Multi-Model Data Modeling and Representation: State of the Art and Research Challenges

Holubová, Irena; Čontoš, Pavel; Svoboda, Martin

doi:10.1145/3472163.3472267

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…MMDWs open many research avenues, at the conceptual level (e.g., how to extend the existing conceptual models to cope with schemaless data Holubová, Svoboda and Lu, [26], Holubová et al [24]), at the logical level (e.g., how to select and use materialized views in MMDWs), and at the physical level (e.g., what ad hoc indexing strategies to adopt for MMDWs, or what is the benefit of using native graphs in an MMDWs). Our future work will be mainly placed at the conceptual level; specifically, we will investigate how to extend the UML profiles usually adopted for conceptual design of multidimensional data to cope with variety issues.…”

Section: Discussionmentioning

confidence: 99%

“…Among the topics currently investigated for MMDBMSs, we mention conceptual modeling of multi-model data Holubová, Contos and Svoboda, [24], inference of multi-model schemata, multi-model querying Holubová, Svoboda and Lu, [26] as well as evolution management Holubová, Klettke and Störl, [25]. To the best of our knowledge, the only work dealing with multi-model logical design starting from a conceptual schema is the one by Svoboda, Contos and Holubová [39], which uses category theory to formalize the transformation from elements of an Entity/Relationship diagram to relational tables, documents, and graphs in an MMDBMS.…”

Section: Polystores and Multi-model Databasesmentioning

confidence: 99%

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Logical design of multi-model data warehouses

et al. 2022

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Multi-model DBMSs, which support different data models with a fully integrated backend, have been shown to be beneficial to data warehouses and OLAP systems. Indeed, they can store data according to the multidimensional model and, at the same time, let each of its elements be represented through the most appropriate model. An open challenge in this context is the lack of methods for logical design. Indeed, in a multi-model context, several alternatives emerge for the logical representation of dimensions and facts. The goal of this paper is to devise a set of guidelines for the logical design of multi-model data warehouses so that the designer can achieve the best trade-off between features such as querying, storage, and ETL. To this end, for each model considered (relational, document-based, and graph-based) and for each type of multidimensional element (e.g., non-strict hierarchy) we propose some solutions and carry out a set of intra-model and inter-model comparisons. The resulting guidelines are then tested on a case study that shows all types of multidimensional elements.

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Section: Discussionmentioning

confidence: 99%

Section: Polystores and Multi-model Databasesmentioning

confidence: 99%

Logical design of multi-model data warehouses

et al. 2022

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“…increase the efficiency of the querying phase in schema-on-read approaches by paving the way to a more sophisticated optimization of query; -develop techniques for online repairing of approximate functional dependencies present in schemaless data, so that the user can get correct analysis results without modifying the original data; -extend the existing conceptual models to cope with schemaless data [21,20]); -understand how to select and use materialized views in MMDWs, and what ad-hoc indexing strategies to adopt for them.…”

Section: Discussionmentioning

confidence: 99%

OLAP and NoSQL: Happily Ever After

Rizzi

2022

Advances in Databases and Information Systems

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NoSQL databases are preferred to relational ones for storing heterogeneous data with variable schema and structure. However, their schemaless nature adds complexity to analytical applications, in which a single OLAP analysis often involves large sets of data with different schemas. In this tutorial we describe the main approaches to enable OLAP on NoSQL data. We start from schema-on-read approaches, where data are left unchanged in their structure until they are accessed by the user, so they are put into multidimensional form at query time. Specifically, we show how this enables a form of approximated OLAP that embraces the inherent variety of schemaless data. Then we move to schema-on-write approaches, where a fixed multidimensional structure is forced onto data, which are loaded into a data warehouse to be then queried. In particular, we introduce multi-model data warehouses as a way to store data in multidimensional form and, at the same time, let each piece of data be natively represented through the most appropriate NoSQL model.

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Towards automating microservices orchestration through data-driven evolutionary architectures

Bergami

2024

SOCA

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Multi-Model Data Modeling and Representation: State of the Art and Research Challenges

Cited by 6 publications

References 11 publications

Logical design of multi-model data warehouses

Logical design of multi-model data warehouses

OLAP and NoSQL: Happily Ever After

Towards automating microservices orchestration through data-driven evolutionary architectures

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