Journals

2024

HIHISIV: a database of gene expression in HIV and SIV host immune response

22-03-2024

Raquel Lopes Costa, Luiz M. R. Gadelha Jr., Mirela D'arc, Marcelo Ribeiro-Alves, David L. Robertson, Jean-Marc Schwartz, Marcelo A. Soares, Fábio Porto

Abstract:

In the battle of the host against lentiviral pathogenesis, the immune response is crucial. However, several questions remain unanswered about the interaction with different viruses and their influence on disease progression. The simian immunodeficiency virus (SIV) infecting nonhuman primates (NHP) is widely used as a model for the study of the human immunodeficiency virus......

2023

SoftED: Metrics for Soft Evaluation of Time Series Event Detection

02-04-2023

Rebecca Salles, Janio Lima, Rafaelli Coutinho, Esther Pacitti, Florent Masseglia, Reza Akbarinia, Chao Chen, Jonathan M. Garibaldi, Fábio Porto, Eduardo S. Ogasawara

Abstract:

Time series event detection methods are evaluated mainly by standard classification metrics that focus solely on detection accuracy. However, inaccuracy in detecting an event can often result from its preceding or delayed effects reflected in neighboring detections. These detections are valuable to trigger necessary actions or help mitigate unwelcome consequences. In this context, current......

2022

Efficient information diffusion in time-varying graphs through deep reinforcement learning

02-06-2022

Matheus R. F. Mendonça, André M. S. Barreto, Artur Ziviani

Abstract:

Network seeding for efficient information diffusion over time-varying graphs (TVGs) is a challenging task with many real-world applications. There are several ways to model this spatio-temporal influence maximization problem, but the ultimate goal is to determine the best moment for a node to start the diffusion process. In this context, we propose Spatio-Temporal Influence......
Machine Learning Approaches to Extreme Weather Events Forecast in Urban Areas: Challenges and Initial Results

25-05-2022

Fabio Porto, Mariza Ferro, Eduardo Ogasawara, Thiago Moeda, Claudio D. T. Barros, Anderson Chaves Silva, Rocio Zorrilla, Rafael S. Pereira, Rafaela Castro, João Victor Silva, Rebecca Salles, Augusto Fonseca, Juliana Hermsdorff, Marcelo Magalhães, Vitor Sá, Antonio Adolfo Simões, Carlos Cardoso, Eduardo Bezerra

Abstract:

Weather forecast services in urban areas face an increasingly hard task of alerting the population on extreme weather events. The hardness of the problem is due to the dynamics of the phenomenon, which challenges numerical weather prediction models and opens an opportunity for Machine Learning (ML) based models that may learn complex mappings between......
Emergence and algorithmic information dynamics of systems and observers

23-05-2022

Felipe S. Abrahão; Hector Zenil

Abstract:

One of the challenges of defining emergence is that one observer’s prior knowledge may cause a phenomenon to present itself as emergent that to another observer appears reducible. By formalizing the act of observing as mutual perturbations between dynamical systems, we demonstrate that the emergence of algorithmic information does depend on the observer’s formal......

2021

Algorithmic Information Distortions in Node-Aligned and Node-Unaligned Multidimensional Networks

29-06-2021

Felipe S. Abrahão, Klaus Wehmuth, Hector Zenil, Artur Ziviani

Abstract:

In this article, we investigate limitations of importing methods based on algorithmic information theory from monoplex networks into multidimensional networks (such as multilayer networks) that have a large number of extra dimensions (i.e., aspects). In the worst-case scenario, it has been previously shown that node-aligned multidimensional networks with non-uniform multidimensional spaces can display exponentially......
SAVIME: An Array DBMS for Simulation Analysis and ML Models Prediction

14-02-2021

Hermano. L. S. Lustosa, Anderson C. Silva, Daniel N. R. da Silva, Patrick Valduriez, Fabio Porto

Abstract:

Limitations in current DBMSs prevent their wide adoption in scientific applications. In order to make them benefit from DBMS support, enabling Declarative data analysis and visualization over scientific data, we present an in-memory array DBMS system called SAVIME. In this work we describe the system SAVIME, along with its data model. Our preliminary evaluation......

