Highlitghts Publications

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

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

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......

2021

DJEnsemble: a Cost-Based Selection and Allocation of a Disjoint Ensemble of Spatio-temporal Models

15-07-2021

Rafael S. Pereira, Yania Molina Souto, Anderson Silva, Rocio Zorrilla, Brian Tsan, Florin Rusu, Eduardo Ogasawara, Arthur Ziviani, Fabio Porto

Consider a set of black-box models – each of them independently trained on a different dataset – answering the same predictive spatio-temporal query. Being built in isolation, each model traverses its own life-cycle until it is deployed to production, learning data patterns from different datasets and facing independent hyper-parameter tuning. In order to answer......

2020

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

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......

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

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......

2019

Graph-Based Skill Acquisition for Reinforcement Learning

12-02-2019

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

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

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......

Point pattern search in big data

09-07-2018

Fabio Porto, João N. Rittmeyer, Eduardo Ogasawara, Alberto Krone-Martins, Patrick Valduriez, Dennis Shasha

Consider a set of points P in space with at least some of the pairwise distances specified. Given this set P, consider the following three kinds of queries against a database D of points : (i) pure constellation query: find all sets S in D of size |P| that exactly match the pairwise distances......

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

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

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

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

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......

Database System Support of Simulation Data

01-09-2016

Hermano Lustosa, Fabio Porto, Pablo Blanco, Patrick Valduriez

Supported by increasingly efficient HPC infra-structure, numerical simulations are rapidly expanding to fields such as oil and gas, medicine and meteorology. As simulations become more precise and cover longer periods of time, they may produce files with terabytes of data that need to be efficiently analyzed. In this paper, we investigate techniques for managing......

2015

A Unifying Model for Representing Time-Varying Graphs

14-10-2015

Klaus Wehmuth, Artur Ziviani, Eric Fleury

Graph-based models form a fundamental aspect of data representation in Data Sciences and play a key role in modeling complex networked systems. In particular, recently there is an ever-increasing interest in modeling dynamic complex networks, i.e. networks in which the topological structure (nodes and edges) may vary over time. In this context, we propose......

Managing Scientific Hypotheses as Data with Support for Predictive Analytics

18-08-2015

Bernardo Gonçalves, Fábio Porto

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

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......

2008

A Conceptual View on Trajectories

02-01-2008

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

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......