Conferences & Workshops

2024

Subset Modelling: A Domain Partitioning Strategy for Data-efficient Machine-Learning

27-11-2024

Vítor Ribeiro, Eduardo H. M. Pena, Raphael de Freitas Saldanha, Reza Akbarinia, Patrick Valduriez, Falaah Arif Khan, Julia Stoyanovich, Fábio Porto

Abstract:

The success of machine learning (ML) systems depends on data availability, volume, quality, and efficient computing resources. A challenge in this context is to reduce computational costs while maintaining adequate accuracy of the models. This paper presents a framework to address this challenge. The idea is to identify “subdomains” within the input space, train......
A Digital Twin System for Oil And Gas Industry: A Use Case on Mooring Lines Integrity Monitoring

23-09-2024

Vinicius Kreischer de Almeida, Douglas Ericson Marcelino de Oliveira, Claudio Daniel Tenorio de Barros, Gabriel dos Santos Scatena, Asdrubal N. Queiroz Filho, Fábio Levy Siqueira, Anna Helena Reali Costa, Edson Satoshi Gomi, Leonardo A. F. Mendoza, Evelyn Conceição Santos Batista, Cristian E. Muñoz, Isabela Guimarães Siqueira, Rodrigo A. Barreira, Ismael H. F. dos Santos, Carlos Cardoso, Eduardo S. Ogasawara, Fabio Porto

Abstract:

A Digital Twin is a virtual representation of a real-world object or process, leveraging powerful computational architectures available both on-premises and in the cloud. By harnessing the increased availability of real-time data and advancements in machine learning predictive algorithms, Digital Twins find applications across various domains such as Earth Science, Oil and Gas, and......
REMD: A Novel Hybrid Anomaly Detection Method Based on EMD and ARIMA

30-06-2024

Jéssica Souza, Ellen Paixão, Fernando Fraga, Lais Baroni, Ronaldo Fernandes Santos Alves, Kele T. Belloze, Joel dos Santos, Eduardo Bezerra, Fábio Porto, Eduardo S. Ogasawara

Abstract:

Anomalies are defined as behavioral deviations from expected patterns and pose challenges to identify them. Anomaly detection is a fundamental activity of time series analysis. It enables informed decision-making in many control and monitoring activities, such as healthcare, water quality, seismic reflection analysis, and oil exploration. Many anomaly detection methods exist, but choosing the......
Online Event Detection in Streaming Time Series: Novel Metrics and Practical Insights

12-06-2024

Janio Lima, Lucas Giusti Tavares, Esther Pacitti, João Eduardo Ferreira, Ismael Santos, Isabela Guimarães Siqueira, Diego Carvalho, Fábio Porto, Rafaelli Coutinho, Eduardo S. Ogasawara

Abstract:

Online event detection in streaming time series is a critical task with applications across various domains. For example, the right-on-time event detection for control systems is a key for correctly addressing the issues related to the events. However, events may not be identified right after their occurrence. Depending on the monitoring solution, a time......
Discovering Denial Constraints in Dynamic Datasets

16-05-2024

Eduardo H. M. Pena; Fabio Porto; Felix Naumann

Abstract:

Denial constraints (DCs) are data dependencies with high expressive power, offering great flexibility for modeling data quality rules. Specifying DCs manually is problematic, as the required domain expertise is expensive and scarce. Moreover, database updates can invalidate DCs thought to hold and simultaneously uncover new DCs. This fact leads to burdensome scenarios where experts......
Subset Models for Multivariate Time Series Forecast

16-05-2024

Raphael Saldanha; Victor Ribeiro; Eduardo H. M. Pena; Marcel Pedroso; Reza Akbarinia; Patrick Valduriez; Fabio Porto

Abstract:

Multivariate time series find extensive applications in conjunction with machine learning methodologies for scenario forecasting across various domains. Nevertheless, certain domains exhibit inherent complexities and diversities, which detrimentally impact the predictive efficacy of global models. This ongoing study introduces a Subset Modeling Framework designed to acknowledge the inherent diversity within a domain’s multivariate space.......

2023

Data Science Platform Applied to Health in Contribution to the Brazilian Unified Health System

01-09-2023

Marcel Pedroso, Rebecca Salles, Raphael de Freitas Saldanha, Vinicius Kreischer de Almeida, Gabriel Souto, Balthazar Paixão, Sérgio Ricardo de Borba Cruz, Carlos Cardoso, Víctor Ribeiro, Raquel Gritz, Carmen Bonifácio, Matheus Miloski, Carlos Augusto de Sousa, Gizelton Pereira Alencar, Ariane Alves, Nelson Niero Neto, Letícia Sabbadini, Eduardo S. Ogasawara, Christovam Barcellos, Fábio Porto, Lucas Zinato Carraro, Jefferson Lima

Abstract:

The Data Science Platform Applied to Health (PCDaS) is a research and technological development project that aims to develop and apply novel data analysis methods to public health data. It fills a technological gap between the variety of data sources available in legacy and unstandardized formats and the current needs and possibilities of Data......

2022

A Data-Driven Model Selection Approach toSpatio-Temporal Prediction

22-09-2022

Rocío Zorrilla, Eduardo Ogasawara,Patrick Valduriez, F ́abio Porto

Abstract:

Spatio-temporal Predictive Queries encompass a spatio-temporalconstraint, defining a region, a target variable, and an evaluation metric. Theoutput of such queries presents the future values for the target variable computedby predictive models at each point of the spatio-temporal region. Unfortunately,especially for large spatio-temporal domains with millions of points, trainingtemporal models at each spatial domain point......

