Special Issue: The First Provenance Challenge

Moreau, Luc; Ludäscher, Bertram; Altıntaş, Ilkay; Barga, Roger; Bowers, Shawn; Callahan, Steven P.; Chin, George; Clifford, Ben; Cohen, Shirley; Cohen-Boulakia, Sarah; Davidson, Susan B.; Deelman, Ewa; Digiampietri, Luciano Antônio; Foster, Ian; Freire, Juliana; Frew, James; Futrelle, Joe; Gibson, Tara; Gil, Yolanda; Goble, Carole; Golbeck, Jennifer; Groth, Paul; Holland, David A.; Jiang, Sheng; Kim, Jihie; Koop, David; Křenek, Aleš; McPhillips, Timothy M.; Mehta, Gaurang; Miles, Simon; Metzger, Dominic; Munroe, Steve; Myers, James D.; Plale, Beth; Podhorszki, Norbert; Ratnakar, Varun; Santos, Emanuele; Scheidegger, Carlos; Schuchardt, Karen; Seltzer, Margo; Simmhan, Yogesh; Silva, Claudio; Slaughter, Peter; Stephan, Eric G.; Stevens, Robert; Turi, Daniele; Vo, Huy T.; Wilde, Mike; Zhao, Jun; Zhao, Yong

doi:10.1002/cpe.1233

Cited by 156 publications

(173 citation statements)

References 25 publications

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“…To answer this, we first consider typical provenance queries, using queries from the FPC [5,6]. Then we consider advantages and disadvantages of various schemas to record provenance.…”

Section: Storing Provenance Datamentioning

confidence: 99%

Automatic capture and efficient storage of e‐Science experiment provenance

Barga

Digiampietri

2007

Concurrency and Computation

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SUMMARYFor the first provenance challenge, we introduce a layered model to represent workflow provenance that allows navigation from an abstract model of the experiment to instance data collected during a specific experiment run. We outline modest extensions to a commercial workflow engine so it will automatically capture provenance at workflow runtime. We also present an approach to store this provenance data in a relational database. Finally, we demonstrate how core provenance queries in the challenge can be expressed in SQL and discuss the merits of our layered representation.

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“…To answer this, we first consider typical provenance queries, using queries from the FPC [5,6]. Then we consider advantages and disadvantages of various schemas to record provenance.…”

Section: Storing Provenance Datamentioning

confidence: 99%

Automatic capture and efficient storage of e‐Science experiment provenance

Barga

Digiampietri

2007

Concurrency and Computation

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“…In PN, actors execute as concurrent processes which communicate by sending ordered streams of data tokens over unidirectional first-in, first-out channels (the directed edges in the workflow graph W ). PN Any actor A will have observed some sequence e 1 A , e 2 A , . .…”

Section: Capturing Provenance In Advanced Models Of Computationmentioning

confidence: 99%

“…when considering stateless instead of stateful computations? One option, discussed elsewhere [1,6,9], is to use a MoC such as COMAD in which token streams have structure and thus meaning in terms of provenance. The COMAD MoC, e.g.…”

Section: Capturing Provenance In Advanced Models Of Computationmentioning

confidence: 99%

“…Thus, actors in a COMAD workflow perform computations not just on generic PN token streams, but on structured collection objects. Consequently, a COMAD MoP should record the dependencies between output and input collections appropriately [1,9], instead of 'blindly' using RW 0 .…”

Section: Capturing Provenance In Advanced Models Of Computationmentioning

confidence: 99%

“…The PASOA/OPA provenance model, e.g. is based on observables/assertions of the form received, sent, and caused [1,3]. may add timestamps to provenance assertions, recording the local time at which an event was observed, or indicate the execution host and user id executing the observed event.…”

Section: Introductionmentioning

confidence: 99%

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From computation models to models of provenance: the RWS approach

Ludäscher

Podhorszki

Altıntaş

et al. 2007

Concurrency and Computation

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SUMMARYScientific workflows often benefit from or even require advanced modeling constructs, e.g. nesting of subworkflows, cycles for executing loops, data-dependent routing, and pipelined execution. In such settings, an often overlooked aspect of provenance takes center stage: a suitable model of provenance (MoP) for scientific workflows should be based upon the underlying model of computation (MoC) used for executing the workflows. We can derive an adequate MoP from a MoC (such as Kahn's process networks) by taking into account the assumptions that a MoC entails, and by recording the observables which it affords. In this way, a MoP captures or at least better approximates 'real' data dependencies for workflows with advanced modeling constructs. As a specific instance, we elaborate on the Read-Write-ReSet model, a simple and flexible MoP suitable for a number of different MoCs.

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Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance

Gil

David

Demir

et al. 2016

Earth and Space Science

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156

153

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Geoscientists now live in a world rich with digital data and methods, and their computational research cannot be fully captured in traditional publications. The Geoscience Paper of the Future (GPF) presents an approach to fully document, share, and cite all their research products including data, software, and computational provenance. This article proposes best practices for GPF authors to make data, software, and methods openly accessible, citable, and well documented. The publication of digital objects empowers scientists to manage their research products as valuable scientific assets in an open and transparent way that enables broader access by other scientists, students, decision makers, and the public. Improving documentation and dissemination of research will accelerate the pace of scientific discovery by improving the ability of others to build upon published work.

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Special Issue: The First Provenance Challenge

Cited by 156 publications

References 25 publications

Automatic capture and efficient storage of e‐Science experiment provenance

Automatic capture and efficient storage of e‐Science experiment provenance

From computation models to models of provenance: the RWS approach

Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance

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