Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes

Ragan, Eric D.; Endert, Alex; Sanyal, Jibonananda; Chen, Jian

doi:10.1109/tvcg.2015.2467551

Cited by 199 publications

(163 citation statements)

References 79 publications

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“…Several research efforts have explored alternative strategies for creating more "truthful" visualizations. Approaches include integrating multiple perspectives of the same data (see Baldonado, 2000), visualizing errors and uncertainties in the data itself (see Bonnau, 2015), and supporting provenance by making change histories transparent (see Ragan, 2016). In their discussion of critical visualization, Marian Dörk et al discuss potential benefits of subjective visualizations such as empowering creators to have their voices heard (Dörk, 2013).…”

Section: Subjectivity In Visualization and Data Artmentioning

confidence: 99%

Subjectivity in personal storytelling with visualization

Thudt

Périn

Willett

et al. 2022

IDJ

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We explore visualization for personal storytelling and investigate techniques for communicating subjective experiences in personal visual narratives. Personal stories are often subjective and storytellers omit, invent, or embellish details to craft engaging stories or to communicate a perspective. As growing personal data collections allow individuals to leverage visualizations, we explore how personal visual narratives can express subjectivity. From an analysis of personal visualizations created by data enthusiasts, designers, and artists, we collect techniques for deliberately expressing subjectivity during data collection, processing, visual encoding, and presentation. Our results prompt a discussion about the role and potential of subjectivity in personal visual storytelling.

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Section: Subjectivity In Visualization and Data Artmentioning

confidence: 99%

Subjectivity in personal storytelling with visualization

Thudt

Périn

Willett

et al. 2022

IDJ

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“…In the context of data analytics, 'provenance' has been used to refer to the history of changes made to the data and interactions with the interface that occur during the sensemaking process [Ragan et al 2016]. Authors have argued that better logging could help elucidate the sensemaking process [Guo et al 2016] and improve the analytics tools [Alspaugh et al 2014].…”

Section: Provenance and Sensemakingmentioning

confidence: 99%

Mining Redescriptions with Siren

Galbrun

Miettinen

2018

ACM Trans. Knowl. Discov. Data

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In many areas of science, scientists need to find distinct common characterizations of the same objects and, vice versa, to identify sets of objects that admit multiple shared descriptions. For example, in biology, an important task is to identify the bioclimatic constraints that allow some species to survive, that is, to describe geographical regions both in terms of the fauna that inhabits them and of their bioclimatic conditions. In data analysis, the task of automatically generating such alternative characterizations is called redescription mining.If a domain expert wants to use redescription mining in his research, merely being able to find redescriptions is not enough. He must also be able to understand the redescriptions found, adjust them to better match his domain knowledge, test alternative hypotheses with them, and guide the mining process towards results he considers interesting. To facilitate these goals, we introduce Siren, an interactive tool for mining and visualizing redescriptions.Siren allows to obtain redescriptions in an anytime fashion through efficient, distributed mining, to examine the results in various linked visualizations, to interact with the results either directly or via the visualizations, and to guide the mining algorithm toward specific redescriptions. In this paper, we explain the features of Siren and why they are useful for redescription mining. We also propose two novel redescription mining algorithms that improve the generalizability of the results compared to the existing ones.

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“…One critical expectation during iterative model building processes is to be able to document the different models built, compare the variables they contain and their performance (often referred to as provenance [33]). This is in particular important in presenting and defending the decisions made during the modelling iterations and explain how the process converged towards a set of plausible models.…”

Section: 25mentioning

confidence: 99%

Supporting theoretically-grounded model building in the social sciences through interactive visualisation

et al. 2017

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThe primary purpose for which statistical models are employed in the social sciences is to understand and explain phenomena occurring in the world around us. In order to be scientifically valid and actionable, the construction of such models need to be strongly informed by theory. To accomplish this, there is a need for methodologies that can enable scientists to utilise their domain knowledge effectively even in the absence of strong a priori hypotheses or whilst dealing with complex datasets containing hundreds of variables and leading to large numbers of potential models. In this paper, we describe enhanced model building processes in which we use interactive visualisations as the underlying mechanism to facilitate the construction and documentation of theory-driven models. We report our observations from a collaborative project involving social and computer scientists, and identify key roles for visualisation to support model building within the context of social science. We describe a suite of techniques to facilitate the exploration of statistical summaries of input variables, to compare the quality of alternative models, and to keep track of the model-building process. We demonstrate how these techniques operate in coordination to allow social scientists to efficiently generate models that are tightly underpinned by domain specific theory.

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Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes

Cited by 199 publications

References 79 publications

Subjectivity in personal storytelling with visualization

Subjectivity in personal storytelling with visualization

Mining Redescriptions with Siren

Supporting theoretically-grounded model building in the social sciences through interactive visualisation

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