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MM-evocat: A Tool for Modelling and Evolution Management of Multi-Model Data

Basic Information

A modular and extensible framework that ensures evolution management and backwards propagation to multi-model data. Following the current trends, the implementation can process efficiently also large amounts of data.

In addition, MM-evocat also provides a categorical representation of multi-model data instances to be processed uniformly.

For more details, see Documentation.



  1. Pavel Koupil, and Irena Holubová. 2022. A unified representation and transformation of multi‐model data using category theory J Big Data 9, 61 (2022). Springer Nature, 2022. DOI: 10.1186/s40537-022-00613-3
  2. Pavel Koupil, and Irena Holubová. 2022. Unifying Categorical Representation of Multi-Model Data. In SAC ’22: 37th ACM/SIGAPP Symposium On Applied Computing (SAC 2022), Brno, Czech Republic, April 2022. ACM, 2022 (accepted) DOI: 10.1145/3477314.3507690
  3. Pavel Koupil, Martin Svoboda, and Irena Holubová. 2021. MM-cat: A Tool for Modeling and Transformation of Multi-Model Data using Category Theory. Proceedings of the ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS 2021), Fukuoka, Japan, October 2021. IEEE, 2021. DOI: 10.1109/MODELS-C53483.2021.00098
  4. Irena Holubová, Pavel Čontoš (Koupil), and Martin Svoboda. 2021. Categorical Management of Multi-Model Data. In 25th International Database Engineering & Applications Symposium (IDEAS 2021). Association for Computing Machinery, New York, NY, USA, 134–140. DOI: 10.1145/3472163.3472166
  5. Martin Svoboda, Pavel Čontoš (Koupil), and Irena Holubová. 2021. Categorical Modeling of Multi-Model Data: One Model to Rule Them All. In International Conference on Model and Data Engineering (MEDI 2021), Tallinn, Estonia, June 2021. Lecture Notes in Computer Science, volume 12732, Springer, Cham, 2021. p. 190-198. ISBN 978-3-030-78427-0. DOI: 10.1007/978-3-030-78428-7_15
  6. Pavel Čontoš (Koupil). 2021. Abstract Model for Multi-model Data. In International Conference on Database Systems for Advanced Applications (DASFAA 2021). Lecture Notes in Computer Science, vol 12683. Springer, Cham, 2021. p. 647-651. ISBN 978-3-030-73199-1. DOI: 10.1007/978-3-030-73200-4_53


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