Resources

Documentation

Tools for Adjustment Optimization and Balancing

The technology from Ellipsoid Analytics is built upon decades of advanced development work in the fields of physics, mechanics, engineering and mathematics. EA founders have leveraged knowledge from across all of these disciplines to invent a new approach to data analytics that we call “Numerical Balancing”. This unique and innovative approach to extracting intelligence from your data, is fast and efficient, requires no pre-conceived assumptions and deals with previously unsolvable problems within data sets -- problems like singularities and heteroscedasticity are resolved. The mathematics behind this development work has been captured in three volumes of documentation – books written in a combination of English and German language. All three volumes available for $79.99 with free shipping. Contact us below to order your copy!

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Evidence Packets

Pure Analytics from the Outside-In

Coming Soon -- In 1921, when Albert Einstein made his famous statement about the connection between the laws of mathematics, reality and certainty, he shined a light on the fact that mathematics, while useful and powerful in many ways, is also imperfect in its ability to describe and understand our physical reality with a high degree of certainty. Just as Einstein declared, it is well-known that there are uncertainties around the ability of mathematics to decisively explain conditions encountered in the real world – especially in the area of assumption based statistics that use estimations and probabilities to understand the data we collect about the world around us. This evidence pack uses the simple example of a sphere, a well-known and easily defined object, to demonstrate how mathematics works perfectly well from the Inside-Out, while struggling from the Outside-In to calculate the same reality. Until now that is -- where the technology of Ellipsoid Analytics can quickly be deployed to assure a 100% accurate, 100% reliable and 100% certain mathematical depiction of any object based on a collection of measured/observed points from the object. Understanding our physical reality from the Outside-In is now just as accurate, and just as accessible, as the Inside-Out.

The Inner Reference - a Unique Constant Inside Every Data Set

Coming Soon -- There are many known mathematical constants of all types and descriptions, with various properties and applications. At Ellipsoid Analytics, we have access to unique geometric relationship that is present throughout our world. In fact, this newly discovered principle is present in every data set of two-dimensions or greater, regardless of the data’s origin or what is describes. This principle is not a number or a mathematical concept, it is in fact a shape that provides a precise orientation of your data and can be used for various tasks to understand data in ways that have not been possible previously. In this evidence pack, we introduce EA’s proprietary technique “numerical balancing” and demonstrate that perfect numerical balance of every point in a data set to each other point, and to their collective unique center of equilibrium, produces a perfect ellipse in two dimensions, an ellipsoid in three dimensions and a hyper-ellipsoid with respect to n-dimensions. We call these elliptical features the “inner-reference” (IR) which form a unique internal “true-north” for any data set. The IR serves as a holistic orientation in 2d/3d problems, and is a representation of the ideal balance in n-dimensions which can be used in various ways to interpret your data sets.

Real-Time, Multi-Parameter, Affine Transformations

Coming Soon -- The capability of affine transformations has been understood for decades, but the ability to put them to use has been limited by the datum points necessary to make the calculations function, and by the computing capacity needed to make the approximations of the transformation. This evidence packet will describe how Ellipsoid Analytics can run high-speed, multi-parameter affine transformations at the touch of a button, often on conventional computing devices. Examples will show how EA does not require datum points within the transformation as reference targets to complete comparative transformations.

Introducing - The Essence Module

Coming Soon -- Ellipsoid Analytics has created the ability to condense any data cloud onto the surface of the hyper-ellipsoid that exists with every data cloud. EA calls these hyper-ellipsoids, along with the condensed information projected on it, the “essence module” -- representing all of the data within the initial data set including the information which describes each points exact location in space and/or any additional features. This evidence packet will discuss the science behind collapsing each data point in a data set along the vector of that particular points “balancing factor” by calculating the inverse of it’s magnitude and direction to the calculated “inner reference”. We will demonstrate how this collapsing can significantly condense the storage required for any data set without loss of any information that describes the data. In addition, the ability to manipulate the orientation of the essence module as desired prior to un-condensing the data will be discussed, as well as benefits to transmitting data faster than was previously possible.

Immediate Knowledge of the Unexpected

Coming Soon -- Anomalies have always presented a series of problems in data science. But what if it was possible to conclusively know which points were anomalies in real-time? Ellipsoid Analytics has changed the game with exactly this capability. This evidence packet demonstrates how EA technology can autonomously determine a maximum allowable deviation from your data’s ideal solution and calculate which points in any data cloud are outside that range – preventing these outliers from skewing the determinations you are making with your data.

The Problem with Singularities

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Letting the Data Choose - Functional Model Determination

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Datum Invariant Positioning - A New Day

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