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

272 Pages·2018·17.66 MB·English

by  Khalid Belhajjame

Khalid Belhajjame
Ashish Gehani
Pinar Alper (Eds.)
Provenance
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and Annotation of Data
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7th International Provenance
and Annotation Workshop, IPAW 2018
London, UK, July 9–10, 2018, Proceedings
123
Lecture Notes in Computer Science 11017
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Khalid Belhajjame Ashish Gehani
(cid:129)
Pinar Alper (Eds.)
Provenance
and Annotation of Data
and Processes
7th International Provenance
and Annotation Workshop, IPAW 2018
–
London, UK, July 9 10, 2018
Proceedings
123
Editors
KhalidBelhajjame PinarAlper
Paris Dauphine University University of Luxembourg
Paris Belvaux
France Luxembourg
AshishGehani
SRIInternational
Menlo Park, CA
USA
ISSN 0302-9743 ISSN 1611-3349 (electronic)
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Preface
This volume contains the proceedings of the 7th International Provenance and
Annotation Workshop (IPAW), held during July 9–10, 2018, at King’s College in
London, UK. For the third time, IPAW was co-located with the Workshop on the
Theory and Practice of Provenance (TaPP). Together, the two leading provenance
workshops anchored Provenance Week 2018, a full week of provenance-related
activitiesthatincludedasharedpostersessionandthreeotherworkshopsonalgorithm
accountability, incremental re-computation, and security. The proceedings of IPAW
include 12 long papers that report in-depth the results of research around provenance,
two system demonstration papers, and 19 poster papers.
IPAW 2018 provided a rich program with a variety of provenance-related topics
rangingfromthecaptureandinference ofprovenancetoitsuseandapplication.Since
provenance is a key ingredient to enable reproducibility, several papers have investi-
gatedmeansforenablingdataflowsteeringandprocessre-computation.Themodeling
of provenance and its simulation has been the subject of a number of papers, which
tackled issues that seek, among other things, to model provenance in software engi-
neeringactivitiesortouseprovenancetomodelaspectsoftheEuropeanUnionGeneral
Data Protection Regulation. Other papers investigated inference techniques to propa-
gate beliefs in provenance graphs, efficiently update RDF graphs, mine similarities
between processes, and discover workflow schema-level dependencies. This year’s
program also featured extensions of the W3C Prov recommendation to support new
features,e.g.,versioningofmutableentities,orcaterfornewdomainknowledge,e.g.,
astronomy.
Inclosing,wewouldliketothankthemembersoftheProgramCommitteefortheir
thoughtful reviews, Vasa Curcin and Simon Miles for the local organization of IPAW
andtheProvenanceWeekatKing’sCollege,London,andtheauthorsandparticipants
for making IPAW a successful event.
June 2018 Khalid Belhajjame
Ashish Gehani
Pinar Alper
Organization
Program Committee
Pinar Alper University of Luxembourg, Luxembourg
Ilkay Altintas SDSC, USA
David Archer Galois, Inc., USA
Khalid Belhajjame University of Paris-Dauphine, France
Vanessa Braganholo UFF, Brazil
Kevin Butler University of Florida, USA
Sarah Cohen-Boulakia LRI, University of Paris-Sud, France
Oscar Corcho Universidad Politécnica de Madrid, Spain
Vasa Curcin King’s College London, UK
Susan Davidson University of Pennsylvania, USA
Daniel de Oliveira Fluminense Federal University, Brazil
Saumen Dey University of California, Davis, USA
Alban Gaignard CNRS, France
Daniel Garijo Information Sciences Institute, USA
Ashish Gehani SRI International, USA
Paul Groth Elsevier Labs, The Netherlands
Trung Dong Huynh King’s College London, UK
Grigoris Karvounarakis LogicBlox, Greece
David Koop University of Massachusetts Dartmouth, USA
Bertram Ludaescher University of Illinois at Urbana-Champaign, USA
Tanu Malik University of Chicago, USA
Marta Mattoso Federal University of Rio de Janeiro, Brazil
Deborah McGuinness Rensselaer Polytechnic Institute (RPI), USA
Simon Miles King’s College London, UK
Paolo Missier Newcastle University, UK
Luc Moreau King’s College London, UK
Beth Plale Indiana University Bloomington, USA
Satya Sahoo Case Western Reserve University, USA
Stian Soiland-Reyes The University of Manchester, UK
Jun Zhao University of Oxford, UK
Additional Reviewers
Carvalho, Lucas Augusto Pimentel, João
Montalvão Costa Rashid, Sabbir
Cała, Jacek Souza, Renan
Chagas, Clayton Yan, Rui
Contents
Reproducibility
Provenance Annotation and Analysis to Support Process Re-computation. . . . 3
Jacek Cała and Paolo Missier
Provenance of Dynamic Adaptations in User-Steered Dataflows . . . . . . . . . . 16
Renan Souza and Marta Mattoso
Classification of Provenance Triples for Scientific Reproducibility:
A Comparative Evaluation of Deep Learning Models
in the ProvCaRe Project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Joshua Valdez, Matthew Kim, Michael Rueschman, Susan Redline,
