Upgrade your Analytics with a Knowledge Graph

Mar 5, 2021, 3 minute read
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Are you currently managing a digital product or product line? If so, you may already be experiencing the following growing pains:

  • Difficulty meeting customer needs due to rigidity of underlying data systems
  • Painful integration with third-party data sources wasting engineering time
  • Lack of interoperability with customer or partner datasets
  • Slow responsiveness to potential new market opportunities

Your new digital product infrastructure

Stardog has helped companies including Schneider Electric, Springer Nature and Ericsson to deliver new digital products with high service levels and user satisfaction. Free from dealing with cumbersome data repositories, these products are able to deliver innovative, user-friendly products that leverage previously untapped or underutilized data assets.

The key is that these companies are leveraging the same tech that brings you Google Search, Uber Eats, Amazon Alexa, Facebook and Linkedin — a knowledge graph. Knowledge graphs represent the direct and implied relationships between data, which opens up new fields of opportunities for monetization and performance improvements (See, What is a Knowledge Graph? for more detail). Knowledge graphs are ideal for highly connected datasets that are physically separated across various data silos.

Knowledge graphs create a network of information from data silos

Beyond that, Stardog solves the backend issues that so frequently slow down release cycles. Stardog’s reusable data modeling supports agility in adapting to new customer needs, since updates to the data model don’t involve any rework. In the words of Schneider Electric’s product lead:

You can build a SQL table and keep it all up to date if you want to do that; but by putting it in the graph, you’re not managing schemas, you’re not normalizing data — you’re saving time.

  • Mark Reid, Director of R&D, Cloud & Mobile Services, Digital Buildings, Schneider Electric

Furthermore, Stardog’s virtualization capability offers a scalable method of accessing data, whether it’s internal or external. Schneider Electric also leveraged virtualization for direct access to IoT data sources required for their building information management product suite.

Schneider Electric tracks hundreds of thousands of devices, controllers, and sensors from building management systems, IoT sensors, controls and other systems and software operating a building. A typical day results in at least 25 million events processed in real time.

Schneider Electric uses Stardog to map the relationships between building spaces and a broad set of data points. While the graph contains the relationships, the actual time series information is kept in other storage services. Stardog’s Virtual Graph capability allows the relationships to be tied to the time series data at query time enabling answers to questions like: “Give me temperature readings for the past month in this particular room on this floor.”

These type of multichained questions are enabled by the power of the knowledge graph, which links related concepts together in a network of information. Of course, Stardog is designed to be performant at scale and can handle large data volumes and even the most complex queries. This month’s 7.6.0 release improves writing data at database creation time by almost 100%, yielding a million triples per second loading speed using a commodity server. Last month’s 7.5.0 release brought 500% improvement to transactional write performance.

Want to learn more about upgrading your product or product portfolio to be backed by a knowledge graph? Contact us or Learn more about analytics modernization with Stardog.

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