Real-World Behavior in Digital Data

 

It is no news that the digital age has in many ways supplanted direct human engagement around interests, desires, activities, brands, and many other elements. While this has permanently changed the way people interact and access information, it has also created a permanent data footprint of our lives. In essence, 'human behavior' is replicated, stored digitally, and accessible on a vast scale. Today, our lives move through time as much in the digital domain as they do in the physical. 

 

The idea of 'social listening' was an attempt to better understand human behavior and make sense of the many factors that influence our perception and decisions, every day. However, results have not met expectations. Reducing human engagement and interaction down to measuring the online quantities or repetition of words and phrases is insufficient to truly understand behavioral complexities.      

 

AURORA and Human Behavior

 

Human behavior is difficult to deconstruct because it is almost always influenced by a number of multiple, interacting factors. In addition, it has a habit of changing over time and is unique for each person. This makes it complex and, at times, even appear random.

 

AURORA is built on the premise that behavior is rarely 'the sum of its parts' and the key to successful analysis lies with the ability to measure the precise interaction and relationships between all parts.  The foundation of AURORA's success is its ability to detect and measure the internal associations between factors, which collectively cause a particular behavioral trait or decision.

 

AURORA is a highly refined, pattern detection platform. It is focused on the ability to identify and map behavior quantitatively by measuring complex individual associations, on a vast scale. This produces highly accurate understanding and measurement of exactly how information is perceived by its audience, and what factors most significantly influence behavior.

 

Demographics, affinity groups, or the anatomy of trends are in essence constructs of overlapping desires, interests, and affinities.  The AURORA engine is able to create behavioral profiles of such constructs by detecting large quantities of individually overlapping and intersecting behavioral associations. In other words, AURORA identifies demographics and behaviorally linked groups via the interactions and associations through which they come to be.

Complex Behavioral Pattern Analysis

 

Most behavior and events around us are not caused by random factors. What we witness and how we act most often appears intuitively predictable, even logical. At the same time, the world around us is shaped and triggered by many factors and elements acting together, at different times. A single action or event, observed in isolation, may appear random, yet be highly predictable when viewed contextually. Most of what we do and what we observe is linked in context to the environment around us. The key to understanding behavior and events is to measure relation. Most things happen for a reason.

 

The single most important element in establishing behavioral pattern between two variables is the distinction between relation and similarity. Having the ability to confirm inherent, functional relation versus mere similarity in data enhances any analytical process at its very core. AURORA uses its foundation in pattern compression to establish the presence, proof, strength, and nature of a relation between two elements. We refer to this approach as detecting ‘Pattern-Proof’ relation, and it represents a single process able to detect diverse complex relational patterns with consistency. It is this key capability which most significantly distinguishes AURORA, opening a wide range of application. In turn, AURORA can be embedded into the core of any existing procedure or application.

 

Relational pattern is measured in consumer data, fraud detection, image recognition, advertising, network security, healthcare, scientific research, and finance. AURORA can replicate or enhance a diverse array of existing technologies and products, as well as be a standalone platform. Leveraging on its 'pattern-proof' inference, AURORA represents the ability to measure and gain inherent understanding of how complex relations impact behavior and events.

 

 

The Challenge With Behavior and Relational Data

 

While we have entered an era where vast amounts of data are growing at our disposal, the fundamental problem is that the science of statistics does not provide us with a comprehensive approach to exploit them. AURORA is a singular approach towards the entire universe of relational pattern. AURORA has the ability to detect, measure, and compare relational pattern across very different circumstances and levels of complexity. It is based on the ability to 'prove ' relational pattern, not observe relational behavior.

 

While large amounts of data are available, 'connecting the dots', continues to represent a challenge. AURORA has been designed specifically to address this problem. AURORA builds contextual understanding by defining and measuring large-scale relational pattern through a sophisticated but singular approach.  In turn, AURORA is able to define and anticipate behavior much more effectively than individual models, statistics, and data.  More importantly, AURORA is able to detect pattern visually (images), in written text across all languages, and through many layers of complex numeric input.

Applications of AURORA

 

AURORA can dissect an entire country into behavioral clusters around many fields of policy or intelligence interests. Having the ability to triangulate relevant elements of influence and clearly mapping behavioral/reactionary flows across the social-media sphere will serve as a tremendous tool in predicting, anticipating, and measuring indications of intent around larger scale organizations and actors. Most importantly, this is achievable with merely using open-source, social media data.  Other online sources, (publications, organic-search, blogs, etc.), and/or access to closed-source data brings the efficacy of the AURORA Engine into a whole new universe.

 

The spectrum of applicable and relevant data AURORA is capable of running goes well beyond the text-to-signal pattern examples we have introduced here. Our recent successes in generating signals and linking patterns out of image pixilation and electrical activity of the human brain are probably good examples of the diversity and creativity our non-linear relation capability opens up.

 

AURORA is a highly advanced learning algorithm that continues to evolve over time. As the system incorporates more data from around the globe, it will determine deeper and more granulated patterns in seemingly diverse sources. Continuous fusion of new data sources and advancement in sensors will assist in the development of the next generations of AURORA. Since AURORA is a learning engine, and the world continues to progress through the digital age, more and more information will be available to the system to fully integrate, delivering a global psychological and behavioral map of the Earth’s population. This provides the users of AURORA with unprecedented insight and knowledge of the World’s thoughts, concerns, and intent. 

 

The applications of AURORA are not solely rooted in the Intelligence Community. Multiple versions of the algorithm can be applied for commercial application to study consumer intent, or to other government agencies to reduce, fraud, waste and abuse. In the healthcare industry for medical records and patient information, pharmaceuticals and new drug development, or in Universities and research centers to assist with pattern analysis of advanced sciences and theoretical physics. As the World becomes more connected and the advancement of technology increases, the applications for AURORA become limitless.