A universal approach for simplified redundancy-aware cross-model querying

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10490596" target="_blank" >RIV/00216208:11320/25:10490596 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=SvrhdmFyFo" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=SvrhdmFyFo</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.is.2024.102456" target="_blank" >10.1016/j.is.2024.102456</a>

Result language
angličtina
Original language name
A universal approach for simplified redundancy-aware cross-model querying
Original language description
Numerous challenges and open problems have appeared with the dawn of multi-model data. In most cases, single-model solutions cannot be straightforwardly extended, and new, efficient approaches must be found. In addition, since there are no standards related to combining and managing multiple models, the situation is even more complicated and confusing for users. This paper deals with the most important aspect of data management - querying. To enable the user to grasp all the popular models, we base our solution on the abstract categorical representation of multi-model data, which can be viewed as a graph. To unify the querying of multi-model data, we enable the user to query the categorical graph using a SPARQL-based model-agnostic query language called MMQL. The query is then decomposed and translated into languages of the underlying systems. The intermediate results are then combined into the final categorical result that can be expressed in any selected format. The support for cross-model redundancy enables one to create distinct query plans and choose the optimal one. We also introduce a proof-of-concept implementation of our solution called MM-quecat.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GA23-07781S" target="_blank" >GA23-07781S: Self-Adapting Management of Multi-Model Databases</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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