Categorical Management of Multi-Model Data

Holubová, Irena; Čontoš, Pavel; Svoboda, Martin

doi:10.1145/3472163.3472166

Cited by 3 publications

(1 citation statement)

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“…Frameworks for objectoriented databases have been proposed in [30] and for documentoriented databases in [51]. The management of multi-model data and data integration issues are studied in [28,32,52] with some basic categorical tools like categories and functors as well as with more advanced one like pullbacks, pushforwards and kan lifts. Finally, metadata models based on category theory have been proposed in [7,12].…”

Section: Contributions Of Category Theory To Data Lakesmentioning

confidence: 99%

A Formal Framework for Data Lakes Based on Category Theory

Guyot

Gillet

Leclercq

et al. 2022

International Database Engineered Applications Symposium

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The management of Big Data requires flexible systems to handle the heterogeneity of data models as well as the complexity of analytical workflows. Traditional systems like data warehouses have reached their limits due to their rigid schema-on-write paradigm, that requires well identified and defined use cases to ingest data. Data lakes, with their schema-on-read paradigm, have been proposed as more flexible systems in which raw data are directly stored in their original format associated with metadata, to be accessed and transformed only when users need to process or analyze them. Thus, it is necessary to define and control the different levels of abstraction and the dependencies among functionalities of a data lake to use it efficiently. In this article, we present a formal framework aiming to define a data lake pattern and to unify the interactions among the functionalities. We use the category theory as theoretical foundations to benefit from its high level of abstraction and its compositionality. By relying on different categories and functors, we ensure the navigation among the functionalities and allow the composition of multiples operations, while keeping track of the entire lineage of data. We also show how our framework can be applied on a simple example of data lake.

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Section: Contributions Of Category Theory To Data Lakesmentioning

confidence: 99%

A Formal Framework for Data Lakes Based on Category Theory

Guyot

Gillet

Leclercq

et al. 2022

International Database Engineered Applications Symposium

View full text Add to dashboard Cite

show abstract

Towards automating microservices orchestration through data-driven evolutionary architectures

Bergami

2024

SOCA

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A unified representation and transformation of multi-model data using category theory

Koupil

Holubová

2022

J Big Data

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The support for multi-model data has become a standard for most of the existing DBMSs. However, the step from a conceptual (e.g., ER or UML) schema to a logical multi-model schema of a particular DBMS is not straightforward. In this paper, we extend our previous proposal of multi-model data representation using category theory for transformations between models. We introduce a mapping between multi-model data and the categorical representation and algorithms for mutual transformations between them. We also show how the algorithms can be implemented using the idea of wrappers with the interface published but specific internal details concealed. Finally, we discuss the applicability of the approach to various data management tasks, such as conceptual querying.

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Categorical Management of Multi-Model Data

Cited by 3 publications

References 15 publications

A Formal Framework for Data Lakes Based on Category Theory

A Formal Framework for Data Lakes Based on Category Theory

Towards automating microservices orchestration through data-driven evolutionary architectures

A unified representation and transformation of multi-model data using category theory

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