The similarity-aware relational division database operator

In Relational Algebra, the operator Division (÷) is an intuitive tool used to write queries with the concept of "for all", and thus, it is constantly required in real applications. However, as we demonstrate here, the division does not support many of the needs common to modern applications, particularly those that involve complex data analysis, such as processing images, audio, genetic data and many other "non-traditional" data types. The main issue is the existence of intrinsic comparisons of attribute values in the operator, which, by definition, are always performed by identity (=), despite the fact that complex data must be compared by similarity. Recent works focus on supporting similarity comparison in relational operators, but none of them treats the division. This paper presents the new Similarity-aware Division (÷) operator. Our novel operator is naturally well suited to answer queries with an idea of "candidate elements and exigencies" to be performed on complex data from real applications of high-impact. For example, we validate our proposal showing that it is potentially useful to support genetic analysis.