2021

Method for Treating Anomalies in Multivariate Time Series

15-11-2021

Thiago Moeda, Mariza Ferro, Eduardo Ogasawara, Fabio Porto

Abstract:

In recent years, the classification of time series has gained great relevance in significant sectors and segments of society. Machine Learning Techniques make it possible to interpret the behavior of anomalous phenomena in multivariate datasets. This work proposes a study of three methods from the perspective of their ability to provide relevant information for......
Managing Sparse Spatio-Temporal Data in SAVIME: An Evaluation of the Ph-tree Index

29-09-2021

Stiw Herrera, Larissa Miguez da Silva, Paulo Ricardo Reis, Anderson Silva, Fabio Porto

Abstract:

Scientific data is mainly multidimensional in its nature, presenting interesting opportunities for optimizations when managed by array databases. However, in scenarios where data is sparse, an efficient implementation is still required. In this paper, we investigate the adoption of the Ph-tree as an in-memory indexing structure for sparse data. We compare the performance in......
Requirements for an Ontology of Digital Twins

06-09-2021

Claudio Barros, Rebecca Salles, Eduardo Ogasawara, Giancarlo Guizzardi, Fabio Porto

Abstract:

Digital twin connects concrete systems to digital representations, encoding the real world using software systems, tools and models. Therefore, digital twins should comprise abstractions, formal namings and definitions of categories, properties and relations between concepts, data and entities substantiating one, many or every element of some domain of interest. Considering the possible synergies between......
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

Abstract:

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

An Algorithmic Information Distortion in Multidimensional Networks Cria

12-09-2020

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

Abstract:

Network complexity, network information content analysis, and lossless compressibility of graph representations have been played an important role in network analysis and network modeling. As multidimensional networks, such as time-varying, multilayer, or dynamic multilayer networks, gain more relevancy in network science, it becomes crucial to investigate in which situations universal algorithmic methods based on......

2019

Machine Learning and Knowledge Graph Inference

07-10-2019

Daniel N. R. da Silva, Artur Ziviani, Fabio Porto

Abstract:

The increasing production and availability of massive and heterogeneous data bringforward challenging opportunities. Among them, the development of computing systemscapable of learning, reasoning, and inferring facts based on prior knowledge. In this sce-nario, knowledge bases are valuable assets for the knowledge representation and automa-ted reasoning of diverse application domains. Especially, inference tasks on knowledgegraphs......
SAVIME: A Database Management System for Simulation Data Analysis and Visualization

14-08-2019

Hermano Lustosa, Fabio Porto, Patrick Valduriez

Abstract:

Limitations in current DBMSs prevent their wide adoption in scientific applications. In order to make scientific applications 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......
A conceptual vision toward the management of Machine Learning models

14-08-2019

Daniel N. R. da Silva, Yania Souto, Adolfo Simões, Carlos Cardoso, João N. Rittmeyer, Hermano Lustosa, Luciana E. G. Vignoli, Rebecca Salles, Eduardo Ogasawara, Flavia C. Delicato, Paulo de F. Pires, Artur Ziviani and Fabio Porto

Abstract:

To turn big data into actionable knowledge, the adoption of machine learning (ML) methods has proven to be one of the de facto approaches. When elaborating an appropriate ML model for a given task, one typically builds many models and generates several data artifacts. Given the amount of information associated with the developed models......
Deep Learning Application for Plant Classification on Unbalanced Training Set

24-06-2019

Rafael Silva Pereira, Fábio Porto

Abstract:

Deep learning models expect a reasonable amount of training in- stances to improve prediction quality. Moreover, in classification problems, the occurrence of an unbalanced distribution may lead to a biased model. In this paper, we investigate the problem of species classification from plant images, where some species have very few image samples. We explore......
Dealing with categorical missing data using CleanerR

24-06-2019

Rafael Silva Pereira, Fábio Porto

Abstract:

Missing data is a common problem in the world of data analysis. They appear in datasets due to a multitude of reasons, from data integration to poor data input. When faced with the problem, the analyst must decide what to do with the missing data since its not always advisable to discard these values......
SDN-Based Architecture for Providing Quality of Service to High Performance Distributed Applications

01-06-2019

Alexandre T. Oliveira, Bruno J. C. A. Martins, Marcelo F. Moreno, Antônio Tadeu A. Gomes, Artur Ziviani, Alex Borges

Abstract:

The specification of quality of service (QoS) requirements in most of the existing networks is still challenging. In part, traditional network environments are limited by their high administrative cost, although software‐defined networks (SDNs), a newer network paradigm, simplify the management of the whole network infrastructure. In fact, SDN provides a simple way to effectively......

2018

Constellation Queries over Big Data

25-08-2018

Fabio Porto, Amir Khatibi, Joao N. Rittmeyer, Eduardo Ogasawara, Patrick Valduriez, Dennis Shasha

Abstract:

A geometrical pattern is a set of points with all pairwise distances (or, more generally, relative distances) specified. Finding matches to such patterns has applications to spatial data in seismic, astronomical, and transportation contexts. Finding geometric patterns is a challenging problem as the potential number of sets of elements that compose shapes is exponentially......
Point pattern search in big data

09-07-2018

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

Abstract:

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......

2017

TARS: An Array Model with Rich Semantics for Multidimensional Data

04-06-2017

Hermano Lustosa, Noel Lemus, Fabio Porto, Patrick Valduriez

Abstract:

Relational DBMSs have been shown to be inefficient for scientific data management. One main reason is the difficulty to represent arrays, which are frequently adopted as a data model for scientific datasets representation. Array DBMSs, e.g. SciDB, were proposed to bridge this gap, building on a native array representation. Unfortunately, important scientific applications, such......

2016

Database System Support of Simulation Data

01-09-2016

Hermano Lustosa, Fabio Porto, Pablo Blanco, Patrick Valduriez

Abstract:

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

Abstract:

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......