(), vol 11788. Springer, Cham. https://doi.org/10.1007/978-3-030-33223-5_36

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  • DOI: https://doi.org/10.1007/978-3-030-33223-5_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33222-8

  • Online ISBN: 978-3-030-33223-5

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