138 citations · 288 across the 18 of their papers we have counts for
19 papers
StreamEnsemble: Predictive Queries over Spatiotemporal Streaming Data
Anderson Chaves, Eduardo Ogasawara, Patrick Valduriez +1
Predictive queries over spatiotemporal (ST) stream data pose significant data processing and analysis challenges. ST data streams involve a set of time series whose data distributi…
AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices
Ji Liu, Tianshi Che, Yang Zhou +4
Federated Learning (FL) has achieved significant achievements recently, enabling collaborative model training on distributed data over edge devices. Iterative gradient or model exc…
KheOps: Cost-effective Repeatability, Reproducibility, and Replicability of Edge-to-Cloud Experiments
Daniel Rosendo, Kate Keahey, Alexandru Costan +3
Distributed infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex scientific workflows to be executed across hybrid sy…
ProvLight: Efficient Workflow Provenance Capture on the Edge-to-Cloud Continuum
Daniel Rosendo, Marta Mattoso, Alexandru Costan +4
Modern scientific workflows require hybrid infrastructures combining numerous decentralized resources on the IoT/Edge interconnected to Cloud/HPC systems (aka the Computing Continu…
Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resources
Ji Liu, Daxiang Dong, Xi Wang +5
Although more layers and more parameters generally improve the accuracy of the models, such big models generally have high computational complexity and require big memory, which ex…
Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review
Daniel Rosendo, Alexandru Costan, Patrick Valduriez +1
The explosion of data volumes generated by an increasing number of applications is strongly impacting the evolution of distributed digital infrastructures for data analytics and ma…
Enabling Reproducible Analysis of Complex Workflows on the Edge-to-Cloud Continuum
Daniel Rosendo, Alexandru Costan, Gabriel Antoniu +1
Distributed digital infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex applications to be executed from IoT Edge de…
Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum
Daniel Rosendo, Alexandru Costan, Gabriel Antoniu +4
In more and more application areas, we are witnessing the emergence of complex workflows that combine computing, analytics and learning. They often require a hybrid execution infra…
Distributed In-memory Data Management for Workflow Executions
Renan Souza, Vítor Silva, Alexandre A. B. Lima +3
Complex scientific experiments from various domains are typically modeled as workflows and executed on large-scale machines using a Parallel Workflow Management System (WMS). Since…
Hyperspherical embedding for novel class classification
Rafael S. Pereira, Alexis Joly, Patrick Valduriez +1
Deep learning models have become increasingly useful in many different industries. On the domain of image classification, convolutional neural networks proved the ability to learn…
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,…
Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering
Renan Souza, Leonardo Azevedo, Vítor Lourenço +10
Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of…
Keeping Track of User Steering Actions in Dynamic Workflows
Renan Souza, Vítor Silva, José Camata +3
In long-lasting scientific workflow executions in HPC machines, computational scientists (the users in this work) often need to fine-tune several workflow parameters. These tunings…
Parallel Computation of PDFs on Big Spatial Data Using Spark
Ji Liu, Noel Moreno Lemus, Esther Pacitti +2
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. us…
Constellation Queries over Big Data
Fabio Porto, Amir Khatibi, João R. Nobre +3
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 spatia…
On Demand Memory Specialization for Distributed Graph Databases
Xavier Martinez-Palau, David Dominguez-Sal, Reza Akbarinia +2
In this paper, we propose the DN-tree that is a data structure to build lossy summaries of the frequent data access patterns of the queries in a distributed graph data management s…
Public Data Integration with WebSmatch
R. Coletta, E. Castanier, P. Valduriez +3
Integrating open data sources can yield high value information but raises major problems in terms of metadata extraction, data source integration and visualization of integrated da…
Principles of Distributed Data Management in 2020?
Patrick Valduriez
With the advents of high-speed networks, fast commodity hardware, and the web, distributed data sources have become ubiquitous. The third edition of the Özsu-Valduriez textbook Pri…
Reducing Network Traffic in Unstructured P2P Systems Using Top-k Queries
Reza Akbarinia, Esther Pacitti, Patrick Valduriez
A major problem of unstructured P2P systems is their heavy network traffic. This is caused mainly by high numbers of query answers, many of which are irrelevant for users. One solu…