With better scaling, semantic technology knocks on enterprise’s door

With better scaling

Behind the newly surging interest in graph data is a rebirth of semantic technology. Semantic approaches improved upon relational methods for data analytics, but they have also had to overcome hurdles. To get a better view on graph developments, SearchDataManagement caught up with Sean Martin, CTO at Cambridge Semantics, who is among those at the center of semantic technology advances. After numerous years and adventures in technology skunkworks-style undertakings at IBM, Martin founded Cambridge Semantics in 2007 to further knowledge graphs and semantic technology in the enterprise.

Scalability, he said, has been a challenge he has worked consistently to meet. Released last year, his firm's Anzo Smart Data Lake is based on an in-memory massively parallel processing (MPP) graph database engine. The product was obtained in 2015, along with Cambridge Semantic's purchase of SPARQL City, whose principals included the top technologists behind MPP pioneers Netezza and ParAccel. At the heart of Anzo Smart Data Lake is support for Resource Description Framework/SPARQL standards for data storing and querying.

Graphs and semantic technology have traveled a long road, but things seem to be coalescing lately. Is that true?

Sean Martin: Well, semantics standards came out 15 or more years ago, but scalability has been an inhibitor. Now, the graph technology has taken off. Most of what people have been looking at it for is [online transactional processing]. Our focus has been on [online analytical processing] -- using graph technology for analytics.

What held graph technology back from doing analytics was the scaling problem. There was promise and hype over those years, but, at every turn, the scale just wasn't there. You could see amazing things in miniature, but enterprises couldn't see them at scale. In effect, we have taken our query technology and applied MPP technology to it. Now, we are seeing tremendous scales of data.

From our point of view, the ability to take on data warehouse loads happened, really, this year. Now, we find we can implement complex data lakes, and graph is a big element of that. Still, we see some people using graph and others hedging their bets; using graph and also using Hadoop family software for analytics.

What does semantic technology bring to the party? What is it meant to improve upon?

You will see much richer representations of data. One of the issues people have is that the data representations they have been practically capable of, using the traditional tool sets, have been pretty limited. They are just not practical anymore. People can create very elaborate relational structures, but the more rich they get, and the more types of classes they represent, then the more difficult it gets to write queries that touch many of the different tables you need to create, and there [are] a lot of artifacts around how the data is actually stored. So, in effect, there is a practical limit on how easy it is to represent the data richly using the traditional tools.

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