Modeling and Querying Scientific Workflow Provenance in the D-OPM

Cuevas-Vicenttín, Víctor; Dey, Saumen; Wang, Michael Li Yuan; Song, Tianhong; Ludäscher, Bertram

doi:10.1109/sc.companion.2012.27

Cited by 19 publications

(13 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We will discuss how the PROV constructs were adapted and extended to capture the provenance traces of scientific workflows. We will discuss the PROV extensions adopted by the scientific workflow system Taverna [15], and D-PROV [8], the provenance model specified by the DataONE scientific workflow and provenance working group.…”

Section: Prov and Scientific Workflowsmentioning

confidence: 99%

The W3C PROV family of specifications for modelling provenance metadata

Missier

Belhajjame

Cheney

2013

Proceedings of the 16th International Conference on Extending Database Technology

159

109

View full text Add to dashboard Cite

Provenance, a form of structured metadata designed to record the origin or source of information, can be instrumental in deciding whether information is to be trusted, how it can be integrated with other diverse information sources, and how to establish attribution of information to authors throughout its history. The PROV set of specifications, produced by the World Wide Web Consortium (W3C), is designed to promote the publication of provenance information on the Web, and offers a basis for interoperability across diverse provenance management systems. The PROV provenance model is deliberately generic and domain-agnostic, but extension mechanisms are available and can be exploited for modelling specific domains. This tutorial provides an account of these specifications. Starting from intuitive and informal examples that present idiomatic provenance patterns, it progressively introduces the relational model of provenance along with the constraints model for validation of provenance documents, and concludes with example applications that show the extension points in use.

show abstract

Section: Prov and Scientific Workflowsmentioning

confidence: 99%

The W3C PROV family of specifications for modelling provenance metadata

Missier

Belhajjame

Cheney

2013

Proceedings of the 16th International Conference on Extending Database Technology

159

109

View full text Add to dashboard Cite

show abstract

“…This query will answer the connections (1,3), (2,4), and (4,3). The connection (1,3) is in the answer as the legs (2,4) and (4,3) are only serviced by airline c. For the same reason the connections (2,4) and (4,3) are also in the answer.…”

Section: Motivating Examplementioning

confidence: 99%

“…This query will answer the connection (1,2) as only airline a is servicing between them. Note that connections (2,4) and (4,3) are also serviced by only airline c. But, airline c is not participating in the mileage program. In the same same way you may want to find the connections serviced only by airlines which are not participating in the mileage program (Query Q4).…”

Section: Motivating Examplementioning

confidence: 99%

“…In the same same way you may want to find the connections serviced only by airlines which are not participating in the mileage program (Query Q4). This query will answer the connections (2,4) and (4,3) as only airline c is servicing between them.…”

Section: Motivating Examplementioning

confidence: 99%

“…These experiments demonstrate that both approaches are viable for our various types of RPQs, and that particularly good performance can be achieved by the use of the right techniques. Partial and preliminary aspects of this work have been published earlier in [7] and [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On implementing provenance-aware regular path queries with relational query engines

Dey

Cuevas-Vicenttín

Köhler

et al. 2013

Proceedings of the Joint EDBT/ICDT 2013 Workshops

Self Cite

View full text Add to dashboard Cite

Use of graphs is growing rapidly in social networks, semantic web, biological databases, scientific workflow provenance, and other areas. Regular Path Queries (RPQs) can be seen as a core graph query language to answer pattern-based reachability queries. Unfortunately, the number of freely available systems for querying graphs using RPQs is rather limited, and available implementations do not provide direct support for a number of desirable variants of RPQs, e.g., to return those edges that are contained in some (or all) paths that match the given regular expression R. Thus, by returning not just a pair (x, y) of end points of paths that match R, but also "witness edges" (u, v) inbetween, our RPQ variants can be understood as returning additional provenance information about the answer (x, y), i.e., those edges (u, v) that are in some (or all) paths from x to y matching R. We propose a number of such RPQ variants and show how they can be implemented using either Datalog or a suitable RDBMS. Our initial experimental results indicate that RPQs and our provenance-aware variants (RPQProv), when implemented using conventional relational technologies, yield reasonable performance even for relatively large graphs. On the other hand, the overhead associated with some of these variants also makes efficient handling of provenance-aware graph queries an interesting challenge for future research.

show abstract

Provenance data discovery through Semantic Web resources

Ornelas

Braga

David

et al. 2017

Concurrency and Computation

View full text Add to dashboard Cite

Summary Providing historical information to deal with knowledge loss about a scientific experiment has been the focus of some several researches. However, computational support for large‐scale scientific experiments is still incipient and is considered one of e‐science's greatest challenges. In this vein, providing provenance information to a scientist is part of these challenges. Provenance information helps to assure the reliability and reproducibility of experiments. Therefore, this work has as its main objective to present a new ontology—Open Provenance Model Ontology‐e (OPMO‐e)—which is part of an architecture, named SciProvMiner. Open Provenance Model Ontology‐e is a new ontology that encompasses both prospective and retrospective provenance. SciProvMiner captures the provenance and implements all inference and completeness rules defined by Open Provenance Model to provide provenance information beyond those already established. We hypothesize that the capture and management of provenance data will provide scientists with implicit strategical information about the experiment through OPMO‐e. Case studies were carried out to evaluate OPMO‐e use considering SciProvMiner. The obtained results revealed that the use of the proposed ontology and SciProvMiner can enhance scientist's knowledge about an experiment.

show abstract

Modeling and Querying Scientific Workflow Provenance in the D-OPM

Cited by 19 publications

References 10 publications

The W3C PROV family of specifications for modelling provenance metadata

The W3C PROV family of specifications for modelling provenance metadata

On implementing provenance-aware regular path queries with relational query engines

Provenance data discovery through Semantic Web resources

Contact Info

Product

Resources

About