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GraphLab Raises $6.75M For Data Analysis Used In Consumer Recommendation Services

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GraphLab, the open-source distributed database, has received $6.75 million from Madrona Venture Group and NEA for its machine learning technology used to analyze data graphs for recommendation engines.

Developed five years ago at Carnegie Mellon University, the open-source data analysis platform takes semi-structured data that describe relationships between people, web traffic, product purchases and other data. It then analyzes that data for services to provide online recommendations.

6n-grafGraph databases, similar to Graphlab, have increased in use as more data needs correlating to better understand its meaning. Wikiepdia describes graph database in the context of graph theory. It applies mathematical structures “used to model pairwise relations between objects. A graph in this context is made up of vertices or nodes and lines called edges that connect them.”

It’s the ability to make the connections between billions of nodes and lines that forms the basis for making recommendations.

Dr. Carlos Guestrin is GraphLab’s CEO. He is the Amazon Professor of Machine Learning at the University of Washington, who developed the technology at Carnegie Mellon. He says it is GraphLab’s flexibility and better machine learning capabilities that makes it better in comparison to other data analytics tools such as Mahout, the open-source machine learning technology.

GraphLab has gained adoption in the market. It is used by a number of consumer services to drive millions of transactions. Pandora and Walmart Labs are cited as users of the technology.

(Graph image courtesy of Wikipedia)