Draft:Huge Data
| Submission declined on 8 May 2024 by Grabup (talk). This submission's references do not show that the subject qualifies for a Wikipedia article—that is, they do not show significant coverage (not just passing mentions) about the subject in published, reliable, secondary sources that are independent of the subject (see the guidelines on the notability of websites). Before any resubmission, additional references meeting these criteria should be added (see technical help and learn about mistakes to avoid when addressing this issue). If no additional references exist, the subject is not suitable for Wikipedia.
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
|  |
Comment: Independent reliable sources needed to establish notablity. Grabup (talk) 02:41, 8 May 2024 (UTC)
Huge Data
Huge Data is a novel new technology that allows access to very large amounts of data; a working example of this is quickly accessing a puzzle pattern (in less than 1/100 of a second) from a data set that has 32,009,658,644,406,818,986,777,955,348,250,624 different puzzle patterns. The data set is greater than 32 Decillion (32 X 10^33) that represents more data than what is on all Hard Disk on Earth and that includes every datacenter. This Technology uses a Patent Pending Puzzle 63/470,384 similar to the Rubik's Cube and a Trade Secret Algorithm and Dataset Technology that can retrieve any of the selected puzzle based on the minimum amounts of moves to solve it; the number of puzzles in each number of moves is known and selectable from the spectrum of moves. See how Qubik at https://qubik.ai is using this technology to Train, Benchmark, and Evaluate the Reliability of AI (Artificial Intelligence) with perfect unbiased data ideal for machine learning and benchmarking.