thesis-lit-prov-prof-confs.bib

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@inproceedings{Chapman2010,
abstract = {Provenance has been touted as a basis to establish trust in data. Intuitively, belief in a hypothesis should depend on how much one trusts the relevant data. However, current proposals to assess trust based solely on provenance are insufficient for rigourous decision making. We describe a model of provenance and belief that is necessary and sufficient to incorporate trust in the data in a way that supports normative inference. The model is based on the observation that provenance can be viewed as a causal structure which can be used to compute belief from assessments of the accuracy of sources and transformations that produced relevant data. In our model, data sources are like sensors with associated conditional probability tables. Provenance identifies dependencies among sensors. Together, this information allows construction of causal networks that can be used to compute the belief in a state of the world based on observation of data. This model formalizes the role of source accuracy, and provides a method for formally assessing belief that uses only information in the provenance store, not the contents of the data.},
address = {San Jose, California},
author = {Chapman, Adriane and Blaustein, Barbara and Elsaesser, Chris},
booktitle = {Proceedings of the 2nd Conference on Theory and Practice of Provenance},
file = {:home/andrew/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Chapman, Blaustein, Elsaesser - 2010 - Provenance-based Belief.pdf:pdf},
pages = {1--14},
publisher = {USENIX Association},
title = {{Provenance-based Belief}},
url = {https://dl.acm.org/citation.cfm?id=1855806},
year = {2010}
}
@article{Acar2010,
abstract = {Provenance has been studied extensively in both database and workflow management systems, so far with little convergence of definitions or models. Provenance in databases has generally been defined for relational or complex object data, by propagating fine-grained annotations or algebraic expressions from the input to the output. This kind of provenance has been found useful in other areas of computer science: annotation databases, probabilistic databases, schema and data integration, etc. In contrast, workflow provenance aims to capture a complete description of evaluation - or enactment - of a workflow, and this is crucial to verification in scientific computation. Workflows and their provenance are often presented using graphical notation, making them easy to visualize but complicating the formal semantics that relates their run-time behavior with their provenance records. We bridge this gap by extending a previously-developed dataflow language which supports both database-style querying and workflow-style batch processing steps to produce a workflow-style provenance graph that can be explicitly queried. We define and describe the model through examples, present queries that extract other forms of provenance, and give an executable definition of the graph semantics of dataflow expressions.},
author = {Acar, Umut and Buneman, Peter and Cheney, James and {Van Den Bussche}, Jan and Kwasnikowska, Natalia and Vansummeren, Stijn},
file = {:home/andrew/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Acar et al. - 2010 - A graph model of data and workflow provenance.pdf:pdf},
journal = {Procs. TAPP'10 workshop (Theory and Practice of Provenance)},
pages = {8},
title = {{A graph model of data and workflow provenance}},
url = {http://dl.acm.org/citation.cfm?id=1855803},
year = {2010}
}
@inproceedings{Batlajery2018,
address = {London, United Kingdom},
author = {Batlajery, Belfrit Victor and Weal, Mark and Chapman, Adriane and Moreau, Luc},
booktitle = {Provenance Week '18: 7th International Provenance And Annotation Workshop},
file = {:home/andrew/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Batlajery et al. - 2018 - Belief Propagation Through Provenance Graphs.pdf:pdf},
keywords = {causality,food,probability,provenance},
mendeley-tags = {causality,food,probability,provenance},
title = {{Belief Propagation Through Provenance Graphs}},
url = {https://kclpure.kcl.ac.uk/portal/en/publications/belief-propagation-through-provenance-graphs(c1b7a54d-4e9c-4a9f-8d7d-cce4a6b1e4ab).html},
year = {2018}
}
@incollection{Saenz-Adan2018,
author = {S{\'{a}}enz-Ad{\'{a}}n, Carlos and P{\'{e}}rez, Beatriz and Huynh, Trung Dong and Moreau, Luc},
booktitle = {44th International Conference on Current Trends in Theory and Practice of Computer Science},
doi = {10.1007/978-3-319-73117-9_47},
file = {:home/andrew/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/S{\'{a}}enz-Ad{\'{a}}n et al. - 2018 - UML2PROV Automating Provenance Capture in Software Engineering.pdf:pdf},
pages = {667--681},
title = {{UML2PROV: Automating Provenance Capture in Software Engineering}},
url = {https://kclpure.kcl.ac.uk/portal/en/publications/uml2prov-automating-provenance-capture-in-software-engineering(3550fbb4-1d16-42db-97ab-0466a8c67683).html http://link.springer.com/10.1007/978-3-319-73117-9{\_}47},
year = {2018}
}
@incollection{Ji2016,
