ProvenanceWeek will take place as a fully virtual event on July 19-22 2021. Following successful previous ProvenanceWeek events, this year’s installment will again co-locate the IPAW and TaPP workshops as well as host the first Workshop on Provenance and Visualization. IPAW and TaPP build on a successful history of provenance workshops that bring together researchers from a wide range of computer science fields including workflows, semantic web, databases, high performance computing, distributed systems, operating systems, programming languages, and software engineering, as well as researchers from other fields, such as biology and physics that have urgent provenance needs.
Because of the COVID 19 situation, ProvenanceWeek 2020 was held as a 1-day virtual event with brief teaser talks. ProvenanceWeek 2021 will bring together authors of the 2020 papers as well as co-locating the 2021 installment of TaPP and presentations by IPAW authors for a 2021 submission round.
While also virtual, the 2021 edition we will be a full 4 day event.
Provenance is increasingly important in data science, cloud computing, workflow systems, and many other areas. By providing a record of the data creation process and of dependencies between data, provenance information is essential for tracing errors in transformed data back to erroneous inputs, access control, auditing, repeatability and reproducibility, evaluating data quality, and establishing ownership of data.
- Vanessa Braganholo (
Universidade Federal Fluminense, Brazil) - IPAW PC chair
- Boris Glavic (
IIT, USA) - Senior PC chair
- David Koop (
Northern Illinois University, USA) - Demo/Poster Chair
- Thomas Moyer (
UNC Charlotte, USA) - General Chair
- Tanu Malik (
DePaul University, USA) - TaPP PC co-chair
- Thomas Pasquier (
University of Bristol, UK) - TaPP PC co-chair
We are glad to announce that Hazeline Asuncion will be TaPP’s keynote speaker. Hazeline Asuncion is an Associate Professor at the University of Washington Bothell. Her research focuses on traceability of data that may be found in different file types, locations, and owner groups. In the domain of software engineering, software traceability aids in various development tasks, such as system comprehension, system debugging, and communication between various stakeholders. In the domain of eScience, tracing how a dataset arrived at its current state, referred to as data provenance, is necessary in assessing a dataset’s integrity and in supporting repeatability of analyses or experiments. She has published over 30 peer-reviewed papers spanning these two topics. Her work has been funded by the National Science Foundation, including NSF REUs and an NSF Career. She received her Ph.D., M.S., and B.S., in Information and Computer Science from the University of California, Irvine.
The proceedings for IPAW have been published by Springer since 2008. You can find them here: https://link.springer.com/conference/ipaw
Due to the uncertainty of the COVID-19 pandemic, we have made the decition to conduct Provenance Week 2021 as a full virtual event.
We are glad to announce that Paolo Missier will be IPAW’s keynote speaker. Paolo Missier is Full Professor in Large-scale Information Management with the School of Computing, Newcastle University, UK, and a Fellow of the Alan Turing Institute for Data Science and AI. He joined academia in 2011, after a prior career as a Research Scientist at Bell Communications Research, USA (1994-2001), and as a Research Fellow at the University of Manchester, School of Computer Science (2004-2011) where he also obtained his PhD in 2008. Most of his research contributions have been in the area of scientific workflows for e-science and on data provenance, including contributions to the W3C PROV Working group (2011-2013). He has recently launched new research efforts in two new areas: the study of fairness in Machine Learning, and applications of (Scalable) Data Science to enable preventive, predictive, and personalised healthcare. In Newcastle, Paolo leads the School’s post-graduate module on scalable technologies for Big Data Analytics.
The first version of our call for papers is online. Check it out!