@article{azevedo2023polystore, title={A Polystore Architecture Using Knowledge Graphs to Support Queries on Heterogeneous Data Stores}, author={Azevedo, Leonardo Guerreiro and Souza, Renan and Soares, Elton F de S and Thiago, Raphael M and Tesolin, Julio Cesar Cardoso and Oliveira, Ann C and Moreno, Marcio Ferreira}, journal={arXiv preprint Databases (cs.DB)}, doi={10.48550/arXiv.2308.03584}, url={https://arxiv.org/abs/2308.03584}, pdf={https://arxiv.org/pdf/2308.03584}, year={2023} } @article{asouza2020workflow, title={Workflow Provenance in the Lifecycle of Scientific Machine Learning}, author={Souza, Renan and G. Azevedo, Leonardo and Lourenço, Vítor and Soares, Elton and Thiago, Raphael and Brandão, Rafael and Civitarese, Daniel and Vital Brazil, Emilio and Moreno, Marcio and Valduriez, Patrick and Mattoso, Marta and Cerqueira, Renato and A. S. Netto, Marco}, year={2021}, abstract={Machine Learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting the computational science and engineering domains, like geoscience, climate science, and health science. In these domains, users need to perform comprehensive data analyses combining scientific data and ML models to provide for critical requirements, such as reproducibility, model explainability, and experiment data understanding. However, scientific ML is multidisciplinary, heterogeneous, and affected by the physical constraints of the domain, making such analyses even more challenging. In this work, we leverage workflow provenance techniques to build a holistic view to support the lifecycle of scientific ML. We contribute with (i) characterization of the lifecycle and taxonomy for data analyses; (ii) design principles to build this view, with a W3C PROV compliant data representation and a reference system architecture; and (iii) lessons learned after an evaluation in an Oil \& Gas case using an HPC cluster with 393 nodes and 946 GPUs. The experiments show that the principles enable queries that integrate domain semantics with ML models while keeping low overhead (<1\%), high scalability, and an order of magnitude of query acceleration under certain workloads against without our representation.}, url={https://doi.org/10.1002/cpe.6544}, pdf={https://arxiv.org/pdf/2010.00330.pdf}, journal={Concurrency and Computation: Practice and Experience}, pages={1--21}, volume={e6544} } @article{da2023workflows, title={Workflows Community Summit 2022: A Roadmap Revolution}, author={da Silva, Rafael Ferreira and Badia, Rosa M and Bala, Venkat and Bard, Debbie and Bremer, Peer-Timo and Buckley, Ian and Caino-Lores, Silvina and Chard, Kyle and Goble, Carole and Jha, Shantenu and ... and Souza, Renan and {et al.}}, journal={arXiv preprint Distributed, Parallel, and Cluster Computing (cs.DC)}, doi={10.48550/arXiv.2304.00019}, url={https://arxiv.org/abs/2304.00019}, pdf={https://arxiv.org/pdf/2304.00019}, year={2023} } @article{souza_distributed_2021, author={Souza, R. and Silva, V. and Lima, A. A. B. and Oliveira, D. and Valduriez, P. and Mattoso, M.}, journal={PeerJ Computer Science}, title={Distributed In-memory Data Management for Workflow Executions}, year={2021}, pdf={https://arxiv.org/ftp/arxiv/papers/2105/2105.04720.pdf}, url={https://peerj.com/articles/cs-527/}, doi={10.7717/peerj-cs.527}, pages={1--30}, volume={7}, abstract={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 such executions usually last for hours or days, some WMSs provide user steering support, i.e., they allow users to run data analyses and, depending on the results, adapt the workflows at runtime. A challenge in the parallel execution control design is to manage workflow data for efficient executions while enabling user steering support. Data access for high scalability is typically transaction-oriented, while for data analysis, it is online analytical-oriented so that managing such hybrid workloads makes the challenge even harder. In this work, we present SchalaDB, an architecture with a set of design principles and techniques based on distributed in-memory data management for efficient workflow execution control and user steering. We propose a distributed data design for scalable workflow task scheduling and high availability driven by a parallel and distributed in-memory DBMS. To evaluate our proposal, we develop d-Chiron, a WMS designed according to SchalaDB's principles. We carry out an extensive experimental evaluation on an HPC cluster with up to 960 computing cores. Among other analyses, we show that even when running data analyses for user steering, SchalaDB's overhead is negligible for workloads composed of hundreds of concurrent tasks on shared data. Our results encourage workflow engine developers to follow a parallel and distributed data-oriented approach not only for scheduling and monitoring but also for user steering.} } @inproceedings{rafael_2021_wf_summit, title={Workflows Community Summit: Advancing the State-of-the-art of Scientific Workflows Management Systems Research and Development}, author={da Silva, Rafael Ferreira and Casanova, Henri and Chard, Kyle and ... and Souza, Renan and {et al.