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.
‘Reproducible eScience: The Data Containerization Challenge’ in Future of eScience track at IEEE eScience’23.
‘Efficient Differencing of System-level Provenance Graphs’ accepted to ACM International Conference of Knowledge Management (CIKM). Congratulations Yuta Nakamura!
‘Towards Shareable and Reproducible Cloud Computing Experiments’ accepted to IEEE CloudSummit! Our vision of how to reproduce cloud-based experiments.
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.