Fuzzy (and Interval) Techniques in the Age of Big Data: An Overview with Applications to Environmental Science, Geosciences, Engineering, and Medicine

Abstract

In some practical situations -e.g., when treating a new illness -we do not have enough data to make valid statistical conclusions. In such situations, it is necessary to use expert knowledge -and thus, it is beneficial to use fuzzy techniques that were specifically designed to process such knowledge. At first glance, it may seem that in situations when we have large amounts of data, the relative importance of expert knowledge should decrease. However, somewhat surprisingly, it turns out that expert knowledge is still very useful in the current age of big data. In this paper, we explain how exactly (and why) expert knowledge is useful, and we overview efficient methods for processing this knowledge. This overview is illustrated by examples from environmental science, geosciences, engineering (in particular, aircraft maintenance and underwater robots), and medicine.

Logic reasoning under data veracity concerns

“…In other approaches, confidence has been modelled considering a level of uncertainty on suitability. As such, intervals [18], type-2 fuzzy sets [5], and R-sets [23] have been used as mathematical tools to impose a kind of upper and lower bound for suitability reflecting uncertainty on it. This uncertainty can be interpreted as reflecting veracity.…”

Green computing: three examples of how non-trivial mathematical analysis can help

“…There are many important problems in environment-related computing -and we ourselves, together with our students, have participated in solving some of these problems; see, e.g., [2][3][4][5][6][7][8][9][10][11] .…”

Can big data tame a “naughty” world?

“…Whilst it is not fair to assume that computational methods have invalid epistemologies or are somehow antithetical to critical research (Wyly ), there is a danger that big data supports a deterministic view of the world where the “proposed solution to residual uncertainty is more data and better computers” (Shearmur , 966). It is sobering to note that, despite the advances in observation and computational power that have resulted in an exponential increase in the amount of meteorological data from observations and models over the past few decades, the role of the human meteorologist still adds value to the forecast, improving the prediction by 10–25% (Kreinovich and Ouncharoen ). This ratio has not changed significantly with time, money, improved theoretical understanding, computer models, or data availability.…”