OMNITY; A SELF-ASSEMBLING, SELF-HEALING, KNOWLEDGE CURATION AND DISCOVERY.
INTRODUCTION TO OMNITY.
Through knowledge which exist in several forms, different means of aggregation and curation has become developed.
Despite being protected, the access to obtain knowledge has remained a constantly increasing challenge. However, from the year 2010, there has constantly been a great level of growth of document-based knowledge.
Omnity has been able to fuse several technologies and disciplines together, it ranges from the processing of natural language and graph mathematics to machine learning as this will help to create a highly scalable curation of knowledge as well as discovery system.
The primary contribution of Omnity has been in the development of an integrated system which enables the self-assembly of documents that is driven through an iterative fusion of natural language processing, the learning of machines, graph-based mathematics, document quality control and the storage of hyper-decentralized document are enabled by the blockchain.
Omnity happens to be a self-assembling, and a decentralized knowledge curation system that interconnects private document repositories at every scale.
THE EFFECT OF ‘’OMNITY’’.
Every knowledge seeker continues to grapple with the way to make most of the insights that can be gleaned from, as well as during the period of drowning in data with a limited attention and time.
OMNITY OFFERS A DECENTRALIZED PROTECTION OF STORED CONTENT.
The ingestion of omnity and its interconnection will be protected in any of different decentralized networks which includes: less centralized storage providers.
Omnity usually will follow three fundamental steps throughout the period of document harvesting, interconnecting and replication processes. The steps include: To collect, to connect an as well to protect every steps mentioned.
THE FRAMEWORK OF DOCUMENT PROCESSING. (The problem with collection)
The ability of a man to remain current in any knowledge domain has become impossible has there is an exponential growth of knowledge that has resulted in disconnection and fragmentation.
It has been discovered that the attorneys create over 70 documents each day and this is equivalent to having an average of 26,000 documents every year.
Every day, over 10,000 scientific journal articles are published.
PROVIDED SOLUTION FOR COLLECTION BY OMNITY.
Omnity has to do with collecting, connecting and also protecting the world’s public documents that exist across different documents, languages, place and time.
The document ingestion strategy of Omnity has first focused on high-quality, third-party vetted U.S federal content such as the one that is available from the U.S department of commerce (D.O.C).
THE PROBLEM WITH CONNECTING.
There happens to be too much information for manual processes.
There is limit of social or tribal knowledge curation.
There is an overly sparse citation connectivity that impedes document discovery through systematic automation.
There is a training static machine learning based models that exist in an era of an exponential knowledge growth.
THE CONNECTION SOLUTION PROVIDED BY OMNITY.
Omnity has spent more than four years in the development, optimization and the commercializing of a means to interconnect documents with shared meaning even during the period when those documents do not reference one another.
THE PROBLEM WITH PROTECTION.
Knowledge happens to be a thing that is fragile, that is susceptible to destruction through a lot of means and that includes: natural disaster, war and some political whim.
The best method of backing up resilience of knowledge is through redundant and encrypted networks backed up in many locations of the world over time.
SOLUTION TO PROTECTION ISSUES OFFERED BY OMNITY.
Omnity protection will see to the components of insight graphs which include: document-nodes and semantic inter-document links that are identified as a replicable unit of an overall omnity network.
GLOBAL SYSTEM ARCHITECTURE.
Omnity achieves balance through the presence of a distributed storage coordinator (DSC). This DSC optimally stores data on a highly available, resilient and self-healing storage network.
The DSC has the ability to employ advanced heuristics that will be able to calculate costs that are associated with every storage transaction and they include:
• The omnity storage index (OSI).
• The omnity distributed storage network (DSN).
The omnity storage monitor.
The omnity file handler.
The omnity data recovery agent (DRA).
The omnity storage strategy selector (SSS).
The omnity storage cost estimation engine (SCEE).
The omnity storage cost scanner (SCS).
• The distributed storage systems bus (SSB).
• A hyper distributed storage network (HDSN).
• A cloud storage networks (CSNs).
THE RELEVANCE OF OMNITY TOKENS TO OMNITY TOKEN HOLDERS.
• An omnity token can be used to ingest into Omnity, it can also be used to query on any number of document or set of documents.
One omnity token will enable a 20-page document to be queried. Token users will be able to ingest and store as many documents as they desire.
• An Om token can be used to trigger an automated production of an information feed.
• There is a customized report and information feeds that is according to company-centric workflow.
CHARACTERISTICS OF OMNITY (OM) TOKENS.
Omnity is made up of two networks that are interwoven and synergistically interacting. These networks are:
- A document network which only represents a relatively small fraction of globally available public and private content.
- A user network that is comprised of fortune of 1000 enterprise customers.
During the ICO period of Omnity, tokens will be issued to buyers. The purchased tokens can be launched in the discovery bots for sustained query activities.
After a successful ICO, a portion of the proceeds will be used to develop a program for Omnity early adopters.
The Omnity network has three drivers and they include:
(i). An increase in the scale of the document network.
(ii). An increase in the scale of social network for omnity.
(iii). Mapping of the semantic space of the Omnity network based on a user affinity for a certain search result. - An increase in the scale of the document network.
- Increase in the scale of user network.
- A semantic mapping that will highlight interesting content that exist within a search-response space.
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Authored by: geeboy88
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