lm2011.bib

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@comment{{Command line: bib2bib -oc lm2011.keys -ob lm2011.bib -c 'export = "yes" and year=2011' lm.bib ../euprovenance.bib ../ops.bib}}

@comment{{This file has been generated by bib2bib 1.99}}

@comment{{Command line: bib2bib -ob lm.bib -oc lm.keys -c 'export = "yes"' ../lm.bib}}

@article{Miles:TOSEM011,
  author = {Simon Miles and Paul Groth and Steve Munroe and Luc Moreau},
  title = {PrIMe: A Methodology for Developing Provenance-Aware Applications},
  journal = {ACM Transactions on Software Engineering and Methodology},
  year = {2011},
  optkey = {},
  volume = {20},
  number = {3},
  month = {August},
  export = {yes},
  optpages = {},
  doi = {10.1145/2000791.2000792},
  eprints = {https://eprints.soton.ac.uk/267450/},
  local = {https://nms.kcl.ac.uk/luc.moreau/papers/tosem09.pdf},
  issn = {1049-331X},
  pages = {1--42},
  optnote = {},
  optannote = {},
  abstract = {Provenance refers to the past processes that brought about a given (version of an) object, item or
entity. By knowing the provenance of data, users can often better understand, trust, reproduce,
and validate it. A provenance-aware application has the functionality to answer questions regard-
ing the provenance of the data it produces, by using documentation of past processes. PrIMe is a
software engineering technique for adapting application designs to enable them to interact with a
provenance middleware layer, thereby making them provenance-aware. In this article, we specify
the steps involved in applying PrIMe, analyse its effectiveness, and illustrate its use with two case
studies, in bioinformatics and medicine.}
}

@article{Moreau:FGCS11,
  title = {The Open Provenance Model core specification (v1.1)},
  author = {Luc Moreau and Ben Clifford and Juliana Freire and Joe
                  Futrelle and Yolanda Gil and Paul Groth and Natalia
                  Kwasnikowska and Simon Miles and Paolo Missier and Jim Myers
                  and Beth Plale and Yogesh Simmhan and Eric Stephan and Jan
                  {Van den Bussche}},
  journal = {Future Generation Computer Systems},
  publisher = {Elsevier},
  year = {2011},
  export = {yes},
  pages = {743--756},
  volume = {27},
  number = {6},
  month = jun,
  journal = {Future Generation Computer Systems},
  doi = {10.1016/j.future.2010.07.005},
  eprints = {https://eprints.soton.ac.uk/271449/},
  local = {https://nms.kcl.ac.uk/luc.moreau/papers/opm1.1.pdf},
  abstract = {The Open Provenance Model is a model of provenance that is designed to
meet the following requirements: (1) To allow provenance information
to be exchanged between systems, by means of a compatibility layer
based on a shared provenance model. (2) To allow developers to build
and share tools that operate on such a provenance model. (3) To define
provenance in a precise, technology-agnostic manner. (4) To support a
digital representation of provenance for any 'thing', whether
produced by computer systems or not. (5) To allow multiple levels of
description to coexist. (6) To define a core set of rules that
identify the valid inferences that can be made on provenance
representation. This document contains the specification of the Open
Provenance Model (v1.1) resulting from a community-effort to achieve
inter-operability in the Provenance Challenge series.
}
}

@article{Groth:FGCS11,
  month = dec,
  title = {Representing Distributed Systems Using OPM},
  author = {Paul Groth and Luc Moreau},
  publisher = {Elsevier},
  journal = {Future Generation Computer Systems},
  year = {2011},
  export = {yes},
  pages = {757--765},
  volume = {27},
  number = {6},
  month = jun,
  journal = {Future Generation Computer Systems},
  doi = {10.1016/j.future.2010.10.001},
  local = {https://nms.kcl.ac.uk/luc.moreau/papers/dist-fgcs11.pdf},
  eprints = {https://eprints.soton.ac.uk/408802/},
  abstract = {From the World Wide Web to supply chains and scientific simulations, distributed systems are a widely used and important approach to building computational systems. Tracking provenance within these systems is crucial for determining the trustworthiness of data they produce, troubleshooting problems, assigning responsibility for decisions, and improving performance. To facilitate such tracking, the Open Provenance Model (OPM) has been created to enable the interchange of provenance between a distributed system's components. However, to date, the ability for OPM to represent distributed systems has not been verified. In this work, we show how OPM can be used to represent a set of distributed systems patterns. We present a profile that shows that these patterns are a specialization of OPM. Finally, we define a contract that enables participants in a distributed system to ensure that their provenance can be integrated cohesively. }
}

@article{Moreau:WEBSEM11,
  month = feb,
  title = {Provenance-Based Reproducibility in the Semantic Web},
  author = {Luc Moreau},
  year = {2011},
  export = {yes},
  journal = {Web Semantics: Science, Services and Agents on the World Wide Web},
  volume = 9,
  issue = 2,
  pages = {202--221},
  doi = {10.1016/j.websem.2011.03.001},
  eprints = {http://eprints.ecs.soton.ac.uk/21992/},
  local = {https://nms.kcl.ac.uk/luc.moreau/papers/websem11.pdf},
  abstract = {Reproducibility is a crucial property of data since it allows users to understand and verify how data was derived, and therefore allows them to put their trust in such data. Reproducibility is essential for science, because the reproducibility of experimental results is a tenet of the scientific method, but reproducibility is also beneficial in many other fields, including automated decision making, visualization, and automated data feeds. To achieve the vision of reproducibility, the workflow-based community has strongly advocated the use of provenance as an underpinning mechanism for reproducibility, since a rich representation of provenance allows steps to be reproduced and all intermediary and final results checked and validated. Concurrently, multiple ontology-based representations of provenance have been devised, to be able to describe past computations, uniformly across a variety of technologies. However, such Semantic Web representations of provenance do not have any formal link with execution. Even assuming a faithful and non-malicious environment, how can we claim that an ontology-based representation of provenance enables reproducibility, since it has not been given any execution semantics, and therefore has no formal way of expressing the reproduction of computations? This is the problem that this paper tackles by defining a denotational semantics for the Open Provenance Model, which is referred to as the reproducibility semantics. This semantics is used to implement a reproducibility service, leveraging multiple Semantic Web technologies, and offering a variety of reproducibility approaches, found in the literature. A series of empirical experiments were
designed to exhibit the range of reproducibility capabilities of our approach;
in particular, we demonstrate the ability to reproduce
computations involving multiple technologies, as is commonly found on the Web.}
}