Provenance and Annotation of Data and Processes 7th International Provenance and Annotation Workshop, IPAW 2018, London, UK, July 9-10, 2018, Proceedings

This book constitutes the refereed proceedings of the 7th International Provenance and Annotation Workshop, IPAW 2018, held in London, UK, in July 2018.The 12 revised full papers, 19 poster papers, and 2 demonstration papers presented were carefully reviewed and selected from 50 submissions. The pap...

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Bibliographic Details
Main Authors Belhajjame, Khalid, Gehani, Ashish, Alper, Pinar
Format eBook
LanguageEnglish
Published Netherlands Springer Nature 2018
Springer International Publishing AG
Springer
Edition1
SeriesLNCS sublibrary. SL 3, Information systems and applications, incl. Internet/Web, and HCI
Subjects
Database management-Congresses
Special computer methods
Online AccessGet full text
ISBN3319983792
9783319983790
3319983784
9783319983783

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Table of Contents:
  • References -- Automating Provenance Capture in Software Engineering with UML2PROV -- 1 Introduction -- 2 Overview: The UML2PROV Approach -- 3 From Class Diagrams to Templates -- 3.1 A Taxonomy of Operations Stereotypes -- 3.2 Class Diagrams to Templates Transformation Patterns -- 4 Implementation -- 4.1 Implementation of the Mapping Patterns -- 4.2 Generation of Artefacts -- 5 Analysis and Discussion -- 6 Related Work -- 7 Conclusions and Future Work -- References -- Simulated Domain-Specific Provenance -- 1 Motivations and Approach -- 2 Provenance Templates -- 2.1 Variable Domains -- 3 Domain-Specific Constraints for Templates -- 3.1 Constraint Types -- 3.2 Solving Constraints -- 4 Processes and Simulation -- 4.1 Processes -- 4.2 Simulation -- 5 Implementation and Architecture -- 6 Evaluation -- 7 Related Work -- 8 Conclusions and Future Work -- References -- PROV Extensions -- Versioned-PROV: A PROV Extension to Support Mutable Data Entities -- 1 Introduction -- 2 Running Example -- 3 Versioned-PROV -- 3.1 Concepts -- 3.2 Mapping Example -- 4 Evaluation -- 5 Related Work -- 6 Final Remarks -- References -- Using the Provenance from Astronomical Workflows to Increase Processing Efficiency -- 1 Introduction -- 2 Astronomy Application -- 2.1 The Image Processing Pipeline -- 2.2 Use Cases -- 3 Provenance in Astronomy Simulations -- 4 Evaluation -- 4.1 Use Case 1 -- 4.2 Use Case 2 -- 5 Related Work -- 5.1 Provenance in Astronomy and e-Science -- 5.2 PROV-TEMPLATES -- 6 Conclusions -- References -- Scientific Workflows -- Discovering Similar Workflows via Provenance Clustering: A Case Study -- 1 Introduction -- 2 Background -- 2.1 Next Generation Gene Sequencing (NGS) -- 2.2 Related Work -- 3 Clustering Workflow Provenance -- 3.1 Provenance Clustering Framework -- 3.2 Data Model: Abstract Provenance Graphs -- 3.3 Feature Extraction
  • 3.4 Measuring Graph Similarity -- 3.5 Clustering Algorithm -- 4 Preliminary Experiments -- 4.1 Provenance Datasets -- 4.2 Analysis over Real Datasets -- 4.3 Analysis over Synthetic Datasets -- 4.4 Running Time Analysis -- 5 Conclusion -- References -- Validation and Inference of Schema-Level Workflow Data-Dependency Annotations -- 1 Introduction -- 2 Workflow Dependency Annotations -- 3 Reasoning over Dependency Types -- 3.1 Dependency Types -- 3.2 Composing Dependency Annotations -- 3.3 Additional Annotation Constraints -- 4 Prototype Implementation -- 5 Related