2020

Tactful Networking: Humans in the Communication Loop

07-12-2020

Rafael Lima Costa, Aline Carneiro Viana, Artur Ziviani, Leobino Nascimento Sampaio

Abstract:

This survey discusses the human-perspective into networking through the Tactful Networking paradigm, whose goal is to add perceptive senses to the network by assigning it with human-like capabilities of observation, interpretation, and reaction to daily-life features and associated entities. To achieve this, knowledge extracted from inherent human behavior in terms of routines, personality, interactions,......
STConvS2S: Spational Convutional Sequence to Sequence Network for Weather Forecasting

10-11-2020

Rafaela Castro, Yania M. Souto, Eduardo Ogasawara, Fabio Porto, Eduardo Bezerra

Abstract:

Applying machine learning models to meteorological data brings many opportunities to the Geosciences field, such as predicting future weather conditions more accurately. In recent years, modeling meteorological data with deep neural networks has become a relevant area of investigation. These works apply either recurrent neural networks (RNN) or some hybrid approach mixing RNN and......
A Survey of Biodiversity Informatics: Concepts, Practices and Challenges

29-09-2020

Luiz M. R. Gadelha Jr., Pedro C. de Siracusa, Artur Ziviani, Eduardo Couto Dalcin, Helen Michelle Affe, Marinez Ferreira de Siqueira, Luís Alexandre Estevão da Silva, Douglas A. Augusto, Eduardo Krempser, Marcia Chame, Raquel Lopes Costa, Pedro Milet Meirelles, Fabiano ThompsonLuiz M. R. Gadelha Jr., Pedro C. de Siracusa, Artur Ziviani, Eduardo Couto Dalcin, Helen Michelle Affe, Marinez Ferreira de Siqueira, Luís Alexandre Estevão da Silva, Douglas A. Augusto, Eduardo Krempser, Marcia Chame, Raquel Lopes Costa, Pedro Milet Meirelles, Fabiano Thompson

Abstract:

The unprecedented size of the human population, along with its associated economic activities, have an ever increasing impact on global environments. Across the world, countries are concerned about the growing resource consumption and the capacity of ecosystems to provide them. To effectively conserve biodiversity, it is essential to make indicators and knowledge openly available......
You Shall not Pass: Avoiding Spurious Paths in Shortest-Path Based Centralities in Multidimensional Complex Networks

28-09-2020

Klaus Wehmuth, Artur Ziviani, Leonardo Chinelate Costa, Ana Paula Couto da Silva, Alex Borges Vieira

Abstract:

In complex network analysis, centralities based on shortest paths, such as betweenness and closeness, are widely used. More recently, many complex systems are being represented by time-varying, multilayer, and time-varying multilayer networks, i.e. multidimensional (or high order) networks. Nevertheless, it is well-known that the aggregation process may create spurious paths on the aggregated view......
An analysis of malaria in the Brazilian Legal Amazon using divergent association rules

03-08-2020

Lais Baroni, Rebecca Salles, Samella Salles, Gustavo Guedes, Fabio Porto, Eduardo Bezerra, Christovam Barcellos, Marcel Pedroso, Eduardo Ogasawara

Abstract:

In data analysis, the mining of frequent patterns plays an important role in the discovery of associations and correlations between data. During this process, it is common to produce thousands of association rules (ARs), making the study of each one arduous. This problem weakens the process of finding useful information. There is a scientific......
CoVeC: Coarse-Grained Vertex Clustering for Efficient Community Detection in Sparse Complex Networks

01-06-2020

Gustavo S. Carnivali, Alex B. Vieira, Artur Ziviani, Paulo A. A. Esquef.

Abstract:

This paper tackles the problem of community detection in large-scale graphs. In the literature devoted to this topic, an iterative algorithm, called Louvain Method (LM), stands out as an effective and fast solution for this problem. However, the first iterations of the LM are the most costly. To overcome this issue, this paper introduces......
BioinfoPortal: A scientific gateway for integrating bioinformatics applications on the Brazilian national high-performance computing network

01-06-2020

Kary A.C.S.Ocaña, Marcelo Galheigo,Carla Osthoff, Luiz M.R. Gadelha Jr., Fabio Porto, Antônio Tadeu A.Gomes, Daniel de Oliveira, Ana Tereza Vasconcelosa

Abstract:

Science gateways have gained increasing attention in the last years from diverse communities. Science gateways are software solutions that bring out the integration of reusable data and specialized techniques via Web servers while hiding the complexity of the underlying high-performance computing resources. Several projects and initiatives have been started worldwide to develop frameworks that......
Projection of hospitalization by COVID-19 in Brazil following different social distance policies

15-05-2020

Henrique Matheus Ferreira da Silva, Rafael Silva Pereira, Raquel de Abreu Junqueira Gritz, Adolfo Simões, Fabio Porto

Abstract:

Following the first infections in the Wuhan City, COVID-19 became a global pandemic as declared by the World Health Organization (WHO). Since it is an airborne disease transmitted between humans, many countries adopted a quarantine on their own population as well as closing borders measures. In Brazil many are discussing the best way to......
SUQ2 : Uncertainty Quantification Queries over Large Spatio-temporal Simulations

