Examples of using Graph databases in English and their translations into Indonesian
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Ecclesiastic
NoSQL databases can be classified on the basis of way of storing data as graph databases, key-value store databases,
NoSQL databases can be classified depending upon the way of storing data as graph databases, key value store databases,
Graph databases are inherently more flexible than traditional relational database systems because it is possible to treat the metadata about the database as data itself,
Graph databases have advantages for use cases such as social networking,
Finally, graph databases are increasingly taking advantage of a completely unexpected development- the growing power
While most databases use tabular searches which can find all the documents in which a name is mentioned, graph databases- imagine a spider web of lines- help reveal all the connections between those names and documents.
Graph databases have been around in one form
In most cases these products focused upon graph databases as property graph databases, and in some cases have jettisoned features such as inferencing that has become increasingly irrelevant as SPARQL achieves broader adoption.
While most databases use tabular searches which can find all the documents in which a name is mentioned, graph databases- imagine a spider web of lines- help reveal all the connections linking those names and documents.
Graph databases have advantages over relational databases for certain use cases- including social networking,
especially towards the end of the 2010s, graph database vendors are increasingly taking advantage of GPUs to traverse and compare node values along these graphs, effectively taking advantage of the massively parallel capabilities such GPUs offer to power graph databases.
documents, graph databases or wide-column stores which do not have standard schema definitions which it needs to adhered to.
a couple open source graph databases, all of them collectively serving a market that was microscopic in comparison to the relational market.
there were maybe four commercial and a couple open source graph databases, all of them collectively serving a market that was microscopic in comparison to the relational market.
As important, several companies began experimenting with graph databases to solve problems that were beginning to become vexing at the corporate level- management of enterprise metadata,
which typically uses a data clustering approach to text analysis as a brute force alternative has recently increasingly become just another mechanism to help build graph databases, to the extent that the most recent graph databases are now incorporating machine-learning algorithms
especially important for graph databases.
Amazon Neptune is a purpose-built, high-performance graph database.
Amazon Neptune is a fully managed graph database service.
Here I would want to try to use a graph database, Neo4j.