abstract = {The World Wide Web evolves into a Web of Data, a huge, globally distributed dataspace that contains a rich body of machine-processable information from a virtually unbound set of providers covering a wide range of topics. However, due to the openness of the Web little is known about who created the data and how. The fact that a large amount of the data on the Web is derived by replication, query processing, modification, or merging raises concerns of information quality. Poor quality data may propagate quickly and contaminate the Web of Data. Provenance information about who created and published the data and how, provides the means for quality assessment. This paper takes a first step towards creating a quality-aware Web of Data: we present approaches to integrate provenance information into the Web of Data and we illustrate how this information can be consumed. In particular, we introduce a vocabulary to describe provenance of Web data as metadata and we discuss possibilities to make such provenance metadata accessible as part of the Web of Data. Furthermore, we describe how this metadata can be queried and consumed to identify outdated information.},
archivePrefix = {arXiv},
arxivId = {1406.2495},
author = {Ji, Yang and Lee, Sangho and Lee, Wenke},
doi = {10.1007/978-3-319-40593-3_1},
eprint = {1406.2495},
file = {:home/andrew/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ji, Lee, Lee - 2016 - RecProv Towards Provenance-Aware User Space Record and Replay.pdf:pdf},
isbn = {978-3-642-34221-9},
issn = {03029743},
keywords = {provenance capturing},
pages = {3--15},
pmid = {9156564},
title = {{RecProv: Towards Provenance-Aware User Space Record and Replay}},
url = {http://link.springer.com/10.1007/978-3-319-40593-3{\_}1},
volume = {7525},
year = {2016}
}
@incollection{Kohwalter2016,
abstract = {The World Wide Web evolves into a Web of Data, a huge, globally distributed dataspace that contains a rich body of machine-processable information from a virtually unbound set of providers covering a wide range of topics. However, due to the openness of the Web little is known about who created the data and how. The fact that a large amount of the data on the Web is derived by replication, query processing, modification, or merging raises concerns of information quality. Poor quality data may propagate quickly and contaminate the Web of Data. Provenance information about who created and published the data and how, provides the means for quality assessment. This paper takes a first step towards creating a quality-aware Web of Data: we present approaches to integrate provenance information into the Web of Data and we illustrate how this information can be consumed. In particular, we introduce a vocabulary to describe provenance of Web data as metadata and we discuss possibilities to make such provenance metadata accessible as part of the Web of Data. Furthermore, we describe how this metadata can be queried and consumed to identify outdated information.},
archivePrefix = {arXiv},
arxivId = {1406.2495},
author = {Kohwalter, Troy and Oliveira, Thiago and Freire, Juliana and Clua, Esteban and Murta, Leonardo},
doi = {10.1007/978-3-319-40593-3_6},
eprint = {1406.2495},
file = {:home/andrew/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kohwalter et al. - 2016 - Prov Viewer A Graph-Based Visualization Tool for Interactive Exploration of Provenance Data.pdf:pdf},
isbn = {978-3-642-34221-9},
issn = {03029743},
keywords = {provenance,visualisation},
mendeley-tags = {provenance,visualisation},
pages = {71--82},
pmid = {9156564},
title = {{Prov Viewer: A Graph-Based Visualization Tool for Interactive Exploration of Provenance Data}},
url = {http://link.springer.com/10.1007/978-3-642-34222-6 http://link.springer.com/10.1007/978-3-319-40593-3{\_}6},
volume = {7525},
year = {2016}
}
@incollection{Stamatogiannakis2016,
abstract = {The World Wide Web evolves into a Web of Data, a huge, globally distributed dataspace that contains a rich body of machine-processable information from a virtually unbound set of providers covering a wide range of topics. However, due to the openness of the Web little is known about who created the data and how. The fact that a large amount of the data on the Web is derived by replication, query processing, modification, or merging raises concerns of information quality. Poor quality data may propagate quickly and contaminate the Web of Data. Provenance information about who created and published the data and how, provides the means for quality assessment. This paper takes a first step towards creating a quality-aware Web of Data: we present approaches to integrate provenance information into the Web of Data and we illustrate how this information can be consumed. In particular, we introduce a vocabulary to describe provenance of Web data as metadata and we discuss possibilities to make such provenance metadata accessible as part of the Web of Data. Furthermore, we describe how this metadata can be queried and consumed to identify outdated information.},
archivePrefix = {arXiv},
arxivId = {1406.2495},
author = {Stamatogiannakis, Manolis and Kazmi, Hasanat and Sharif, Hashim and Vermeulen, Remco and Gehani, Ashish and Bos, Herbert and Groth, Paul},
doi = {10.1007/978-3-319-40593-3_3},
eprint = {1406.2495},
file = {:home/andrew/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Stamatogiannakis et al. - 2016 - Trade-Offs in Automatic Provenance Capture.pdf:pdf},
isbn = {978-3-642-34221-9},
issn = {03029743},
keywords = {llvm,provenance,spade,strace,taint tracking},
pages = {29--41},
pmid = {9156564},
title = {{Trade-Offs in Automatic Provenance Capture}},
url = {http://link.springer.com/10.1007/978-3-642-34222-6 http://link.springer.com/10.1007/978-3-319-40593-3{\_}3},
volume = {7525},
year = {2016}
}