03-04-2020

Noel Moreno Lemus, Fabio Porto, Yania M. Souto, Rafael S. Pereira, Ji Liu, Esther Pacciti, and Patrick Valduriez

Abstract:

The combination of high-performance computing towards Exascale power and numerical techniques enables exploring complex physical phenomena using large-scale spatio-temporal modeling and simulation. The improvements on the fidelity of phenomena simulation require more sophisticated uncertainty quantification analysis, leaving behind measurements restricted to low order statistical moments and moving towards more expressive probability density functions models......
Efficient Network Seeding under Variable Node Cost and Limited Budget for Social Networks

01-04-2020

R. C. Souza, D. R. Figueiredo, A. A. A. Rocha, Artur Ziviani

Abstract:

The efficiency of information diffusion on networks highly depends on both the network structure and the set of early spreaders. Moreover, in various realistic scenarios, to seed different nodes implies different costs, as in the case of viral marketing, where costs often correlate with local network structure. The budgeted influence maximization (BIM) problem consists......
Parallel computation of PDFs on big spatial data using Spark

02-03-2020

Ji Liu, Noel Moreno Lemus, Esther Pacitti, Fábio Porto, Patrick Valduriez

Abstract:

We consider big spatial data, which is typically produced in scientific areas such as geological or seismic interpretation. The spatial data can be produced by observation (e.g. using sensors or soil instruments) or numerical simulation programs and correspond to points that represent a 3D soil cube area. However, errors in signal processing and modeling......
New perspectives on analysing data from biological collections based on social network analytics

01-02-2020

P. C. Siracusa, Luiz M. R. Gadelha Jr., Artur Ziviani

Abstract:

Biological collections have been historically regarded as fundamental sources of scientific information on biodiversity. They are commonly associated with a variety of biases, which must be characterized and mitigated before data can be consumed. In this work, we are motivated by taxonomic and collector biases, which can be understood as the effect of particular......

2019

Towards Optimizing the Execution of Spark Scientific Workflows Using Machine Learning based Parameter Tuning

27-02-2019

Douglas de Oliveira, Fábio Porto, Cristina Boeres, Daniel de Oliveira

Abstract:

In the last few years, Apache Spark has become de facto the standard of big data framework on both industry and academy projects. Especially in the scientific domain, it is already used to execute compute- and data-intensive workflows from biology to astronomy. Although Spark is an easy-to-install framework, it has more than one hundred......
Graph-Based Skill Acquisition for Reinforcement Learning

12-02-2019

Matheus R. F. Mendonça, Artur Ziviani, André M. S. Barreto

Abstract:

In machine learning, Reinforcement Learning (RL) is an important tool for creating intelligent agents that learn solely through experience. One particular subarea within the RL domain that has received great attention is how to define macro-actions, which are temporal abstractions composed of a sequence of primitive actions. This subarea, loosely called skill acquisition, has......

2018

Understanding Human Mobility and Workload Dynamics Due To Different Large-Scale Events Using Mobile Phone Data

16-10-2018

Humberto T. Marques-Neto, Faber H. Z. Xavier, Wender Z. Xavier, Carlos Henrique S. Malab, Artur Ziviani, Lucas M. Silveira & Jussara M. Almeida

Abstract:

The analysis of mobile phone data can help carriers to improve the way they deal with unusual workloads imposed by large-scale events. This paper analyzes human mobility and the resulting dynamics in the network workload caused by three different types of large-scale events: a major soccer match, a rock concert, and a New Year’s......
BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments

14-06-2018

Maria Luiza Mondelli, Thiago Magalhães, Guilherme Loss, Michael Wilde, Ian Foster, Marta Mattoso, Daniel Katz, Helio Barbosa, Ana Tereza R. de Vasconcelos, Kary Ocaña, Luiz M.R. Gadelha Jr​

Abstract:

Advances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments. Because these experiments are computation- and data-intensive, they require high-performance computing techniques and can benefit from specialized technologies such as Scientific Workflow Management Systems and databases. In this work, we present BioWorkbench, a framework for......

2017

GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis

13-06-2017

Raquel L. Costa​, Luiz Gadelha, Marcelo Ribeiro-Alves, Fábio Porto

Abstract:

There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions,......

