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Poultry and Coins: OriginTrail Knowledge Graph Explained Using the Binance Case

Towards an inclusive knowledge economy: How can enriched datasets enable better insights into the assets traced on Origintrail Decentralised Network?

OriginTrail’s graph data layer provides the best foundation for tracing any asset, be it physical or digital. After hearing that more than $40 million worth of bitcoin was stolen from Binance in this year’s biggest hack so far, we started thinking about how the OriginTrail protocol and the OriginTrail Decentralized Network (ODN) could help trace and discourage incidents like this? We presented our first insights last month.

We have received amazing feedback. The use case showcased the true versatility of the ODN, proving it can support tracing any asset, not just physical products. The ODN can handle both food provenance, as it is doing for a premium poultry producer in Slovenia, and digital assets, like the hacked bitcoins. Traceability of financial flows could be applied to anti-money-laundering (AML) solutions, to analyze and point to suspicious trading behavior, measure the performance of any public blockchain with the highest level of credibility, and much more.

At the heart of this use case is a knowledge graph, a powerful feature of the OriginTrail protocol, which expands its usability beyond supply chains. By supporting the interoperability of various data sources and connecting their inputs in the form of a graph, ODN is used to combine any relevant information about a particular asset (physical or digital). In the case of supply chains, this information can be tied to compliance, quality, certification or any other additional data. In the case of digital crypto assets, similar information can be related to wallet ownership or red flags about involvement in illegal activities, etc. It also allows considering a multitude of wallets as a single pool — useful for analysis that wants to disregard “internal” transactions between some wallets. By expanding the data sources about either supply chains or crypto assets, we are also expanding the size of the knowledge graph about it. ODN, being an L2 data scaling layer on top of Ethereum, also ensures the integrity (immutability) of each such published dataset and integrity of the graph as a whole.

Track and Trace — A Visualisation from the ODN Data Layer

Let’s take a deeper look at how the same technology, the OriginTrail protocol, can track both poultry and bitcoin and why it enables more insights than the traceability enabled by the traditional blockchain explorers.

Traceability of transactions is a core feature of most blockchains, but it is hard to get the whole picture of complex flows of currency by directly using data from raw transactions. The OriginTrail graph provides great overview of complex and interconnected data that can be further enriched with information from multiple sources leveraging the self-governed ODN based on Ethereum.

For example, connecting technical data about the stops in the supply chains with additional data about the farmers, the conditions of the premium poultry breeding, and the certifications connected to the process, provides consumers with a rich user experience and valuable information for making their decision.

On a similar note, joining the data from the exchanges to the trail of blockchain transactions of stolen bitcoin provides more context, making it easier to understand the hack and harder for the hackers to spend the funds.

You can see how the same logic is applied in these examples of the Track and Trace tool as available in the nOS. The Track and Trace application enables users to easily track both physical and digital assets along their value chain and display data for analysis, efficient management, alert systems, and more.

Track and Trace example for poultry
Track and Trace example for bitcoins

>> Explore the Binance dataset on the OriginTrail Network Explorer

The Power of the OriginTrail Data Layer

The data layer of the OriginTrail Decentralized Network utilizes graph data structures that make it possible to add arbitrary additional data to a specific dataset. In this way, more context is provided and the initial dataset is enriched to form a growing knowledge graph.

The data for this implementation is structured in a standardized GS1 EPCIS form, which is widely used in the supply chain industry and is applicable to both physical and digital objects. We extracted data from bitcoin transactions following the flow of bitcoin from two Binance wallets affected by the hack. Data was then transformed into the GS1 EPCIS form, containing transformation and object events that represent those transactions. It resulted in GS1 EPCIS XML documents as datasets that we uploaded to the OriginTrail node and replicated on the OriginTrail Decentralized Network (mainnet). As the data is uploaded to the OriginTrail node, it is transformed and connected into a large knowledge graph, suitable for observation.

Enriched data can also be visualized with other tools. For the following visualisation, we used ArangoDB. The GS1 EPCIS form is used to construct the graph of transactions. We can observe the path of coins from affected wallets through transactions. The transactions are represented as GS1 EPCIS events and wallets are represented as Business Locations.

When the data is imported into the graph database, the imported graph contains paths of bitcoins through transactions between wallets. This image shows part of one such path: a transaction of some of the hacked bitcoin from one wallet to another, and then a transaction from the second wallet to two additional ones. “AT” arrows represent the block number that the transfer happened in.
Image 2 shows the same data as in Image 1 in GS1 EPCIS XML document which was used to create wallet vertices of the knowledge graph.

Trusted Data at Scale with OriginTrail and Ethereum

An important aspect of the grand vision of the decentralized Web 3.0 is the ability to interact with credible, trusted real world data within smart contracts. This widely discussed issue, dubbed the “Oracle problem,” states that a smart contract assumes trusted data has been input into it, otherwise the trustless nature of smart contracts could be questioned — the system is “garbage-in, garbage-out.” Therefore, the integrity of the original input data is key, which is why decentralization-enabled data immutability is not enough on its own.

The power of the trusted knowledge graph, as the core of the data layer of the OriginTrail Decentralized Network, lies directly in the ability to additionally validate real world data. The graph form gives data important context, allowing cross-checking (with the consensus check feature) on a very granular level of published data from multiple parties at once. The decentralized network is itself designed for storing large amounts of data at low cost, and making it tamper proof with blockchain fingerprinting allows for an organically growing globally shared knowledge graph to be an excellent L2 solution for data scalability in the Ethereum ecosystem. In that sense, the OriginTrail network can be observed as a trusted, linked data oracle with consensus check capabilities that extends the power of Ethereum. As the graph grows and the data sharing economy accelerates, the synergistic nature of all data in the graph ultimately leads to knowledge that can be trusted with certain assumptions (i.e. several identifiable parties making the same claim) and utilized directly for a vision of Web 3.0.

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OriginTrail is the world’s first Decentralized Knowledge Graph, organizing humanity’s most important assets, and making them discoverable, verifiable and valuable.

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The World’s First Decentralized Knowledge Graph

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