If organizations could only augment blockchain’s strengths—its immutability, security, and decentralization—while addressing its latency and scalability issues, it could become the vaunted enterprise tool it was initially intended. That day will soon come courtesy of Directed Acyclic Graphs (DAGs).
blockchain’s premise is straightforward, utilitarian, and more lucrative than that of any other new technology to recently emerge. This distributed ledger system promises near real-time updates of transactions between remote parties for trustworthy, impenetrable peer-to-peer networks, eliminating the need (and expense) of middlemen.
Unfortunately, realizing that promise has proved decidedly difficult, especially in the cryptocurrency world in which blockchain has struggled with issues of scale. Due to its linear nature, the larger a particular blockchain becomes, the longer it takes to validate transactions. Notable cryptocurrencies have experienced inordinate delays verifying transactions,resulting in unwieldy fees and deflated expectations.
That day will soon come courtesy of Directed Acyclic Graphs (DAG). Their non-linear approach ensures that the larger their networks grow, the quicker they validate transactions.
Moreover, by endowing them with triple-attribute permissions and uniform semantic standards, they become even more trustworthy and globally interoperable. Confining their resources to private networks renders them the most capable platform for smart contracts and instant, distributed transactions between partners.
The cardinal means by which acyclic semantic graphs redress blockchain’s latency is in expediting the validation process. Blockchain requires each of its previous transactions to validate new transactions. Furthermore, this process doesn’t begin for one transaction until the one before it’s completed.
New transactions in acyclic graphs require validation from only two other transactions to ensure the trust of the first to the present one. Thus, the deployment of multi-master acyclical semantic graph databases enables parties in decentralized locations to readily verify transactions in which the latest contains the entire history of all parties.
These decentralized databases are synced together to describe all facets of a smart contract, for example, or the history of transactions between participants in the network—including the last transaction.
Since they’re widely perceived as acyclical in nature, semantic graphs are built for such use cases. Additionally, by deploying them in decentralized networks between known partners (such as supply chain networks, or in corporate IoT implementations), they’re not susceptible to a 34 percent attack in which one party has more than 34 percent of the network’s compute power and falsifies transactions. By using these graphs in private (not public) settings, parties can readily see any trust breaches.
