Atkinson, Malcolm

Cheney, James

Filgueira Vicente, Rosa

Spinuso, Alessandro

other

2018-10-30T10:19:01Z

2018-10-30T10:19:01Z

2018-11-29

The role of provenance information in data-intensive research is a significant topic of discussion among technical experts and scientists. Typical use cases addressing traceability, versioning and reproducibility of the research findings are extended with more interactive scenarios in support, for instance, of computational steering and results management. In this thesis we investigate the impact that lineage records can have on the early phases of the analysis, for instance performed through near-real-time systems and Virtual Research Environments (VREs) tailored to the requirements of a specific community. By positioning provenance at the centre of the computational research cycle, we highlight the importance of having mechanisms at the data-scientists’ side that, by integrating with the abstractions offered by the processing technologies, such as scientific workflows and data-intensive tools, facilitate the experts’ contribution to the lineage at runtime. Ultimately, by encouraging tuning and use of provenance for rapid feedback, the thesis aims at improving the synergy between different user groups to increase productivity and understanding of their processes. We present a model of provenance, called S-PROV, that uses and further extends PROV and ProvONE. The relationships and properties characterising the workflow’s abstractions and their concrete executions are re-elaborated to include aspects related to delegation, distribution and steering of stateful streaming operators. The model is supported by the Active framework for tuneable and actionable lineage ensuring the user’s engagement by fostering rapid exploitation. Here, concepts such as provenance types, configuration and explicit state management allow users to capture complex provenance scenarios and activate selective controls based on domain and user-defined metadata. We outline how the traces are recorded in a new comprehensive system, called S-ProvFlow, enabling different classes of consumers to explore the provenance data with services and tools for monitoring, in-depth validation and comprehensive visual-analytics. The work of this thesis will be discussed in the context of an existing computational framework and the experience matured in implementing provenance-aware tools for seismology and climate VREs. It will continue to evolve through newly funded projects, thereby providing generic and user-centred solutions for data-intensive research.

http://hdl.handle.net/1842/33181

en

The University of Edinburgh

A. Spinuso, J. Cheney, and M. Atkinson. Provenance for seismological processing pipelines in a distributed streaming workflow. Proceedings of the Joint EDBT/ICDT 2013 Workshops, 2013.

S. Gesing, M. Atkinson, R. Filgueira, I. Taylor, A. Jones, V. Stankovski, C. S. Liew, A. Spinuso, G. Terstyanszky, and P. Kacsuk. Workflows in a dashboard: a new generation of usability. In Proceedings of the 9th Workshop on Workflows in Support of Large-Scale Science, pages 82-93. IEEE Press, 2014.

R. Filgueira, A. Krause, M. Atkinson, I. Klampanos, A. Spinuso, and S. Sanchez- Exposito. dispel4py: An agile framework for data-intensive escience. In 11th IEEE International Conference on e-Science, pages 454-464. IEEE, 2015.

M. Atkinson, M. Carpen´e, E. Casarotti, S. Claus, R. Filgueira, A. Frank, M. Galea, T. Garth, A. Gem¨u nd, H. Igel, I. Klampanos, A. Krause, L. Krischer, S. H. Leong, F. Magnoni, J. Matser, A. Michelini, A. Rietbrock, H. Schwichtenberg, A. Spinuso, and J. P. Vilotte. VERCE delivers a productive e-science environment for seismology research. In 2015 IEEE 11th International Conference on e-Science, pages 224-236, Aug 2015.

T. Garth, A. Rietbrock, S. Hicks, A. Fuenzalida Velasco, E. Casarotti, and A. Spinuso. Full waveform modelling using the VERCE platform-application to aftershock seismicity in the chile subduction zone. In EGU General Assembly, Conference Abstracts, volume 17, 2015.

A. Spinuso, R. Filgueira, M. Atkinson, and A. Gem¨und. Visualisation methods for large provenance collections in data-intensive collaborative platforms. In EGU General Assembly Conference Abstracts, volume 18, 2016.

T. Kiss, P. Kacsuk, R. Lovas, A. Balask´o, A. Spinuso, M. Atkinson, D. D’Agostino, E. Danovaro, and M. Schiffers.WS-PGRADE/gUSE in European projects. In Kacsuk [162], pages 235-254.

A. Mihajlovski, A. Spinuso, M. Plieger, andW. Som de Cerff. Enabling datadriven provenance in NetCDF, via OGC WPS operations. climate analysis services use case. In AGU Fall Meeting Abstracts, 2016.

informatics

data science

reproducibility

provenance

workflows

e-science

Active provenance for data intensive research

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy