Provenance Analysis for Sensemaking. Fekete, J.; Jankun-Kelly, T. J.; Tory, M.; and Xu, K. IEEE Computer Graphics and Applications, 39(6):27–29, November, 2019.
doi  abstract   bibtex   
The articles in this special section examine the concept of "sensemaking", which refers to how we structure the unknown so as to be able to act in it. In the context of data analysis it involves understanding the data, generating hypotheses, selecting analysis methods, creating novel solutions, and critical thinking and learning wherever needed. Due to its explorative and creative nature, sensemaking is arguably the most challenging part of any data analysis.
@article{fekete_provenance_2019,
	title = {Provenance {Analysis} for {Sensemaking}},
	volume = {39},
	issn = {1558-1756},
	doi = {10.1109/MCG.2019.2945378},
	abstract = {The articles in this special section examine the concept of "sensemaking", which refers to how we structure the unknown so as to be able to act in it. In the context of data analysis it involves understanding the data, generating hypotheses, selecting analysis methods, creating novel solutions, and critical thinking and learning wherever needed. Due to its explorative and creative nature, sensemaking is arguably the most challenging part of any data analysis.},
	number = {6},
	journal = {IEEE Computer Graphics and Applications},
	author = {Fekete, Jean-Daniel and Jankun-Kelly, T. J. and Tory, Melanie and Xu, Kai},
	month = nov,
	year = {2019},
	keywords = {Type of Work: Survey},
	pages = {27--29},
	file = {IEEE Xplore Full Text PDF:C\:\\Users\\conny\\Zotero\\storage\\9B5URJ2C\\Fekete et al. - 2019 - Provenance Analysis for Sensemaking.pdf:application/pdf}
}
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