Making sense of big data with graph technology, machine learning

August 16, 2016 //By Jonathan Wilkins
Making sense of big data with graph technology, machine learning
The theory of six degrees of separation, first proposed in 1929, suggested that every individual in the world was connected to anyone else in no more than five links. Today, social networking tools and graph technology can accurately map and extract valuable insights from the relationships between various entities in a network. Networks can also be analysed by machine learning, a technique in which a computer can adapt its own algorithms.

Modern manufacturing equipment has been advancing rapidly; plants are filled with sensors to monitor equipment performance. The number of sensors that allow devices to connect to the internet is growing and so too is the volume and complexity of data available to plant managers. The collection, storage and analysis of this data is vital in unlocking the benefits big data can provide.

Graph databases
Traditionally, data has been stored in table-structured relational databases, but development in this field has led to the introduction of the next generation of relational databases, graph databases, a type of NoSQL database. In a graph database, information is stored and represented with nodes, edges and properties. Nodes represent individual entities, edges are lines that connect nodes to each other and properties represent information relevant to the nodes. Unlike relational databases, which form a square structure, graph databases are much more flexible.

Graph databases can be used to quickly access information and identify trends in large data sets, such as supply chain patterns, logistics and new business leads. The system is naturally adaptive, allowing new nodes to be easily added. The analysis can be done in real time to address problems in manufacturing.

Machine learning
Machine learning is a concept that has been around for many decades. In machine learning the computer doesn’t rely on rule-based programming, rather the algorithms can adapt and learn from the data. This means that manufacturers using this software don’t need to rely on the time and expense of dedicated data analysts to find patterns and make predictions. Companies like Amazon have also used cloud based machine learning to make warehouse logistics more efficient by being able to quickly and seamlessly adapt to changes in inventory demand at peak times and during seasonal highs and lows.

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