Fuzzy Logic Data Classification and Selection{1}


This article focuses on the idea of fuzzy logic integrated into a database. What is fuzzy logic? It is a generalized logical condition (GLC) extension with its own set of rules in determining the data selection and data classification into one entity. In other words, fuzzy logic has a more “reasonable” approach and instead of being exactly what was asked of the query, it approximates the result. This gives users results that may have a potential to be true. Of course in order to take the fuzzy approach, you have to abide by its rules, much like any type of SQL program. Now for my best example, you want to make an order to your suppliers for new parts for your company when you have say 1000 parts left. With fuzzy logic, it would tell you to order new parts when it “felt” right to do so, as if it were saying that 1050 parts left is close enough. Probably a bad example but I tried, and everything I researched online were math equations and I doubt it would have helped more.  In any case, fuzzy logic would simply give the user approximate values and ambiguous data instead of absolute answers.

In relating to the class, I think this article takes a look at what is out there in terms of looking at data differently. It represents a way of retrieving data by using queries. However, like the article mentions, would allow approximate values instead of the typical binary yes or no answers. We are looking at simple SQL queries and want direct data for what we are looking for. So in terms of using something like this in a classroom, I don’t think it would be wise when just starting out.

This article caught my eye for some different reasons. One obviously because we are currently delving into SQL queries and the logic behind retrieving data. Also, to me fuzzy logic is more like looking at an uncertainty within the data. So it may be useful to have an idea about what it does for future reference. I would like to see this idea first hand to get a closer look and better understanding of what exactly it is capable of.

 

Hudec, Miroslav, and Mirko Vujosevic. “Integration of Data Selection and Classification by Fuzzy Logic.” Expert Systems with Applications, 39.10 (2012): 8817-8823.