Unification was founded on a bold idea: The key to unlocking a brighter future for humanity lies in improving the way we manage data on a global scale.

As it stands, much of the world’s data is locked up in closely guarded silos. It is difficult for developers and researchers to access the data they need to create ground-breaking innovations or conduct scientific research.

At Unification, we believe the answer to these challenges can be found in data standardization. If data sets are standardized into a unified format, they will be able to be correlated against each other automatically. Processing data within a single unified format promises to reveal patterns that have gone unnoticed by the naked eye, producing breakthroughs in research and functionality.

To learn more about our vision of a world governed by unified data, and to understand the ripple effects of this technology, read our three-part series, “Why Unified Data is Inevitable.”

Technical processes of data standardization

To make data standardization a reality, at Unification we are building a predictive modeling tool that utilizes deep neural network algorithms to map Metadata Schemas (currently in JSON format) to the underlying data sources.

The machine-learning algorithm is utilized for multiple purposes, including:

Dynamic Metadata Schema and Data Sources Mapping Unification ID and Provider’s native user ID mapping Merging data from multiple providers Filtering data based on End User permission and Data Consumer’s data request requirements

At Unification, we rely on the machine-learning algorithm to standardize data internally, as well as to generate predictive intent for Autonomous Machine Learnable Smart Contracts (AMLSC).

The same algorithm is also used to identify the Unification user ID, which corresponds to the real user ID in the ecosystems. This ensures the automation of data standardization and elimination of any stochastic process in bringing the relevant datasets to the corresponding users.

In order to address the complications of the wide range of data storage implementations our adopters may potentially be utilizing, we provide a data-export solution that flattens out the data into a block processable by machine learning algorithms. This enables the HAIKU Server Nodes ETL component to support a number of structured and unstructured data sources out of the box, with more being implemented as the ecosystem evolves.

Overall Architecture of the Machine Learning Algorithm Implementation

In order to address the complications of the wide range of data storage implementations, our adopters may potentially be utilizing, we provide a data-export solution that flattens out the data into processable block by machine-learning algorithms.

The machine-learning algorithm processes the block and produces a data package in a single, unified format. This is accomplished using input parameters supplied by the data producer, indicating which fields hold importance. It also allows the Unification ecosystem to ultimately be data-source agnostic.

The custom-designed algorithm is responsible for how the data requests are processed, what the data sources are (valid/invalid), how to efficiently map the publicly visible data to their corresponding data source(s) and filtering based on user permissions or other parameters. The algorithm alleviates the issues when there are different data providers with various data configurations, such as size of the data, class imbalance, whether the data is structured or unstructured, what type of database search is needed, etc.

Flexibility components are considered when designing the model in such a way that developers can fine-tune the corresponding parameters, without any machine learning knowledge, to systematically fit their data requirements. The efficiency of using a machine-learning algorithm resides in the capability of handling huge number of datasets without any prior hardcoding, so they can be dynamically modified by developers.

The architecture of the meta algorithm contains sub-algorithms (modules) for different purposes, which later can be used as a stacked predictive tool. JSON Schema is mapped to the underlying sources. Next, the mapping and Schema are used to filter user permissions, or any additional requirement mentioned in the query.

The mapping between Unification IDs and real User IDs is also done by the machine-learning algorithm. Therefore, a meta learner is designed that performs as a stacked algorithm (one neural network on top of another neural network on top of a random forest etc.)

Mapping of native Users’ ID to Unification’s ID Neural Network Architecture

Introducing Autonomous Machine Learnable Smart Contracts (AMLSC)

Our trained algorithm can be used as a dynamic component to do search grid in a permissions table, as well as to dynamically update the smart contract based on certain conditions — what we term Autonomous Machine Learnable Smart Contracts (AMLSC).

As of now, business conditions for smart contracts are static but with the implementation of this algorithm, future modifications can be automatically performed. Additionally queries can be done much faster with such algorithm.

Autonomous Machine Learnable Smart Contracts (AMLSC) are the next generation of Smart Contracts, enabling autonomy with consideration of the intent of all contract participants to find the most efficient match and create the highest economic value for all parties.

In parallel, this technology also enables autonomous matching algorithms — which already exist in centralized markets, such as financial exchanges — to be used any business. This is of particular interest to businesses who rely on sharing and service economies.

Stay tuned for a deeper explanation of the architecture behind Autonomous Machine Learnable Smart Contracts.