2016

MobHet: Predicting Human Mobility Using Heterogeneous Data Sources

12-12-2016

Lucas M. Silveira, Jussara M. de Almeida, Humberto T. Marques-Neto, Carlos Sarraute, Artur Ziviani

Abstract:

The literature is rich in mobility models that aim at predicting human mobility. Yet, these models typically consider only a single kind of data source, such as data from mobile calls or location data obtained from GPS and web applications. Thus, the robustness and effectiveness of such data-driven models from the literature remain unknown......
On MultiAspect Graphs

12-10-2016

Klaus Wehmuth, Éric Fleury, Artur Ziviani

Abstract:

Different graph generalizations have been recently used in an ad-hoc manner to represent multilayer networks, i.e. systems formed by distinct layers where each layer can be seen as a network. Similar constructions have also been used to represent time-varying networks. We introduce the concept of MultiAspect Graph (MAG) as a graph generalization that we......
A note on the complexity of the causal ordering problem

04-07-2016

Bernardo Gonçalves, Fabio Porto

Abstract:

In this note we provide a concise report on the complexity of the causal ordering problem, originally introduced by Simon to reason about causal dependencies implicit in systems of mathematical equations. We show that Simon's classical algorithm to infer causal ordering is NP-Hard—an intractability previously guessed but never proven. We present then a detailed......

2015

Managing Scientific Hypotheses as Data with Support for Predictive Analytics

18-08-2015

Bernardo Gonçalves, Fábio Porto

Abstract:

The sheer scale of high-resolution raw data generated by simulation has motivated nonconventional approaches for data exploration, referred to as immersive and in situ query processing. Another step toward supporting scientific progress is to enable data-driven hypothesis management and predictive analytics out of simulation results. The authors of this article present a synthesis method......
BaMBa: towards the integrated management of Brazilian marine environmental data

16-06-2015

Pedro Milet Meirelles, Luiz M. R. Gadelha, Jr, Ronaldo Bastos Francini-Filho, Rodrigo Leão de Moura, Gilberto Menezes Amado-Filho, Alex Cardoso Bastos, Rodolfo Pinheiro da Rocha Paranhos, Carlos Eduardo Rezende, Jean Swings, Eduardo Siegle, Nils Edvin Asp Neto, Sigrid Neumann Leitão, Ricardo Coutinho, Marta Mattoso, Paulo S. Salomon, Rogério A.B. Valle, Renato Crespo Pereira, Ricardo Henrique Kruger, Cristiane Thompson, Fabiano L. Thompson

Abstract:

A new open access database, Brazilian Marine Biodiversity (BaMBa) (https://marinebiodiversity.lncc.br), was developed in order to maintain large datasets from the Brazilian marine environment. Essentially, any environmental information can be added to BaMBa. Certified datasets obtained from integrated holistic studies, comprising physical–chemical parameters, -omics, microbiology, benthic and fish surveys can be deposited in the new......

2014

Y-DB: Managing scientific hypotheses as uncertain data

13-08-2014

Bernardo Gonçalves, Fábio Porto

Abstract:

In view of the paradigm shift that makes science ever more data-driven, we consider deterministic scientific hypotheses as uncertain data. This vision comprises a probabilistic database (p-DB) design methodology for the systematic construction and management of U-relational hypothesis DBs, viz., Υ-DBs. It introduces hypothesis management as a promising new class of applications for p-DBs.......
Applying Provenance to Protect Attribution in Distributed Computational Scientific Experiments. In Provenance and Annotation of Data and Processes

11-06-2014

Luiz M. R. Gadelha Jr., Marta Mattoso

Abstract:

The automation of large scale computational scientific experiments can be accomplished with the use of scientific workflow management systems, which allow for the definition of their activities and data dependencies. The manual analysis of the data resulting from their execution is burdensome, due to the usually large amounts of information. Provenance systems can be......

2012

MTCProv: a practical provenance query framework for many-task scientific computing

13-06-2012

Luiz M. R. Gadelha Jr., Michael Wilde, Marta Mattoso & Ian Foster

Abstract:

Scientific research is increasingly assisted by computer-based experiments. Such experiments are often composed of a vast number of loosely-coupled computational tasks that are specified and automated as scientific workflows. This large scale is also characteristic of the data that flows within such “many-task” computations (MTC). Provenance information can record the behavior of such computational......

2008

A Conceptual View on Trajectories

02-01-2008

Stefano Spaccapietra, Christine Parent, Maria Luiza Damiani, José Antônio F. Macedo, Fábio Porto, Christelle Vangenot

Abstract:

Stefano Analysis of trajectory data is the key to a growing number of applications aiming at global understanding and management of complex phenomena that involve moving objects (e.g. worldwide courier distribution, city traffic management, bird migration monitoring). Current DBMS support for such data is limited to the ability to store and query raw movement......