Kelly Rosa Braghetto

Fernando Freire Scattone, Kelly Rosa Braghetto

A considerable volume of data is collected from sensors today and needs to be processed in real time. Complex Event Processing (CEP) is one of the most important techniques developed for this purpose. In CEP, each new sensor measurement is considered an event and new event types can be defined based on other events occurrence. There exists several open-source CEP implementations currently available, but all of them use orchestration to distribute event processing. This kind of architectural organization may harm system resilience, since it relies on a central core (i.e. the orchestrator). Any failures in the core might impact the whole system. Moreover, the core can become a bottleneck on system performance. In this work, a choreography-based microservices architecture is proposed for distributed CEP, in order to benefit from the low coupling and greater horizontal scalability this kind of architecture provides.

Filipi Miranda Soares, Antonio Mauro Saraiva, Luís Ferreira Pires et al.

Managing scientific names in ontologies that represent species taxonomies is challenging due to the ever-evolving nature of these taxonomies. Manually maintaining these names becomes increasingly difficult when dealing with thousands of scientific names. To address this issue, this paper investigates the use of ChatGPT-4 to automate the development of the :Organism module in the Agricultural Product Types Ontology (APTO) for species classification. Our methodology involved leveraging ChatGPT-4 to extract data from the GBIF Backbone API and generate OWL files for further integration in APTO. Two alternative approaches were explored: (1) issuing a series of prompts for ChatGPT-4 to execute tasks via the BrowserOP plugin and (2) directing ChatGPT-4 to design a Python algorithm to perform analogous tasks. Both approaches rely on a prompting method where we provide instructions, context, input data, and an output indicator. The first approach showed scalability limitations, while the second approach used the Python algorithm to overcome these challenges, but it struggled with typographical errors in data handling. This study highlights the potential of Large language models like ChatGPT-4 to streamline the management of species names in ontologies. Despite certain limitations, these tools offer promising advancements in automating taxonomy-related tasks and improving the efficiency of ontology development.