DATAEUM; Data Generation Redefined

DATAEUM; Data Generation Redefined

Much of the research in advanced manufacturing involves the creation of models and simulation-based experimentation. Simulation leads to significantly faster results than physical experiment that require use of real machine tools and materials in a physical shop floor. Such simulations and models require data for carrying out those experiments. These data can be representative of a large number of sources such as machine tools, robots, suppliers, etc. the data types are also quite varied such as material density and strength, machine tool wear and energy usage.

The concept of simulated data has also been referred to in multiple ways in the literature. Many people use terms such as artificial data, generated data, fake data, and synthetic data to all mean the same thing. In contrast, people denote data generated by physical machines as real data, physically-generated data, or live data but it will be used interchangeably here.
A recent review of the literature related to synthetic data identifies use in multiple fields including economics, urban planning, transportation planning, cybersecurity, weather forecasting, and bioinformatics.

Challenges of Physical data generation

There are many reasons why it can be difficult or impossible to get sufficient real data both by type and category: the process can be too complex and not appropriate for a non- tech inclined people. Information is many times invisible, inaccessible or not even available, not to mention the high cost of operation and analysis. Also, there are no concrete rewards for users and the fact that content is sometimes not accessible, can be discouraging and disruptive.
There are many other challenges owing to the very nature of physical data itself and these include the fact that over 56% of physical stores do not optimize for local SEO, meaning that information is not accessible and not brought up to date in online listings. Business hours and phone numbers change regularly. Then, there is the issue of ever-expanding urbanization of developing countries. Data may be formatted inadequately leaving ambiguity and data may be suspect – modified without documentation or unknown.

The solution with Dataeum

The solution involves the use of crowdsourcing – the practice of obtaining information or input into a task or project by enlisting the services of a large number of people, either paid or unpaid, typically via the internet.

It will provide incentives or rewards for those generate data and the data will be obtainable for use in a decentralized marketplace, depending on the various needs of actors in the market. The solution also allows for a transparent, secure and a free flow of data from the people who gather them to the users.

A marketplace is being initiated with the Dataeum token “XDT” which will make exchange from collectors to data procurers possible. The leveraging of the blockchain technology will ensure transparency of the data generating process, guarantee its correctness.

Crowdsourced Data Generation

Crowsourcing data collection consists in building data sets with the help of a large group of people. There are a source and data suppliers who are willing to enrich the data with relevant, missing, or new information.
This method of gathering data has great potentials that remain largely untapped, it is the way to access physical data from any location and with wide coverage. Dataeum will tap into this technology by having data collected through a mobile application used by a community of collectors who will be reward for their effort.

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