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.
‘Differential Analysis for System Provenance’ accepted to PhD Symposium at 40th IEEE International Conference on Data Engineering (ICDE). Congratulations Yuta Nakamura!
‘Efficiently Reducing Storage Footprint in Reproducible Containers via I/O Specialization’ accepted to 24th IEEE/ACM international Symposium on Cluster, Cloud and Internet Computing (CCGrid). Congratulations Aniket Modi, Rohan Tikmany and Moaz Reyad!
‘Kondo: Efficient Provenance-driven Data Debloating’ accepted to 40th IEEE International Conference on Data Engineering (ICDE). Congratulations Aniket Modi and Rohan Tikmany!
‘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!
The First ACM Conference on Reproducibility and Replicability (ACM-REP) was a hugh success! See a summary here.
‘Towards Shareable and Reproducible Cloud Computing Experiments’ accepted to IEEE CloudSummit! Our vision of how to reproduce cloud-based experiments.
DICE Lab presents several posters and a talk on ‘Reproducible Notebook Containers’ at 10th Greater Chicago Area Systems Research Workshop (GCASR).
