Unlocking the potential of nextGen multi-model databases for semantic big data projects
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10398874" target="_blank" >RIV/00216208:11320/19:10398874 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1145/3323878.3325807" target="_blank" >http://dx.doi.org/10.1145/3323878.3325807</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3323878.3325807" target="_blank" >10.1145/3323878.3325807</a>
Alternative languages
Result language
angličtina
Original language name
Unlocking the potential of nextGen multi-model databases for semantic big data projects
Original language description
As we discuss in this paper, a new generation of multi-model database systems seems a promising architectural choice for building such scalable, non-native triple stores. In this paper, we first characterize this new generation of multi-model databases. Then, discussing an example scenario, we show how they allow for agile and flexible schema management, spanning a large design space for creative and incremental data modelling. We identify the challenge of generating sound triple-views from data stored in several, interlinked models, for SPARQL querying. We regard this as one of several appealing research challenges where the semantic big data and the database architecture community may join forces.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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)
Result continuities
Project
<a href="/en/project/GA19-01641S" target="_blank" >GA19-01641S: Contextual Similarity Search in Open Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the International Workshop on Semantic Big Data
ISBN
978-1-4503-6766-0
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
1-6
Publisher name
ACM
Place of publication
New York, NY, USA
Event location
Amsterdam, Netherlands
Event date
Jul 5, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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