Paper Info
Title
Unlocking the potential of nextGen multi-model databases for semantic big data projects
Abstract
A new vision in semantic big data processing is to create enterprise data hubs, with a 360° view on all data that matters to a corporation. 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.
Year
DOI
Venue
2019
10.1145/3323878.3325807
Proceedings of the International Workshop on Semantic Big Data
Keywords
Field
DocType
multi-model DBMS, schema evolution, semantic data management
Data architecture,Data modeling,Computer science,SPARQL,Agile software development,Enterprise data management,Big data,Schema evolution,Database,Scalability
Conference
ISBN
Citations 
PageRank 
978-1-4503-6766-0
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Irena Holubová11910.58
Stefanie Scherzinger220920.82