Complexity Engineering

A Different Look at Air Traffic

As we have seen in our previous blogs, holistic benchmarking can be applied to a wide class of problems, including images. Images originating from astronomical observations, medicine, weather radar, etc. In this short blog we illustrate the case of air traffic. In particular, we examine and compare a “critical” (very high volume) traffic situation to a “standard” one, the intent being to actually measure the difference between the two. For the purpose we use two traffic density maps. Both images are illustrated below together with the corresponding Complexity Maps as obtained using OntoBench, our holistic benchmarking system.

Based on the comparison of the topologies of the two Complexity Maps (reference image has complexity  C = 275.49, while the second has C = 302.15) which is more significant than a simple comparison of image complexities (or entropies) one obtains that the degree of image similarity is 59.56%. Consequently, the difference between the second and first image is, globally speaking, 100 – 59.56 = 40.44%.

Based on the analysis of image complexities one may state that the overall difference between the two scenarios is  100 – 59.56% = 40%. In other words, the “critical” situation is 40% more “severe” (or complex) than the baseline.

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

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