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An Automated Patterns-Based Model-to-Model Mapping and Transformation System for Labeled Property Graphs

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Research Challenges in Information Science: Information Science and the Connected World (RCIS 2023)


Due to the increasing collection of highly interconnected and complex datasets, Labeled Property Graphs are gaining importance in extracting meaningful information for decision support. In addition, UML Class Diagrams are still a commonly used modeling technique for representing the main concepts of a domain. Although there are several model-to-model transformation approaches, these are mainly focused on moving from class diagrams to relational databases. Less work has been done on transforming class diagrams into labeled property graphs. This work constitutes a step forward in filling this gap by i) using a method that defines a set of patterns to improve the transformation process from class diagrams to labeled property graphs, considering the analytical requirements of a domain, and ii) proposing a technological system as an instantiation of the method, demonstrating its feasibility and enabling the assessment of its suitability. This system is grounded in a collection of templates for specifying the domain concepts and a library of transformation rules and patterns, and was evaluated using a widely known dataset exhibiting the proposed model-to-model transformation approach.

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This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020, and by the Spanish Ministry of Universities and the Universitat Politècnica de València under the Margarita Salas Next Generation EU grant.

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Correspondence to Pedro Guimarães .

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Guimarães, P., León, A., Santos, M.Y. (2023). An Automated Patterns-Based Model-to-Model Mapping and Transformation System for Labeled Property Graphs. In: Nurcan, S., Opdahl, A.L., Mouratidis, H., Tsohou, A. (eds) Research Challenges in Information Science: Information Science and the Connected World. RCIS 2023. Lecture Notes in Business Information Processing, vol 476. Springer, Cham.

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