Unification is a blockchain-based protocol, utilizing off-chain apps and on-chain Smart Contracts to create a transparent and standardized data marketplace and user sovereign identity center. Bringing users, apps, research institutions, and advertisers together in an open and free marketplace benefits everyone.
Users manage their data permissions and can be paid for sharing access. Small companies can readily buy and sell data in an open marketplace. Advertisers can cross-reference information for better data points, and research institutions can access large amounts of previously unavailable data.
How Unification Standardizes Data With Artificial Intelligence & Machine Learning
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.)
Unification’s Partners Benefit From Unified Data
HealthTech with SmartMat
SmartMat is an interactive yoga mat that reads the user’s position, form and other key fitness metrics. It records these values, forms a fitness profile of the user and makes predictive corrections to help the user improve their yoga and fitness practice.
Unification interacts with SmartMat and other health enterprises (both hardware/App and App only) through the following value adds:
- SmartMat integrates CAPSULE into its existing techstack
- Health and fitness data is transported into a standardized format and each end user is assigned a Unified Verifiable Credentials Identification (UVCID) which is cross compatible throughout the whole Unification ecosystem
- The data availability is hashed in MOTHER and announced in BABEL
- The data is automatically analyzed for useful combinations with other existing sources via our machine-learnable algorithm
- As any viable data partnerships appear, they are proposed to SmartMat (the enterprise) and its end users. The value of aggregated data sales/partnerships in anticipated to be an average of 5X of siloed data sales.
- This also allows the end user’s data to combine and interact with other fitness & health platforms to allow the user a unified view of their activities & profile
- End users make their fitness profile available to other data consumers who help them monetize (via advertising, reward systems with other partners based on activities), improve product experience (such as combining with other generic/medical information for a holistic profile analysis), or lower costs (for example, providing fitness information to insurance providers)
Services & Hospitality with CoinSparrow
CoinSparrow is a crypto-concierge marketplace that allows people to conveniently access a wide range of product and service providers (Sparrows) on demand, and pay with Ethereum safely through escrow-based smart contracts.
Unification interacts with CoinSparrow and other similar service provider applications through the following value adds:
- CoinSparrow integrates CAPSULE into its existing techstack
- CoinSparrow users are assigned a Unified Verifiable Credentials Identification (UVCID) which is cross compatible throughout the whole Unification ecosystem. (If a user has been assigned a UVCID through a previous application but hasn’t’ claimed their ID, Unification’s Machine-Learnable (ML) algorithm will automatically match/sync their existing accounts.)
- Given the additional layers of KYC/AML built into UVCID, Coinsparrow increases escrow rates once they claim and setup up their Unification accounts with minimal risk exposure as an enterprise or to it’s members
- CoinSparrow increases it’s crypto payment offerings beyond Ethereum through new currencies offered by Unification’s payment gateway
- CoinSparrow uses Unification’s data standardization to better match existing members with offers in it’s system
Example: Using Unification’s ML system CoinSparrow gains insights into the fact that their users who have purchased a plane ticket have expressed interest for entertainment but have not made a purchase
- CoinSparrow uses the BABEL network to lower customer acquisition costs by exposure (paid and organic) to new members who have affinity for services offered in the platform
Given the complementary nature of hospitality businesses and the amount of redundant inventory that is the norm in that industry, it’s possible for these businesses to build internal data transfers whereby remnant inventory of the hospitality industry is offered across partners to end users with the right assumed intent, determined by itinerary, GPS locations, and financial information. In this instance, user affiliations or interests (declared or assumed) based on the user’s profiles are incorporated to build the best offer for both them and businesses.
For Example: a golf course that has a cancelation or open inventory offers the excess inventory of a particular tee time. The golf course will initially offer that inventory to its internal customer base, but by building a larger exchange via a state channel with other hotels, airlines, car rentals agencies, etc, they also extend that offer to other users outside their immediate customer list who have previously registered interest or activity about golf, or who may display a higher spending pattern.
Social Networking with Blabber
Blabber is the world’s first tokenized and location-based social platform that lets user’s monetize their content and personal data.
