high_scalability high_scalability-2009 high_scalability-2009-628 knowledge-graph by maker-knowledge-mining

628 high scalability-2009-06-13-Neo4j - a Graph Database that Kicks Buttox


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Introduction: Update: Social networks in the database: using a graph database . A nice post on representing, traversing, and performing other common social network operations using a graph database. If you are Digg or LinkedIn you can build your own speedy graph database to represent your complex social network relationships. For those of more modest means Neo4j , a graph database, is a good alternative. A graph is a collection nodes (things) and edges (relationships) that connect pairs of nodes. Slap properties (key-value pairs) on nodes and relationships and you have a surprisingly powerful way to represent most anything you can think of. In a graph database "relationships are first-class citizens. They connect two nodes and both nodes and relationships can hold an arbitrary amount of key-value pairs. So you can look at a graph database as a key-value store, with full support for relationships." A graph looks something like: For more lovely examples take a look at the Graph Image Gal


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3 If you are Digg or LinkedIn you can build your own speedy graph database to represent your complex social network relationships. [sent-3, score-0.958]

4 For those of more modest means Neo4j , a graph database, is a good alternative. [sent-4, score-0.591]

5 A graph is a collection nodes (things) and edges (relationships) that connect pairs of nodes. [sent-5, score-0.905]

6 Slap properties (key-value pairs) on nodes and relationships and you have a surprisingly powerful way to represent most anything you can think of. [sent-6, score-0.545]

7 They connect two nodes and both nodes and relationships can hold an arbitrary amount of key-value pairs. [sent-8, score-0.605]

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9 " A graph looks something like: For more lovely examples take a look at the Graph Image Gallery . [sent-10, score-0.652]

10 Here's a good summary by Emil Eifrem, founder of the Neo4j, making the case for why graph databases rule: Most applications today handle data that is deeply associative, i. [sent-11, score-0.719]

11 The most obvious example of this is social networking sites, but even tagging systems, content management systems and wikis deal with inherently hierarchical or graph-shaped data. [sent-14, score-0.378]

12 In essence, each traversal along a link in a graph is a join, and joins are known to be very expensive. [sent-16, score-0.791]

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20 The drawback is that for huge data amounts (>1Billion nodes) the clustering and partitioning of the graph becomes non-trivial, which is one of the areas we are working on. [sent-69, score-0.724]


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