Graph analytics: A primer
One of the advantages of social media is it gives organizations a way to get messages out to customers and receive communications back.
But social also allows enterprises to figure out connections between people.
The tool for this is graph analytics. Bill Schmarzo of ECM just finished a two-part piece on graphs and graph databases which readers will find very informative.
As he notes, graphs provide a way of organizing data to highlight relationships between people — or, if you want devices — on or across a network.
With complex analytical techniques you can identify the central influencer in a social network, or complex patterns of behavior indicative of attrition, advocacy, and/or fraud.
According to 451 Group, graph database companies include Neo Technology’s Neo4j, Objectivity Inc.’s InifineGraph, YarcData’s Urika, IBM’s NoSQL graph store for DB2, Oracle Spatial and Graph option for Oracle Database and Teradata’s graph analytics engine for its Aster Discovery Platform.
A graph database uses graph structures with nodes (in this case individuals), edges (lines that connect nodes), and properties to represent and store data, he writes. By comparison with with relational databases, graph databases are often faster for associative data sets, and map more directly to the structure of object-oriented applications, Schmarzo says.
Graph analytics is used widely, and not just probing customer data in social media. Government intelligent agencies can use it to identify threats through patterns of relationships and group communications in social media, email, texting and call detail records.
Scharzo says it’s being used in healthcare, manufacturing, energy, gas exploration, travel, biology, conservation, computer chip design, chemistry, physics, higher education research and other sectors analyzing relationships as well as metadata.
Graph analytics is a great compliment to Hadoop, he adds.
Read the more article.
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