A FractalWeb.App Microsite

UnDeliverable Ballot Database – Why Bad Data Is Really Good!

During our year with Mike Lindell, the Fractal team went from never having seen an election roll to running the largest election database ever created with over 1.7 billion records – for 12 states alone.

With only 165 million or so voters in the United States, why such a large database?

Data travels, data moves, data tells a story as it traverses different databases – over time.

Let’s take an example.

Phineas Phrogg, our made up character is on a voter roll.  Phineas owns a home, has three credit cards, two cars, does limited social media, is a deacon at his church and active in the Lions Club.

Phineas’ data in any single database yields 1 x 1 = 1 level of insight.

A state voter roll, taken on March 15, is a flat surface with little actionable information.

If we take multiple databases where Phineas appears – his credit file, auto history, auto registration, donation info and perhaps 10 other common places Phineas innocently appears, we get a relief map – not a flat surface.

Artificial intelligence predicts a lot of what Phineas is likely to do and likely to buy.

Here Phineas’ information is 1 (Phineas) x number of data sources = 1,000 or 10,000 data points.  The A.I. program knows more about Phineas than he may know himself.

This is not high tech.  Every major consumer goods company does this work today – every reader of this article exists in scores of these databases.

Now for the hard stuff.

What if we take a snapshot of every one of Phineas databases on different dates?  Perhaps every month, or every week?

We see Phineas’ actions over time.

We see Phineas’ likes and dislikes in one database – perhaps the car he chooses – change over the time series.  We can probably tie some of those new preferences to changes in another data source – perhaps a contribution database.

Phineas started giving money to animal rescue, this change might ripple through some other preferences as well – maybe he is moving toward being a vegetarian.

The A.I. systems for the consumer goods company will pick this up too – 3 years from now.  We identify the behavior change almost instantly.

That, people, is a game changer.

That is why, for a single state, like Pennsylvania or Georgia, we collected over 350,000,000 (350 million) – records from their voter rolls in less than one year.  I just wanted you to see all the zeroes.

When our team built the TSA No-Fly List technology and the auto fraud systems for State Farm, GEICO, USAA and others, their data teams bemoaned the “dirty data” in those databases.  They had clear agita from the misspellings, wrong addresses, different ways to show an address, fraudulent entries.

Our team loved dirty data.

This is not a porno thing – but dirty data – inaccuracies, misspellings, multiple ways of entering a street name – are Hansel and Gretel’s little stones leading to insight you cannot find anywhere else.

For eBay, when we built their cyber fraud prevention system – they already called the Secret Service, the FBI, every neural net company – they were all stymied by – you guessed it – dirty data!

The data magnification lesson is over.  Let’s get to voter rolls.

If you do not think the government’s election commissions are in on the massive voter fraud inherent in every state’s voter rolls, you can stop reading here – because they are and we can prove it in state after state.  Read some of our reports on www.Omega4America.com.

Anyone can compare voter rolls with NCOA (National Change of Address DB) and find people who moved.

Any high school math kid can run statistics against voter rolls and find anomalies growing on trees.

Any tech quant, living in their parents’ basement can run an obscure algorithm showing vote numbering inconsistent with historical patterns.

Come to think of it, in 2021 and 2022 these guys were everywhere – and they didn’t remove fake voters.  Time to move on – they failed and the Republicans failed with them.

We know phantom voters are the seed bed for fake ballots – the ballots aren’t fake, they are quite real, but called “fake” because they aren’t voted by the name on their envelope.

We know fake ballots are mailed, at industrial scale, to legitimate voters, fake voters, dead voters, voters who moved.

The Fractal election system is used by voter integrity teams to show, by cross searching personal property rolls, for instance, that Phineas Phrogg votes and lives in an address that is an Ace Hardware Store.  That should be enough to get Phineas off the voter roll.

What if it isn’t?

The UnDeliverable Ballot Database is not a “bad address” list.

It is real time – almost, depending on the data – using snapshotting technology developed with the Wisconsin voter integrity team.  It picks up changes in multiple data sources – constantly!

Here are examples from 2020 and 2022:

Phineas lives at an apartment building with 125 units.  The property roll tells us it is a multi-family unit, but Phineas does not have his UNIT or APT number in the election roll – so a ballot is going to Phineas, but he won’t get it.

We know this today – 2 years before 2024.

We can know this for every apartment building in every state, in every county in America in 90 days.  Maybe right now might be a good time to take action to either get Phineas’ APT number in the voter roll or get him off of it.

But if not, we know with certainty that his ballot cannot legally be voted.

A local integrity team may want to hang out when ballots arrive – with the leftist who will certainly be there – to track what happens with that ballot.  No voter intimidation here – just want to make sure the leftist kid there to collect Phineas’ ballot – doesn’t!

Fractal cross searches every voter against every physical address they claim to inhabit – and kicks out “anomalies.”

In a Midwestern state this month, canvassers who used the RNC data for electioneering months ago – used the Fractal-cleaned lists – said “…in 22 years canvassing we have never seen such accurate lists, the RNC lists were garbage.”

Because Fractal told them the square footage of every single-family residence, they could determine which were phantom nests with 15 registered adults in an 800 square foot home.

The Wisconsin team innovated in 2022 with the “root query.”

The Fractal system found 1,250 people living at a single address – a college dorm.  The Wisconsin team found only 300 people can live there at any one time.  Good find.

That was not enough for these guys.  They had the Fractal team create the “root search” – thus dig into the address one layer deeper.  Guess what?

Not only were 1,250 people getting ballots at an address where only 300 can live, but 450 of those ballots went to a single dorm room!

What if that room number changes?

The Fractal system picks it up by snapshotting the voter rolls every month.  RNC data – well, tough luck!

Thus, the UnDeliverable Ballot database knows where 900 fake ballots are going to be sent – even when they change the address!

Multiply that by every college dorm in America, every apartment building, homeless shelter, church – you get to election-impacting pretty fast!

Think maybe that might be cool info to know?

We are fortunate our teams used the RNC lists and the Fractal lists in the same state, months apart with massively different results.  Thus, perhaps a tech solution is at hand.

As we work with voter integrity teams to create the UnDeliverable Ballot Database in other states, we look to ingest literally trillions of records – with tons of dirty data – because we can know with certainty where a 2024 ballot is going but Phineas isn’t going to get it.