The similarity-aware relational division database operator with case studies in agriculture and genetics

Abstract 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, large graphs, fingerprints, 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 no one treats the division. This paper presents the new Similarity-aware Division ( ÷ ˆ ) 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 modern applications. For example, it is potentially useful to support agriculture, genetic analyses, digital library search, prospective client identification, and even to help controlling the quality of manufactured products in industry. We validate our proposals by studying the first two of these applications.