and Satya S. Sahoo
Modeling, Simulating and Capturing Provenance
A Provenance Model for the European Union General Data
Protection Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Benjamin E. Ujcich, Adam Bates, and William H. Sanders
Automating Provenance Capture in Software Engineering
with UML2PROV. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
Carlos Sáenz-Adán, Luc Moreau, Beatriz Pérez, Simon Miles,
and Francisco J. García-Izquierdo
Simulated Domain-Specific Provenance. . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Pinar Alper, Elliot Fairweather, and Vasa Curcin
PROV Extensions
Versioned-PROV: A PROV Extension to Support Mutable Data Entities. . . . 87
João Felipe N. Pimentel, Paolo Missier, Leonardo Murta,
and Vanessa Braganholo
Using the Provenance from Astronomical Workflows to Increase
Processing Efficiency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Michael A. C. Johnson, Luc Moreau, Adriane Chapman,
Poshak Gandhi, and Carlos Sáenz-Adán
VIII Contents
Scientific Workflows
Discovering Similar Workflows via Provenance Clustering: A Case Study. . . 115
Abdussalam Alawini, Leshang Chen, Susan Davidson, Stephen Fisher,
and Junhyong Kim
Validation and Inference of Schema-Level Workflow
Data-Dependency Annotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
Shawn Bowers, Timothy McPhillips, and Bertram Ludäscher
Applications
Belief Propagation Through Provenance Graphs . . . . . . . . . . . . . . . . . . . . . 145
BelfritVictorBatlajery,MarkWeal,AdrianeChapman,andLucMoreau
Using Provenance to Efficiently Propagate SPARQL Updates
on RDF Source Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
Iman Naja and Nicholas Gibbins
System Demonstrations
Implementing Data Provenance in Health Data Analytics Software . . . . . . . . 173
Shen Xu, Elliot Fairweather, Toby Rogers, and Vasa Curcin
Quine: A Temporal Graph System for Provenance Storage and Analysis . . . . 177
Ryan Wright
Joint IPAW/TaPP Poster Session
CapturingProvenanceforRuntimeDataAnalysisinComputationalScience
and Engineering Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
Vítor Silva, Renan Souza, Jose Camata, Daniel de Oliveira,
Patrick Valduriez, Alvaro L. G. A. Coutinho, and Marta Mattoso
UniProv - Provenance Management for UNICORE Workflows
in HPC Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
André Giesler, Myriam Czekala, and Björn Hagemeier
Towards a PROV Ontology for Simulation Models. . . . . . . . . . . . . . . . . . . 192
Andreas Ruscheinski, Dragana Gjorgevikj, Marcus Dombrowsky,
Kai Budde, and Adelinde M. Uhrmacher
Capturing the Provenance of Internet of Things Deployments. . . . . . . . . . . . 196
David Corsar, Milan Markovic, and Peter Edwards
Contents IX
Towards Transparency of IoT Message Brokers . . . . . . . . . . . . . . . . . . . . . 200
Milan Markovic, David Corsar, Waqar Asif, Peter Edwards,
and Muttukrishnan Rajarajan
Provenance-Based Root Cause Analysis for Revenue Leakage Detection:
A Telecommunication Case Study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
Wisam Abbasi and Adel Taweel
Case Base Reasoning Decision Support Using the DecPROV
Ontology for Decision Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
Nicholas J. Car
Bottleneck Patterns in Provenance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
Sara Boutamina, James D. A. Millington, and Simon Miles
Architecture for Template-Driven Provenance Recording . . . . . . . . . . . . . . . 217
Elliot Fairweather, Pinar Alper, Talya Porat, and Vasa Curcin
Combining Provenance Management and Schema Evolution. . . . . . . . . . . . . 222
Tanja Auge and Andreas Heuer
Provenance for Entity Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
Sarah Oppold and Melanie Herschel
Where Provenance in Database Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
Alexander Rasin, Tanu Malik, James Wagner, and Caleb Kim
Streaming Provenance Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236
Raza Ahmad, Melanie Bru, and Ashish Gehani
Structural Analysis of Whole-System Provenance Graphs. . . . . . . . . . . . . . . 241
Jyothish Soman, Thomas Bytheway, Lucian Carata,
Nikilesh D. Balakrishnan, Ripduman Sohan, and Robert N. M. Watson
A Graph Testing Framework for Provenance Network Analytics. . . . . . . . . . 245
Bernard Roper, Adriane Chapman, David Martin, and Jeremy Morley
Provenance for Astrophysical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252
Anastasia Galkin, Kristin Riebe, Ole Streicher, Francois Bonnarel,
Mireille Louys, Michèle Sanguillon, Mathieu Servillat,
and Markus Nullmeier
Data Provenance in Agriculture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257
Sérgio Manuel Serra da Cruz, Marcos Bacis Ceddia,
Renan Carvalho Tàvora Miranda, Gabriel Rizzo, Filipe Klinger,
Renato Cerceau, Ricardo Mesquita, Ricardo Cerceau,
Elton Carneiro Marinho, Eber Assis Schmitz, Elaine Sigette,
and Pedro Vieira Cruz