Institute of Computing · Universidade Federal Fluminense · Niterói, Brazil 📧 danielcmo@ic.uff.br · 🌐 github.com/UFFeScience
The eScience Research Group at the Institute of Computing of the Fluminense Federal University (IC/UFF) develops open-source tools, middleware, and frameworks focused on scientific workflows, provenance data management, high-performance computing, and distributed systems. Our work spans from containerized workflow orchestration to deep learning provenance, serving both academic and industrial research communities.
🔷 AkôFlow
Open-source middleware for orchestrating and executing container-based scientific workflows across heterogeneous environments.
AkôFlow supports execution on Kubernetes clusters (AWS EKS, GCP GKE, Azure AKS), Singularity for HPC isolated environments, and the SDumont supercomputer at LNCC (Brazil). Originally started as a final undergraduate project, it has grown into a production-ready workflow engine actively maintained by the group.
- Language: Go · Stars: ⭐ 63 · Latest release: v0.5.1
- Install:
curl -fsSL https://akoflow.com/run | bash - Docs: uffescience.github.io/akoflow
🔷 SAMbA
Extending Apache Spark for Scientific Computational Experiments.
SAMbA adds provenance-aware capabilities to Apache Spark, enabling scientists to track and analyze data transformations throughout large-scale computational experiments.
- Language: Scala · Stars: ⭐ 16
🔷 DLProv
Provenance data integration service for Deep Learning workflows.
Evolved from DNNProv, DLProv supports online hyperparameter analysis and retrospective provenance capture across the full deep learning lifecycle — from data pre-processing through model training and evaluation. It integrates with MonetDB for online analysis and Neo4j for W3C PROV-compliant graph queries.
- Language: Python · Stars: ⭐ 15
- Docker:
docker pull dbpina/dlprov
Workflow Engine for scientific experiments in cloud environments.
SciCumulus is a cloud-based scientific workflow engine designed to manage and execute parameter sweep experiments across distributed resources.
- Language: Java · Stars: ⭐ 14
Management system for phenotyping experiments.
A platform for organizing, tracking, and analyzing data from phenotyping experiments in agricultural and biological research.
- Language: CSS/Web · Stars: ⭐ 13
🔷 Denethor
Serverless scientific workflow analysis and provenance tool.
Denethor focuses on provenance data collection and analysis for scientific workflows executed in serverless computing environments.
- Language: Python · Stars: ⭐ 13 · License: GPL-3.0
The group maintains a full ecosystem of repositories around AkôFlow:
| Repository | Description |
|---|---|
| akoflow | Core workflow engine (Go) |
| akoflow-deployment-control-plane | Deployment control plane backend (PHP) |
| akoflow-deployment-control-plane-ui | Control plane frontend UI (TypeScript) |
| akoflow-driver-s3-uploader | AWS S3 storage driver (Python) |
| akoflow-driver-gcs-uploader | Google Cloud Storage driver (Python) |
| akoflow-driver-dataverse-uploader | Dataverse repository driver (Python) |
| akoflow-example-wf-etl-clothing | Example ETL workflow (Python) |
- Scientific Workflow Management Systems (WfMS)
- Provenance Data Capture & Analysis
- High-Performance Computing (HPC) & Supercomputing
- Containerized & Serverless Computing
- Deep Learning Lifecycle Management
- Data-Centric AI & Reproducibility
- Cloud & Kubernetes Orchestration
- Ferreira, W. et al. (2024). AkôFlow: um Middleware para execução de Workflows científicos em múltiplos ambientes conteinerizados. SBBD 2024. DOI:10.5753/sbbd.2024.241126
- Ferreira, W. et al. (2025). Plug and Flow: Execução de Workflows Científicos em Contêineres com o Middleware AkôFlow. SBBD 2025. (accepted)
- Pina, D. et al. (2024). DLProv: A Data-Centric Support for Deep Learning Workflow Analyses. DEEM @ SIGMOD 2024. ACM DL
- Pina, D. et al. (2023). Deep learning provenance data integration: a practical approach. WWW Companion 2023. ACM DL
- de Oliveira, L.S. et al. (2023). PINNProv: Provenance for Physics-Informed Neural Networks. SBAC-PADW 2023. IEEE
| Name | Role | Affiliation |
|---|---|---|
| D.Sc. Daniel de Oliveira | Research Advisor | IC/UFF |
| Wesley Ferreira (@ovvesley) | Maintainer (AkôFlow) | IC/UFF |
| Liliane Kunstmann | Researcher | COPPE/UFRJ |
| Debora Pina | Researcher | COPPE/UFRJ |
| Raphael Garcia | Researcher | IC/UFF |
| Yuri Frota | Collaborator | IC/UFF |
| Marcos Bedo | Collaborator | IC/UFF |
| Aline Paes | Collaborator | IC/UFF |
| Luan Teylo | Collaborator | INRIA / Univ. de Bordeaux |
We welcome contributions, collaborations, and feedback from the community.
- 📂 Browse our 68+ repositories
- 🐛 Open issues on individual project repositories
- 📧 Contact the group at danielcmo@ic.uff.br
- 🎥 Watch the AkôFlow demo video (Portuguese)
eScience Research Group · Institute of Computing · Universidade Federal Fluminense (UFF) · Brazil