Data mining algorithms to find association rules are an important tool to extract knowledge from databases. However, these algorithms produce an enormous amount of rules, many of which could be redundant or irrelevant for a specific decision-making process. Also, the use of previous knowledge and hypothesis are not considered by these algorithms. On the other hand, most existing data mining approaches look for patterns in a single data table, ignoring the relations presented in relational databases. The contribution of this paper is the proposition of a multi-relational data mining algorithm based on association rules, called TBMR-Radix, which considers previous knowledge and hypothesis through the using of the Templates technique. Applying this approach over two real databases, we were able to reduce the number of generated rules, use the existing knowledge about the data and reduce the waste of computational resources while processing. Our experiments show that the developed algorithm was also able to perform in a multi-relational environment, while the MR-Radix, that does not use Templates technique, was not.

The popularization of the internet allowed the use of different technologies for storing and retrieving bilingual dictionaries. An example is the Resource Description Framework (RDF) datasets. The storage of bilingual dictionaries in this type of structure makes it possible to discover indirectly connected translations through inference. However, such datasets are not efficient in graph search operations, which are important actions for inference techniques in the discovery of translations. On the other hand, database management systems based on the graph model offer a collection of resources that facilitate the tasks that involve such operations. In this way, a group of bilingual dictionaries from an RDF dataset was adapted to a Graph Database Management System (GDMS) which made it possible to reduce and simplify the structures of these dictionaries. Subsequently, 2 translation inference techniques were implemented in which two new bilingual English-Portuguese dictionaries were automatically generated. Thus, such inference algorithms are efficient in generating new dictionaries through GDMS. The scientific contribution is the application of a new technology for data storage and retrieval in support of bilingual dictionaries, in which there are advantages together with inference techniques in the generation of bilingual dictionaries and not addressed by related papers in literature.

Computer vision systems are essential for automating agricultural tasks such as disease detection and fruit defect identification. However, their application in coffee farming faces significant challenges due to environmental variability and the complex structure of coffee trees, which complicate image acquisition. Thus, this study addresses two key questions: 1) Can low-cost, user-friendly equipment adapt to crop conditions while ensuring image quality 2) Can a computer vision algorithm accurately count and classify coffee beans with over 80% accuracy using data from low-cost cameras To answer these questions, an image acquisition system was developed based on the phenological characteristics of coffee plants, ensuring focused and consistent image capture. Additionally, a novel algorithm was created, utilizing statistical analysis of color spaces to effectively separate fruits from the background, segment images, and count fruits. The algorithm achieved accuracy rates, when compared with a traditional approach, within the desired range for each coffee fruit class: green (83%), green-olive (79%), cherry (86%), and raisin (80%). These results demonstrate the potential of this approach for accurate and efficient fruit processing in coffee farming, particularly when images are captured directly from tree branches.

Spatial Big Data mining is often hindered by high computational complexity and the intrinsic autocorrelation of georeferenced records. To address these challenges, this study proposes an architectural optimization framework for the CHSMST+ algorithm, designated as CHSMST+MR. Rather than introducing a brand-new clustering paradigm, the framework focuses on a Distributed Spatial Cardinality Reduction (DSCR) layer that aggregates redundant spatial records before the core iterative mining logic begins. By transforming raw records into a weighted key-value representation within the Apache Spark environment, the proposed approach significantly mitigates the shuffling bottleneck common in distributed systems. Experimental validation using high-density biological datasets demonstrates an average execution-time reduction of 51.36%, with performance gains reaching up to 79.96% in specific high-redundancy scenarios. The results, obtained through controlled local emulation, confirm that this architectural optimization provides a scalable, deterministic, and lossless solution for accelerating spatial clustering. This work contributes a methodological path for enhancing the performance of iterative spatial mining algorithms in environments characterized by massive data density and coordinate redundancy.