Fernanda Baião | PUC-RJ - Academia.edu (original) (raw)

Papers by Fernanda Baião

Frontiers in Public Health, May 11, 2023

WIT Transactions on Information and Communication Technologies, 1970

Brazilian Symposium on Databases, 2003

... Matheus Wildemberg, Melise MV Paula, Fernanda Baião, Marta Mattoso {mwild, mel, baiao, marta}... more ... Matheus Wildemberg, Melise MV Paula, Fernanda Baião, Marta Mattoso {mwild, mel, baiao, marta}@cos.ufrj.br Programa de Engenharia de Sistemas e ... De posse de tais parâmetros, define-se uma função de custo a partir da qual pode-se estimar o custo da execução de um ...

Revista Brasileira De Terapia Intensiva, Jun 1, 2020

WIREs Data Mining and Knowledge Discovery, 2021

For many years, the role played by domain knowledge in all stages of knowledge discovery has been... more For many years, the role played by domain knowledge in all stages of knowledge discovery has been recognized. However, the real‐world semantics embedded in data is often still not fully considered in traditional data mining methods. In this article, we argue that the quality of data mining results is directly related to the extent that they reflect important properties of real‐world entities represented therein. Analyzing and characterizing the nature of these entities is the very business of the area of formal ontology. We briefly elaborate on two particular types of artifacts produced by this area: foundational ontologies and ontology‐driven conceptual modeling languages grounded on them. We then elaborate on the benefits they can bring to several activities in a data mining process.This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Data Concepts

Proceedings of the XV Brazilian Symposium on Information Systems, 2019

Database tuning is a crucial task to address the performance of information systems that deal wit... more Database tuning is a crucial task to address the performance of information systems that deal with a considerable amount of information stored in databases. Current tuning tools are very platform-specific and do not provide adequate support for the database administrator to reason about performance improvement suggestions. In this paper, we discuss several architectural and implementation decisions of Outer-Tuning, our framework that supports database tuning. Outer-Tuning follows a model-driven development and a modular architecture design, which enabled several benefits. This paper contributes with: (i) the architectural design model adopted in Outer-Tuning, which combines imperative and declarative programming; (ii) the discussions and steps to integrate several software components; and (iii) the actual framework implementation. We assess our framework with an experiment using the TPC-H benchmark. The results evidence that Outer-Tuning infers useful tuning actions and supports the DBA by providing a more semantic environment to create and adapt tuning heuristics using concepts closer to his/her domain, and also relevant information on the rationale of the tuning actions through a friendly web interface.

User participation is a promising approach for Ontology Matching; however, determining the most r... more User participation is a promising approach for Ontology Matching; however, determining the most representative pairs of entities is still a challenge. This paper delineates an Ontology Matching approach for user participation employing a clustering algorithm.

PLOS ONE, 2021

Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least... more Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines in the Genomics of Drug Sensitivity in Cancer database, we used machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms representing diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. We artificially subsampled the data ...

The Lancet Respiratory Medicine, 2021

Revista Brasileira de Terapia Intensiva, 2020

2014 IEEE 10th International Conference on e-Science, 2014

Data provenance is the process of managing a collection of metadata that catalogs the origin and ... more Data provenance is the process of managing a collection of metadata that catalogs the origin and history of data. In scientific workflows, this metadata assists scientists and domain specialists in several tasks, including the reproduction of scientific experiments and planning of new scenarios to be experimented. However, the amount of provenance data generated from scientific workflow executions can grow through time, becoming infeasible for scientists to manually evaluate them. Thus, mechanisms for automatically extracting knowledge from provenance data and presenting them to the user are demanding. Due to the diversity and flexibility inherent to scientific experimentation scenarios, declarative models are potentially adequate. In this work, we propose to apply techniques for learning a declarative model from provenance data generated by scientific workflows, from which the domain specialist will be able to plan future scenarios for his/her scientific experiment. The proposed solution is illustrated in a case study of a scientific experiment on ontology matching, which is a data-intensive strategy that is required to solve the problem of information integration in several areas of knowledge.

Proceedings of the International Conference on Knowledge Management and Information Sharing, 2011

A Knowledge-intensive process (KIP) is characterized as a bag of activities based on knowledge in... more A Knowledge-intensive process (KIP) is characterized as a bag of activities based on knowledge intensive acquisition and manipulation. A KIP differentiates from traditional business processes mainly because of the dynamism or the variability of event flows among the process instances. Thus, traditional business process modeling notations do not adequately represent the broad spectrum of flows that occur in each KIP instance, making them even more challenging to be understood or managed. Some approaches to represent a KIP were defined in the literature, but representing the KIP variability in a cognitively efficient way is still an open issue. This paper presents the graphical notation KIPN-C, which proposes diagrams to represent variability of a KIP relating it to the context of its instances.

ONTOBRAS, 2017

Modeling KnowledgeIntensive Processes (KIP) is very important for understanding critical scenario... more Modeling KnowledgeIntensive Processes (KIP) is very important for understanding critical scenarios in current organizations. KIPO (KnowledgeIntensive Process Ontology) is an ontology wellfounded, semantically rich conceptualization of KIP. However, it is difficult to distinguish instances and models in KIP. Our goal is to propose an application with the notion of multilevel conceptual modeling for representing elements with multiple classification level.