| Grant number: | 14/21483-2 |
| Support Opportunities: | Regular Research Grants |
| Start date: | May 01, 2015 |
| End date: | April 30, 2017 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | Robson Leonardo Ferreira Cordeiro |
| Grantee: | Robson Leonardo Ferreira Cordeiro |
| Host Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
| Associated researchers: | Agma Juci Machado Traina ; Caetano Traina Junior ; Luciana Alvim Santos Romani ; Priscila Pereira Coltri ; Renata Ribeiro Do Valle Gonçalves |
Abstract
In the Relational Algebra, the operator of Division (÷) is an intuitive tool to write queries involving the concept of "for all", and thus, it is constantly required in many real applications. However, we demonstrate here that the relational division cannot support many of the needs common to current applications, particularly, those that involve complex data analysis, such as processing images, audio, long texts, fingerprints, and several other "nontraditional" data types. Investigating the problem we found out that the main limitation is the existence of intrinsic attribute comparisons in the relational division, which, by definition, are always based on identity (=), despite the fact that in most cases complex data must be compared by similarity. Today, many works in the literature focus on similarity-aware relational operators, however, none of them treat the relational division. This research project proposes to investigate and to extend the operator of Division (÷) from the Relational Algebra aimed at making it well-suited to the needs of current applications, by supporting similarity-based attribute comparisons. We also show that the similarity division is naturally well-suited to answer queries involving an idea of "candidate elements and exigences", described in the project, to be performed on complex data objects coming from real, high impact applications. For example, it is potentially useful in agriculture, in hiring personnel for enterprises, and even to help identifying promising shares in stock marketing. To validate the ideas, we propose a case study to investigate the automatic identification of cities well-suited to produce particular types of agricultural crops,and to develop one computational system to support strategic decisions in agriculture, based on the analysis of remote sensing images. (AU)
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