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Making smarter connections with knowledge graphs

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The Hume Map feature allows for a geospatial presentation of graph data, and temporal analysis lets users select and visualize certain time ranges andidentify patterns not originally apparent in the graph. “We will also be having a strong focus on analytics in the coming year,” Negro remarked, “with tools that will make analyses easier, especially for those who are not experts in data science.”

User-friendly for developers

As active as the graph database market is, the technology is not pervasive enough to be familiar yet. “Developers have to learn new skills since the query languages are different,” said Jay Yu, vice president of product and innovation at TigerGraph, which offers a graph database product. “To help reduce the learning curve for SQL users, we developed a graph query language called GSQL that mimics SQL,” he continued. “Moreover, developers can create applications more easily because instead of selecting fields from tables, they select from a graph traversal path, which is much simpler. For example, a recommendation application can be written for our graph database using just 10 lines of code.”

TigerGraph is known for its ability to scale. “We have users in production who are analyzing 5 terabytes of data,” said Yu, “and in an industry benchmark [LDBC SNB-BI], we were the only vendor that could manage the 36-terabyte test.” For business continuity, TigerGraph provides cross-region failover that moves data to a different set of servers outside an area of natural disaster or technical failure.

In one large-scale application, TigerGraph is analyzing 5TB of data from 100 million XBox users. XBox wants to see how users interact with each other, and whether they chatted or “liked” each other’s comments. This use case is an ideal application for graph databases because of its focus on relationships. “We have a library of 55 graph algorithms in open source so developers do not have to start from scratch,” noted Yu. XBox uses the data to understand users’ interests in order to develop new products and upsell existing ones.

Other methods of making TigerGraph easier to use for developers include its Graph Studio, which assists developers in visually defining schema, mapping from a relational data model to a graph model, defining a query, analyzing social networks, and running queries. For end users, such as business analysts, TigerGraph partners with vendors to build industry solution kits. These are available across 10 different industries, including fraud detection, anti-money laundering, and Patient 360 for medical analyses and personalization. “These industry solutions, which run on top of TigerGraph’s database, reduce development to just a few weeks,” Yu observed, “and the resulting interface allows non-technical end users to explore the data.”

Managing supply chain complexity

Supply chain management has proved to be a productive application for graph databases. Essilor is a French company that makes ophthalmic products and operates a worldwide network of production plants, prescription laboratories, and distribution centers. The company supplies corrective lenses, glasses, and sunglasses to opticians and optical chains. It also markets to consumers via online retailers that Essilor owns and/or operates directly.

The portfolio of materials and products required for the manufacture of Essilor’s ophthalmic products is complex; the catalogs contain hundreds of thousands of variations of stock and finished lens products offered at more than 500 locations worldwide. In addition, Essilor has fabrication labs and branches in many countries. “To manage the internal supply chain and to control supply risks, we must be able to model complex product configurations in order to have visibility into our supply situation,” said Mel Yuson, director of enterprise architecture, Essilor AMERA.

Essilor tried a number of approaches, including third-party solutions, but these were unsuccessful because of the extensive customization required. An in-house system using relational database technology was not able to model the complex relationships of Essilor’s extensive product configurations. In a final effort to solve the problem, Essilor decided to develop a semantic knowledge graph based on AllegroGraph from Franz, Inc. A staff engineer at Essilor had seen AllegroGraph at a trade show a few years earlier and recommended that the company explore this option.

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