Work -- 6 Conclusion and Future Work -- References -- Applications -- Belief Propagation Through Provenance Graphs -- 1 Introduction -- 2 Background -- 2.1 Provenance -- 2.2 Belief Propagation -- 3 Food Supply Chain as a Use Case -- 3.1 Food Provenance and Food Regulations -- 3.2 Modular Process Risk Model (MPRM) -- 4 The prFrame Framework -- 4.1 Food Risk Model with Monte-Carlo Simulation -- 4.2 Belief Propagation in the Provenance Network -- 4.3 Methodology to Infer Risk of Contamination -- 5 Evaluation of the Methodology -- 5.1 The Effect of the Distance and Position Between Nodes -- 5.2 Analysis of the Result -- 6 Conclusion and Future Work -- References -- Using Provenance to Efficiently Propagate SPARQL Updates on RDF Source Graphs -- 1 Introduction -- 2 Related Work -- 3 Running Example -- 4 The RGPROV Vocabulary -- 5 The Model and Algorithms -- 5.1 System Architecture -- 5.2 Update Propagation per Set Theoretic Operations -- 5.3 Partial Re-derivation Algorithms -- 6 Results -- 7 Conclusion and Future Work -- References -- System Demonstrations -- Implementing Data Provenance in Health Data Analytics Software -- Abstract -- 1 Introduction -- 2 Use Case -- 3 Summary -- References -- Quine: A Temporal Graph System for Provenance Storage and Analysis -- Abstract -- 1 Introduction
  • Intro -- Preface -- Organization -- Contents -- Reproducibility -- Provenance Annotation and Analysis to Support Process Re-computation -- 1 Introduction -- 1.1 Version Changes and Their Scope -- 1.2 Problem Formulation and Contributions -- 1.3 Example: Versioning in Genomics -- 2 Recomputation Fronts and Restart Trees -- 2.1 Recomputation Fronts -- 2.2 Building a Restart Tree -- 3 Computing the Re-computation Front -- 4 Related Work -- 5 Discussion and Conclusions -- References -- Provenance of Dynamic Adaptations in User-Steered Dataflows -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Workflows, Computational Steering and Data Provenance -- 3.1 Dataflow-Oriented Approach and Runtime Provenance -- 3.2 A Diagram for Runtime Provenance in HPC Workflows -- 4 Provenance of Dynamic Adaptation in User-Steered Dataflows -- 5 Specializing PROV-DfA Concepts -- 5.1 Simulation Parameter Tuning -- 5.2 Online Adaptation of Iterative Simulations -- 5.3 Data Reduction -- 6 Case Study -- 7 Conclusion -- Acknowledgement -- References -- Classification of Provenance Triples for Scientific Reproducibility: A Comparative Evaluation of Deep Learning Models in the ProvCaRe Project -- Abstract -- 1 Introduction -- 2 Method -- 2.1 ProvCaRe S3 Model and Ontology -- 2.2 Training of Deep Learning Models -- 2.3 Deep Learning Model Architectures -- 3 Result and Discussion -- 3.1 Classification Results -- 3.2 Comparative Evaluation Results -- 4 Conclusion -- Acknowledgement -- References -- Modeling, Simulating and Capturing Provenance -- A Provenance Model for the European Union General Data Protection Regulation -- 1 Introduction -- 2 Background and Related Work -- 2.1 GDPR Background -- 2.2 Related Work -- 3 GDPR Data Provenance Model -- 4 Using the GDPR Data Provenance Model -- 4.1 Design Patterns -- 4.2 Verifying Compliance -- 5 Discussion -- 6 Conclusion
