Data management systems, Infrastructure-based services, Computational optimization problems and virtual Environments in School of Computing at DePaul University, Chicago
The Data, Infrastructure, Computation, and Environments (DICE) Lab aims at expanding the understanding of issues relating to data, infrastructure, computational problems, and environments. The current focus is on provenance data, systems and infrastructure for computational reproducibility, optimization and decision problems arising within this data and systems, and exploration of a variety of virtual environments that are relevant for establishing computational reproducibility.
‘Querying Container Provenance’ accepted to ACM Theory and Practice of Provenance at ProvenanceWeek. Congratulations Aniket Modi and Moaz Reyad!
Our paper ‘IOSPReD: I/O Specialized Packaging of Reduced Datasets and Data-Intensive Applications for Efficient Reproducibility’ accepted to IEEE Access.
Yuta Nakamura presents ‘Provenance-based Workflow Diagnostics Using Program Specification’ at 29th IEEE International Conference on High Performance Computing, Data, and Analytics.
The first ACM Conference on Reproducibility and Replicability. Please consider contributing!
COV882: Resource Virtualization With Containers well-received at IIT, Delhi. Thanks to IIT, Delhi students and a wonderful experience!
‘Reproducible Notebook Containers using Application Virtualization’ wins nominated as the Best Student Paper at eScience’22! Congrats Raza Ahmad and Nithin Manne
‘Provenance-based Workflow Diagnostics Using Program Specification’ accepted to 29th IEEE International Conference on High Performance Computing, Data, and Analytics. Congrats Yuta Nakamura!
Nithin presents ‘CHEX: Multiversion Replay with Ordered Checkpoints’ at VLDB’22.