}}, journal={arXiv preprint Distributed, Parallel, and Cluster Computing (cs.DC)}, pdf = {https://arxiv.org/pdf/2106.05177.pdf}, url = {https://arxiv.org/abs/2106.05177}, pages={1--24}, year={2021} } @article{azevedo2020adding, title={Adding Hyperknowledge-enabled data lineage to a machine learning workflow management system for oil and gas}, author={Azevedo, Leonardo Guerreiro and Souza, Renan and Brandão, Rafael and Lourenço, Vítor N and Costalonga, Marcelo and de Machado, Marcelo and Moreno, Marcio and Cerqueira, Renato}, journal={First Break}, volume={38}, number={7}, pages={89--93}, year={2020}, publisher={European Association of Geoscientists \& Engineers}, doi = {10.3997/1365-2397.fb2020055} } @article{Souza2017Data, title = {Data Reduction in Scientific Workflows Using Provenance Monitoring and User Steering}, volume = {110}, pdf = {https://hal-lirmm.ccsd.cnrs.fr/lirmm-01679967/document}, issn = {0167-739X}, doi = {10.1016/j.future.2017.11.028}, author = {Souza, Renan and Silva, Vítor and Coutinho, Alvaro L. G. A. and Valduriez, Patrick and Mattoso, Marta}, journal = {Future Generation Computer Systems}, pages = {481--501}, keyword = {Scientific Workflows, Human in the Loop, Online Data Reduction, Provenance Data, Dynamic Workflows}, year = {2017}, abstract = {Scientific workflows need to be iteratively, and often interactively, executed for large input datasets. Reducing data from input datasets is a powerful way to reduce overall execution time in such workflows. When this is accomplished online (i.e., without requiring the user to stop execution to reduce the data, and then resume), it can save much time. However, determining which subsets of the input data should be removed becomes a major problem. A related problem is to guarantee that the workflow system will maintain execution and data consistent with the reduction. Keeping track of how users interact with the workflow is essential for data provenance purposes. In this paper, we adopt the “human-in-the-loop” approach, which enables users to steer the running workflow and reduce subsets from datasets online. We propose an adaptive workflow monitoring approach that combines provenance data monitoring and computational steering to support users in analyzing the evolution of key parameters and determining the subset of data to remove. We extend a provenance data model to keep track of users’ interactions when they reduce data at runtime. In our experimental validation, we develop a test case from the oil and gas domain, using a 936-cores cluster. The results on this test case show that the approach yields reductions of 32\% of execution time and 14\% of the data processed.} } @article{souza_keeping_2019, title = {Keeping Track of User Steering Actions in Dynamic Workflows}, volume = {99}, issn = {0167-739X}, pdf = {https://hal-lirmm.ccsd.cnrs.fr/lirmm-02127456/document}, doi = {10.1016/j.future.2019.05.011}, url = {https://doi.org/10.1016/j.future.2019.05.011}, pages = {624--643}, journal = {Future Generation Computer Systems}, author = {Souza, Renan and Silva, Vítor and Camata, Jose J. and Coutinho, Alvaro L. G. A. and Valduriez, Patrick and Mattoso, Marta}, year = {2019}, keyword = {Dynamic workflows, Computational steering, Provenance data, Parameter tuning}, abstract = {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 are done through user steering actions that may significantly improve performance (e.g., reduce execution time) or improve the overall results. However, in executions that last for weeks, users can lose track of what has been adapted if the tunings are not properly registered. In this work, we build on provenance data management to address the problem of tracking online parameter fine-tuning in dynamic workflows steered by users. We propose a lightweight solution to capture and manage provenance of the steering actions online with negligible overhead. The resulting provenance database relates tuning data with data for domain, dataflow provenance, execution, and performance, and is available for analysis at runtime. We show how users may get a detailed view of the execution, providing insights to determine when and how to tune. We discuss the applicability of our solution in different domains and validate its ability to allow for online capture and analyses of parameter fine-tunings in a real workflow in the Oil and Gas industry. In this experiment, the user could determine which tuned parameters influenced simulation accuracy and performance. The observed overhead for keeping track of user steering actions at runtime is less than 1\% of total execution time.} } @article{silva_adding_2018, title = {Adding Domain Data to Code Profiling Tools to Debug Workflow Parallel Execution}, issn = {0167-739X}, doi = {10.1016/j.future.2018.05.078}, author = {Silva, Vítor and Neves, Leonardo and Souza, Renan and Coutinho, Alvaro L. G. A. and de Oliveira, Daniel and Mattoso, Marta}, journal = {Future Generation Computer Systems}, year = {2018}, pages = {624--643}, keyword = {Scientific workflow, Debugging, Provenance, Performance analysis} } @article{de2017hybrid, title={A Hybrid Architecture for Multi-party Conversational Systems}, author={de Bayser, Maira Gatti and Cavalin, Paulo and Souza, Renan and Braz, Alan and Candello, Heloisa and Pinhanez, Claudio and Briot, Jean-Pierre}, journal={arXiv preprint Computation and Language (cs.CL)}, pdf = {https://arxiv.org/pdf/1705.01214.pdf}, url = {https://arxiv.org/abs/1705.01214}, pages={1--40}, year={2017} }