%0 Journal Article %1 OLIVEIRA20171592 %A Oliveira, Weiner %A Ambrósio, Lenitta M. %A Braga, Regina %A Ströele, Victor %A David, José Maria %A Campos, Fernanda %D 2017 %J Procedia Computer Science %K provenance visualization %P 1592 - 1601 %R https://doi.org/10.1016/j.procs.2017.05.216 %T A Framework for Provenance Analysis and Visualization %U http://www.sciencedirect.com/science/article/pii/S1877050917308098 %V 108 %X Data provenance is a fundamental concept in scientific experimentation. However, for their proper understanding and use, efficient and user-friendly mechanisms are needed. Research in software visualization, ontologies and complex networks can help in this process. This paper presents a framework to assist in the understanding and use of data provenance using visualization techniques, ontologies and complex networks. The framework capture the provenance data and generates new information using ontologies and provenance graph analysis. The graph is analyzed through complex networks techniques and provide some metrics to help in each node analyzes. The visualization presents and highlights the inferences and results. The framework was used in the E-SECO scientific ecosystem to support the scientific experimentation.

@article{OLIVEIRA20171592, abstract = {Data provenance is a fundamental concept in scientific experimentation. However, for their proper understanding and use, efficient and user-friendly mechanisms are needed. Research in software visualization, ontologies and complex networks can help in this process. This paper presents a framework to assist in the understanding and use of data provenance using visualization techniques, ontologies and complex networks. The framework capture the provenance data and generates new information using ontologies and provenance graph analysis. The graph is analyzed through complex networks techniques and provide some metrics to help in each node analyzes. The visualization presents and highlights the inferences and results. The framework was used in the E-SECO scientific ecosystem to support the scientific experimentation.}, added-at = {2018-11-27T11:46:18.000+0100}, author = {Oliveira, Weiner and Ambrósio, Lenitta M. and Braga, Regina and Ströele, Victor and David, José Maria and Campos, Fernanda}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2cdc5ccf1fbbde6134160e2fd126e5b6f/diglezakis}, doi = {https://doi.org/10.1016/j.procs.2017.05.216}, interhash = {76a03ad792f1fceda5c8489067d72d0b}, intrahash = {cdc5ccf1fbbde6134160e2fd126e5b6f}, issn = {1877-0509}, journal = {Procedia Computer Science}, keywords = {provenance visualization}, note = {International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland}, pages = {1592 - 1601}, timestamp = {2018-11-27T10:46:18.000+0100}, title = {A Framework for Provenance Analysis and Visualization}, url = {http://www.sciencedirect.com/science/article/pii/S1877050917308098}, volume = 108, year = 2017 }