  • 1.1 Highly Connected Temporal Data -- 1.2 Queries on Complex Structures over Time -- 1.3 Evolving Schema -- 1.4 Scalability for Large Datasets -- 2 Demonstration Topics -- 3 Conclusion -- Acknowledgments -- References -- Joint IPAW/TaPP Poster Session -- Capturing Provenance for Runtime Data Analysis in Computational Science and Engineering Applications -- Abstract -- 1 Introduction -- 2 DfA-prov Making CSE Applications Provenance-Aware -- 3 Conclusions -- Acknowledgments -- References -- UniProv - Provenance Management for UNICORE Workflows in HPC Environments -- Abstract -- 1 Introduction -- 2 Interoperable Provenance Framework for UNICORE -- 3 Capturing and Storing Provenance Data -- Acknowledgements -- References -- Towards a PROV Ontology for Simulation Models -- 1 Introduction -- 2 Exploiting PROV-DM for Simulation Model Development -- 3 Towards an PROV Ontology for Simulation Model Development -- References -- Capturing the Provenance of Internet of Things Deployments -- 1 Introduction -- 2 Describing IoT System Deployments -- 3 Future Work -- References -- Towards Transparency of IoT Message Brokers -- 1 Introduction -- 2 The MQTT-PLAN Ontology -- 3 Discussion and Future Work -- References -- Provenance-Based Root Cause Analysis for Revenue Leakage Detection: A Telecommunication Case Study -- 1 Introduction -- 2 The Proposed Approach -- 3 Running Examples -- 4 Results -- References -- Case Base Reasoning Decision Support Using the DecPROV Ontology for Decision Modelling -- 1 Decision Modelling Need and a Domain -- 2 Standardised PROV Decision Modelling -- 3 Case-Based Reasoning with Decisions -- 4 Current Work -- 5 Future Work -- References -- Bottleneck Patterns in Provenance -- 1 Introduction -- 2 Classification of Bottlenecks -- 3 Bottlenecks Patterns -- References -- Architecture for Template-Driven Provenance Recording -- 1 Introduction
  • 2 Methodology -- 3 Architecture -- 4 Conclusions and Future Work -- References -- Combining Provenance Management and Schema Evolution -- 1 Introduction -- 2 Problem and Poster Description -- 2.1 Calculation of a Minimal Sub-database -- 2.2 Unification of Provenance and Evolution -- 2.3 Query Q -- 2.4 Evolution E -- 2.5 Data Provenance Qprov -- References -- Provenance for Entity Resolution -- 1 Motivation -- 2 Provenance Model for Abstract ER Pipelines -- 3 Implementing Provenance Capture for HIL ER Rules -- 4 Conclusion and Outlook -- References -- Where Provenance in Database Storage -- 1 Introduction -- 2 Background and Related Work -- 3 Motivating Where Provenance in DBMSes -- 4 Forensic Evidence in Where Provenance -- 5 Conclusion -- References -- Streaming Provenance Compression -- 1 Introduction -- 2 Contributions -- References -- Structural Analysis of Whole-System Provenance Graphs -- 1 Introduction -- 2 Graph Types in WSP -- 3 Setup -- 4 Results -- 5 Conclusions -- References -- A Graph Testing Framework for Provenance Network Analytics -- Abstract -- 1 Introduction -- 2 A Multi-model Graph Analysis Framework -- 3 Future Work -- References -- Provenance for Astrophysical Data -- 1 Introduction -- 2 Use Cases for Provenace in Astronomy -- 2.1 Cherenkov Telescope Array -- 2.2 Spectroscopic Surveys -- 2.3 APPLAUSE Database - Scanning Historical Photoplates -- 2.4 MUSE Data Reduction Pipeline -- 2.5 RAVE Survey -- 3 Special Requirements in Modelling Provenance in Astronomy -- 4 Integration into the IVOA Ecosystem -- 5 Summary -- References -- Data Provenance in Agriculture -- Abstract -- 1 Introduction -- 2 Experiments in Soils Science -- 3 Open Soils -- 4 Concluding Remarks -- Acknowledgments -- References -- Extracting Provenance Metadata from Privacy Policies -- 1 Motivation -- 2 Provenance Metadata -- 3 Potential Applications
  • 4 Conclusion