Case Studies
Within this lesson you will OriginTrail and it's Decentralized Knowledge Graph in action!
*SCAN - The Supplier Compliance Audit Network (SCAN) is an association of importers that was formed to eliminate foreign factory audit fatigue associated with Supply Chain Security importing criteria within the US Customs Trade Partnership Against Terrorism program (CTPAT). SCAN importing members have combined annual sales of over USD 1.36 trillion and source from factories around the world. Today, SCAN has more than 21,300 factories in its database, with several hundred new audits conducted monthly. All of the audits are secured utilizing the OriginTrail Decentralized Network and comply with SCAN’s rigorous data privacy requirements. With flexible data permissions, the solution enables SCAN to share data with government agencies such as CTPAT.
SBB - A network is only as strong as its weakest link. In railway systems, rail parts need regular maintenance and control to ensure safety for travelers and cargo. However, in large systems with multiple service providers and vehicles constantly in motion, special attention is needed. SBB, the Swiss national rail company, is looking to move beyond the state of the art to ensure real-time availability of quality traceability information for individual parts involved in their systems to deliver one of the world's most reliable rail journeys.
What is OriginTrail?
OriginTrail links together two powerful technologies — blockchains, most widely known to underpin cryptocurrencies, and knowledge graphs, the intelligent data management component driving data platforms like Google, Amazon and Facebook. After launching in early 2019, the OriginTrail Decentralized Knowledge Graph supported by an open network of over 2000 nodes hosted by both individuals and businesses globally, has seen its initial use in enabling discoverability and verifiability of real world assets in supply chains.
What is a knowledge graph?
There are many definitions of knowledge graphs (KGs), all slightly different. Without emphasizing on precision, all of them explain knowledge graphs as networks of entities — physical & digital objects, events or concepts and relationships between them.
Key characteristics
- Focus on data connections as "first class citizens" (linked data)
- Designed to ingest data from multiple sources, usually in different formats
- Flexible data models, easily extendable
How Knowledge Graphs are used today
Today’s common knowledge graphs are deployed within the domain of one organization and are designed to capture data from various sources. KGs are used in Web2 by companies such as Amazon, Google, Uber, IBM and others for various applications - search, data integration, knowledge reasoning, recommendation engines, analytics, machine learning and AI etc. This generates enormous value and makes these organizations the biggest in the Web2 space.
Welcome to our online 101 course! We hope you’re ready for an engaging, rewarding online learning experience.