Unification interacts with Blabber and other similar social enterprises through the following value adds:
- Blabber builds on top of Unification’s Unified Verifiable Credentials Identification (UVCID) system which is cross compatible throughout the whole Unification ecosystem and eliminates the need to develop a user system while providing additional layers of KYC/AML built into UVCID
- Blabber has instant access to all users across the Unification’s platforms who have claimed their UVCID, enabling fast and cost-efficient user acquisition through exposure in BABEL and with BABEL partners
- Blabber uses AMLSC to find best matches of content reward among partners
- Blabber allows users to manage their permissions control through BABEL, making it easy to share and control data with partners that bring the highest value to the enterprise and matter most to the end users
- Unification’s matching algorithms help the enterprise & end users find useful content through BABEL (Blabber also puts out larger requests for users to share content on BABEL)
- Unification’s network helps find larger sets of people in local communities which are essential to Blabbler’s expansion plans
- Blabbler uses Unification’s payment gateway to further empower their users to interact with their planned content and ad reward system using multiple cryptocurrencies
FoodTech with HintChain
HintChain is a decentralized platform that utilizes blockchain technology to provide food profiles, a set of personal food data, which is stored with in their current network of over 10.5 million users. They compensate contributors who build food profiles in their community with relevant rewards.
Unification interacts with HintChain and other FoodTech Enterprises through the following value adds:
- HintChain integrates CAPSULE into its existing techstack
- HintChain users are assigned a Unified Verifiable Credentials Identification (UVCID) which is cross compatible throughout the whole Unification ecosystem. (If a user has been assigned a UVCID through a previous application but hasn’t claimed their ID, Unification’s ML algorithm will automatically match/sync their existing accounts.)
- Given additional layers of information available on its users (medical, activity, GPS, habits, etc), HintChain has immediately increased the data available on it’s user profile via the other data attached to the user’s UVCID
- In cases where there are specific layers of data missing for specific engagements, HintChain queries BABEL/MOTHER for availability of that data or puts out their own custom request to gather it
- HintChain uses HAIKU standardization info combined with internal data to better server info (medical data, GPS data)
- HintChain uses Unification’s platform to establish state channels with other food partners
- BABEL tracks HintChain and matches with other partners who would offer beneficial details to their users
- BABEL matches HintChain users with other partners looking for users consuming ingredients tracked by HintChain
Nov. 17 First presentation of Unification concepts at a private dinner in Tokyo
Jan. 18 Backend/Blockchain Dev Team recruitment, begin conceptual architecture
Mar. 18 Testnet Development begins
Apr. 18 Public presentation of Unification concepts at the Tokenomx Conference
Apr. 18 Final BABEL conceptualization, production begins with front-end dev team
May. 18 Early Stage Angel Funding
Jun. 18 HAIKU MVP Deployed on GitHub
Sep. 18 Private Pre-Sale
Oct. 18 Public Crowdsale
Oct. 18 Token Generation Event
Oct. 18 HAIKU Testnet Deployed
Dec. 18 HAIKU Mainnet Deployed
Jan. 19 Enterprise Outreach Office
Hard Cap and Valuation
There will be 1 billion UND tokens and the project aims to raise 40,000 ETH for 25% of the token total. The remaining 75% will be distributed thus: 35% will be used for ecosystem development, 12% will go to the founding team, 10% will serve as the go-to-market budget, 8% will be reserved for future team members, 5% will be shared among the project’s advisors, and the final 5% will be distributed to the Unification community.
35,000 out of the 40,000 ETH hard cap will be raised during private sale, leaving only 5,000 ETH for the public sale. As a result, each person whitelisted for the project’s public sale has a max. contribution cap of 0.5 ETH. Additionally, there will be no token lock-ups for public sale investors.
On the other hand, there will be no bonuses for tokens sold during private sale and they are subject to this lock-up schedule: 50% released at the TGE, 25% after 3 months, and the final 25% after another 3 months.
Founding & future team tokens are vested for 3 years with quarterly releases — starting one quarter after the TGE. Advisor tokens will also be vested for a year with quarterly releases.
With all tokens in circulation, Unification’s valuation stands at $72M. Considering the lock-ups, during the TGE however, only 24% of the total tokens will be in circulation within the first 3 months, resulting in a circulating market cap of $17.3M.
Price Per Token
Since there are no bonuses for private sale investors, the price per token is the same for both the private sale and the public sale rounds.
UND private sale is going on at the moment and allocations are being sold to investors that offer strategic value to the project — allocation size varies with how much value each investor offers. Public sale — and token distribution — is scheduled for October 2018.
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