Applied Visual Complexity

Applied Visual Complexity (AVC) is a concept that encompasses different tools and technologies for data visualization and that includes concepts from Complexity Theory.

Different institutions and companies have worked for more than 20 years in data visualization; however, until recent years it has been possible, given the advancement of graphic capabilities in computer equipment, to represent 100% of a data set on screen, especially when the number of registrations exceeds the hundreds of thousands and reaches the millions.

AVC has mainly two premises:

The visualization of 100% of the data of a data set, regardless of its size (hundreds, thousands, or millions of records)
The faithful representation of the data, that is, the real-time representation of the data.

Complexity theory is included from the moment in which we mix data from two or more systems simultaneously and these systems interact with each other.

The problem lies in the amount of information that is produced every day, where it is estimated that every second 1.7 MB of data is generated per second in 2020 (Source: Domo) and 2.5 quintillion bytes of data produced by humans are produced daily (source: Social Media Today).

Each person creates 1.7 MB of data per second during 2020.
In the last two years alone, a staggering 90% of the world's data has been created.
Humans produce 2.5 trillion bytes of data every day.
Humans will generate 463 exabytes of data every day starting in 2025.
Every day 95 million photos and videos are shared on Instagram.
By the end of 2020, 44 zettabytes will make up the entire digital universe.
Every day 306.4 billion emails are sent and 5 million Tweets are made. (source: http://www.sciencedirect.com/science/article/pii/S0001691815300160)

This generates a scenario where by 2025 we will have approximately 463 exabytes of data generated per day (source: Ranconteur) according to the amount of data that moves through video, communications, and social networks. According to STATISTA data, there are 4.57 billion active internet users today.

Adding up all these scenarios we can see that the storage of information will be a big problem in the future, as well as the interpretation and decision-making process. The traditional processes for the generation of data visualization correspond to the upper part of a data warehouse that is consumed based on specific requirements or questions, leaving the user with partial visibility of the information ecosystem.

Currently, the speed of understanding information is paramount. This, linked to the multiplicity of data sources of different types, traditional information presented in structured data, unstructured information, fuzzy logic variables, and metadata, creates a perfect scenario to be approached through the Complexity Theory.

Complex Systems are understood as those systems with a large number of components that interact with each other (agents, processes, inputs, outputs, etc). Its aggregate activity is NOT linear (Not derivable from the sum of the activity of the individual components). The WHOLE is more important than the sum of the parts. Research in complexity sciences is multidisciplinary.

Based on this problem, AVC technology was created as a set of libraries and languages based on open source data visualization projects, and a modernization of all traditional tools was generated to build a graphics engine called vOne that allows visualizing different sources, integrating them, linking them, and transforming them in a process known as vETL, triggering vOne as a new technology to process data visually. In parallel, the concept of Applied Visual Complexity was coined as the theoretical premise for understanding and understanding big data.

AVC technology is a precursor breaking the paradigms of traditional Big Data and Data Visualization, where the user interacts in 3D spaces from which it is possible to manipulate and interpret the data.

The premise is innovative compared to current theories where to create a data visualization it is necessary to segment the information and prepare an extract to present it. Applied Visual Complexity provides a new approach based on the traditional scientific method where observation is the first step in the chain. This is solved by the vOne platform designed to connect the data source to the Visual Data Model and allow, through business rules, algorithms, and complexity theory, to represent information for decision making in a faster and more precise way.

Each of the data sources that can be databases, work files, multimedia, metadata, web services, data streams in real time, to name a few, is integrated as an information layer allowing the creation of Visual Models.

Each visual model covers a theme. Each of the records in the data set is known as an object of study. Applied Visual Complexity proposes the presentation of 100% of the data individually to the end user to allow them to develop attention, cognition, understanding, and interpretation processes for effective decision-making.

According to the author TOR NORRETRANDERS, in the book The User Illusion, the user to whom we present the information has greater problems if it requires a mathematical, statistical, and conscious way of thinking; However, if we use vision to receive information subconsciously, understanding and the ability to process the content in both increases.

References

"Applied visual complexity". www.facebook.com. Retrieved 2020-09-10.
Palumbo, Letizia; Ogden, Ruth; Makin, Alexis D. J.; Bertamini, Marco (2014-12-01). "Examining visual complexity and its influence on perceived duration". Journal of Vision. 14 (14): 3. doi:10.1167/14.14.3. ISSN 1534-7362. PMID 25487112.
Purchase, Helen C.; Freeman, Euan; Hamer, John (2012). Cox, Philip; Plimmer, Beryl; Rodgers, Peter, eds. "An Exploration of Visual Complexity". Diagrammatic Representation and Inference. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer. 7352: 200–213. doi:10.1007/978-3-642-31223-6_22. ISBN 978-3-642-31223-6.
Machado, Penousal; Romero, Juan; Nadal, Marcos; Santos, Antonino; Correia, João; Carballal, Adrián (2015-09-01). "Computerized measures of visual complexity". Acta Psychologica. 160: 43–57. doi:10.1016/j.actpsy.2015.06.005. ISSN 0001-6918. PMID 26164647.

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