activity
20192023
most citedWorkflow Provenance in the Lifecycle of Scientific Machine Learning

44 citations · 45 across the 8 of their papers we have counts for

collaborators

11 papers

cs.DB2023

A Polystore Architecture Using Knowledge Graphs to Support Queries on Heterogeneous Data Stores

Leonardo Guerreiro Azevedo, Renan Francisco Santos Souza, Elton F. de S. Soares +4

Modern applications commonly need to manage dataset types composed of heterogeneous data and schemas, making it difficult to access them in an integrated way. A single data store t…

cs.AI2023

A Knowledge-Oriented Approach to Enhance Integration and Communicability in the Polkadot Ecosystem

Marcio Ferreira Moreno, Rafael Rossi de Mello Brandão

The Polkadot ecosystem is a disruptive and highly complex multi-chain architecture that poses challenges in terms of data analysis and communicability. Currently, there is a lack o…

cs.DB2020★ 44 cited

Workflow Provenance in the Lifecycle of Scientific Machine Learning

Renan Souza, Leonardo G. Azevedo, Vítor Lourenço +10

Machine Learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting the computational science and engineering domains,…

cs.DB2020

Managing Data Lineage of O&G Machine Learning Models: The Sweet Spot for Shale Use Case

Raphael Thiago, Renan Souza, L. Azevedo +7

Machine Learning (ML) has increased its role, becoming essential in several industries. However, questions around training data lineage, such as "where has the dataset used to trai…

cs.AI2019★ 1 cited

Effective Integration of Symbolic and Connectionist Approaches through a Hybrid Representation

Marcio Moreno, Daniel Civitarese, Rafael Brandao +1

In this paper, we present our position for a neuralsymbolic integration strategy, arguing in favor of a hybrid representation to promote an effective integration. Such description…

cs.AI2019

Bridging the Gap between Semantics and Multimedia Processing

Marcio Ferreira Moreno, Guilherme Lima, Rodrigo Costa Mesquita Santos +2

In this paper, we give an overview of the semantic gap problem in multimedia and discuss how machine learning and symbolic AI can be combined to narrow this gap. We describe the ga…