Platform Engines — The Machine Room of DACONOMY
Behind the scenes of DACONOMY’s decentralized data ecosystem are several platform engines, also known as the machine room of DACONOMY. These engines are driven by artificial intelligence and were developed to solve some of the major problems that the data industry faces today, such as data analysis, data anonymization, and data pricing. In this post, we’ll cover the high-powered engines being used to change the data landscape as we know it.
DACONOMY — Analytics Engine
At DACONOMY, one of our primary goals is to ensure the growth of our ecosystem; clustering data types and markets partially do this in a way that meets and caters to demand. At the forefront of these efforts is our analytics engine. Once someone has put their data on DACONOMY’s platform, it’s immediately run through the analytics engine. Since we don’t own or store the data, once it comes into our engine we extract all the information needed to make the data marketable. The analytics engine does not only identify data type, it also curates data, cleans data, and ensures it’s neither broken nor corrupted.
The DACONOMY Data Anonymity Engine
A critical aspect of the DACONOMY marketplace is its ability to handle data in an anonymous manner. A buyer can specify the structured items that they are interested in, and the DACONOMY marketplace will identify matches for that query as a trustee of the respective seller. To facilitate this process, the anonymity engine allows dataset owners to apply a certain level of anonymity to their datasets based on pre-set features determined by DACONOMY or their personal criteria; this ensures that the only information shared by data owners is the information that they’ve chosen. Datasets making use of the anonymity engine will typically not be exchanged between seller and buyer directly, but rather through DACONOMY marketplace smart contracts acting as both the orchestrator of similar data elements and the broker acting on behalf of a group of data sellers.
The DACONOMY Classification and Taxonomy Algorithms
The classification and taxonomy algorithms are a core part of the DACONOMY architecture, and one of the primary interfaces data provision has to undergo. These algorithms, apart from indexing all the data, enable DACONOMY to autonomously classify the data to make it fit in the most appropriate taxonomic class(es). Once the classification, indexing, and taxonomic model are assigned, a sample of the structured data is stored for matching buyers and sellers.
The DACONOMY Verification and Validation Engine
The DACONOMY verification and validation engine will verify users during registration and provide different verification levels based on deals successfully executed, verification data provided, recommendation data from peers, and KYC profiles. On the data side, the verification and validation engine will validate the data sets and assign a quality indicator based on the AI-based validation checks, grading consistency, statistical significance, successful checks against reference data, update cycles, and more. Furthermore, the verification and validation engine allows the DACONOMY marketplace to verify data sources in terms of their legal correctness and the user rights of the seller.
The DACONOMY Matching Engine
Finding appropriate, relevant data easily and quickly is a core strength of the DACONOMY platform. With state-of-the-art data-searching algorithms on structured data sets, the platform will ensure that it presents fit-for-purpose results. Also, to improve the efficiency and speed of relevant data matching, the engine will not only return results, but will also include an easy to digest graphical representation of the data sets found consisting of heat maps, bar diagrams, and other data visualization tools; this will ensure a clear understanding of the data that is being presented.
The DACONOMY Pricing Engine
The pricing engine calculates a value for any given dataset or data stream. It uses actual prices paid for data exchanged over the platform to refine the pricing function from deal to deal. The price can vary with certain parameters of the dataset, level of depth provided (see also precision levels in Anonymity Engine above), update cycles (none, weekly, daily, hourly), and right of use (royalty, one-time use, use and burn, etc.). In addition, the owner of a dataset can either override any price proposal calculated by the DACONOMY platform or select “auction” as the pricing algorithm. The auction algorithm advertises datasets for a given period and collects offers from registered buyers. The seller can then decide to market the data, for example, to the ten highest bidders in the auction process or determine any other distribution mechanism based on the prices bid by the interested parties.
The beauty in our engines is that they are far from complete. Since they’re developed using advanced AI, the engines will grow as the ecosystem does; we will gather more data, host more interactions, and lead the data economy.
