Graph Data, A Core Component of Big Data

Big data is composed of unstructured data and graph data.
Therefore, if graph data is missing in analysis, it leads to considerable data loss.
Graph analytics and machine learning is the only way to solve this problem.

A New Paradigm for A New Era

We are already in a world of big data.
However, how much do we understand about big data?

In the past, big data analytics referred to the processing of large amounts of data based on Hadoop, statistics, and machine learning and identifying distribution of data.

However, a large component of big data is graph data by nature. Unfortunately, graph data describes the “relationship” between data, something that cannot be processed using past analysis methods, and therefore a new approach is needed to arrive at meaningful insights.

Graph Analytics, as one of the Top 10 Data and Analytics Technology

In 2019, the global research and advisory company Gartner
named graph analytics as one of the top 10 Data and Analytics Technology.

Graph analytics and graph-based machine learning are the only ways to analyze graph data. It’s getting the spotlight and named by Gartner as the top 10 data and analytics technology in 2019.

NetMiner 365 features rich graph analytics algorithms and functions based on the network analysis software NetMiner.

Graph Analytics and Machine Learning

Graph Analytics can enhance the accuracy of data prediction and classification.

Graph analytics is not only the method of understanding graphs but also that of increasing the accuracy of machine learning predictions.

Extracting the “relationship” features from the graph structures inside big data and utilizing them in machine learning helps you to understand and predict data more precisely. Graph machine learning can also learn graph data and figure out relational features.

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