Provenance and Annotation of Data and Processes

8th and 9th International Provenance and Annotation Workshop, IPAW 2020 + IPAW 2021, Virtual Event, July 19-22, 2021, Proceedings
1. Auflage 2021
ISBN: 978-3-030-80959-1
Verlag: Springer International Publishing

Buch, Englisch, Band 12839, 271 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 441 g

Reihe: Lecture Notes in Computer Science

This book constitutes the proceedings of the 8th and 9th International Provenance and Annotation Workshop, IPAW 2020 and IPAW 2021 which were held as part of ProvenanceWeek in 2020 and 2021. Due to the COVID-19 pandemic, PropvenanceWeek 2020 was held as a 1-day virtual event with brief teaser talks on June 22, 2020. In 2021, the conference was held virtually during July 19-22, 2021. 
The 11 full papers and 12 posters and system demonstrations included in these proceedings were carefully reviewed and selected from a total of 31 submissions. They were organized in the following topical sections: provenance capture and representation; security; provenance types, inference, queries and summarization; reliability and trustworthiness; joint IPAW/TaPP poster and demonstration session. 

Zielgruppe

Research

Autoren/Hrsg.

Glavic, Boris

Koop, David

Braganholo, Vanessa

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Provenance Capture and Representation.- A Delayed Instantiation Approach to Template-driven Provenance for Electronic Health Record Phenotyping.- Provenance Supporting Hyperparameter Analysis in Deep Neural Networks.- Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods and Results.- The PROV-JSONLD Serialization.- Security.- Proactive Provenance Policies for Automatic Cryptographic Data Centric Security.- Provenance-based Security Audits and its Application to COVID-19 Contact Tracing Apps.- Provenance Types, Inference, Queries and Summarization.- Notebook Archaeology: Inferring Provenance from Computational Notebooks.- Efficient Computation of Provenance for Query Result Exploration.- Incremental Inference of Provenance Types.- Reliability and Trustworthiness.- Non-repudiable Provenance for Clinical Decision Support Systems.- A Model and System for Querying Provenance from Data Cleaning Workflows.- Joint IPAW/TaPP Poster and Demonstration Session.- ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks.- Mapping Trusted Paths to VGI.- Querying Data Preparation Modules Using Data Examples.- Privacy Aspects of Provenance Queries.- ISO 23494: Biotechnology – Provenance Information Model for Biological Specimen and Data.- Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles.- ProvViz: An Intuitive Prov Editor and Visualiser.- Curating Covid-19 data in Links.- Towards a provenance management system for astronomical observatories.- Towards Provenance Integration for Field Devices in Industrial IoT systems.- COVID-19 Analytics in Jupyter: Intuitive Provenance Integration using ProvIt.- CPR - A Comprehensible Provenance Record for Verification Workflows in Whole Tale.