Excerpt of article published on IDG Connect.
Machine learning (ML) is getting a lot of attention at the moment. This is partly because a slew of new companies are emerging which are using it in innovative ways. And partly because it can get easily subsumed into the fuss and furore about AI and the rise of evil robot intelligence. Graph technology, on the other hand, is something which takes more of a back seat and yet, in a lot of ways, also sits at the forefront of the big data and analytics movement.
Jim Webber, chief scientist at Neo Technology – which describes itself as “the world’s leading graph database” – adds: “Machine learning is about analysing data to ‘learn’ a model or using an algorithm that can be applied to make predictions on new data sets. Machine learning is not tied to a particular representation of data.”
“Machine learning algorithms help data scientists discover meaning in data sets, and these insights can be expressed as relationships between nodes in a graph. Graph databases enable efficient storage and traversal of information about relationships. Therefore, graph data can either be the input or the output of machine learning processing.”
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Machine learning (ML) is getting a lot of attention at the moment. This is partly because a slew of new companies are emerging which are using it in innovative ways. And partly because it can get easily subsumed into the fuss and furore about AI and the rise of evil robot intelligence. Graph technology, on the other hand, is something which takes more of a back seat and yet, in a lot of ways, also sits at the forefront of the big data and analytics movement.
Jim Webber, chief scientist at Neo Technology – which describes itself as “the world’s leading graph database” – adds: “Machine learning is about analysing data to ‘learn’ a model or using an algorithm that can be applied to make predictions on new data sets. Machine learning is not tied to a particular representation of data.”
“Machine learning algorithms help data scientists discover meaning in data sets, and these insights can be expressed as relationships between nodes in a graph. Graph databases enable efficient storage and traversal of information about relationships. Therefore, graph data can either be the input or the output of machine learning processing.”
Read the full article →
Keywords: graph-based machine learning • IDG Connect • news