DataSplash: A Direct Manipulation Environment for Programming Semantic Zoom Visualizations of Tabular Data

Woodruff, Allison; Olston, Christopher; Aiken, Alexander; Chu, Michael; Ercegovac, Vuk; Lin, Mark; Spalding, Mybrid

doi:10.1006/jvlc.2001.0219

Cited by 17 publications

(13 citation statements)

References 17 publications

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“…In contrast, both Sage and Tioga-2 / DataSplash [Aiken et al 1996] [Woodruff et al 2001] focus more on how flexible the data transformations need to be, and, specifically, that data transformations need to be composable. This composability is needed for providing the maximal flexibility to the analyst, especially with the integration with visualization, so that the every intermediate result can be seen visually.…”

Section: What Issues Are Involved When Implementing Data Transformatimentioning

confidence: 99%

“…Related Work: In existing general-purpose visualization tools, the most common data model choice is indeed the relational model. This model is used in Snap-Together Visualizations [North et al 2002], DEVise [Livny et al 1997] [Livny et al 1996], Tioga-2 / DataSplash [Aiken et al 1996] [Woodruff et al 2001], Sage / Visage , and VisDB [Keim and Kriegel 1994]. These tools focus primarily on normalized relational data, since they focus significant effort on how to let the user easily perform joins (a nontrivial problem that Rivet does not tackle).…”

Section: Data Model and Representationmentioning

confidence: 99%

“…Related Work: Many existing visualization systems focus on the issues that arise after the data is already in the system and do not discuss data access extensively. Snap-Together Visualizations [North et al 2002], and Tioga-2 / DataSplash [Aiken et al 1996] [Woodruff et al 2001] mention their reliance on data stored in databases. DEVise [Livny et al 1997] [Livny et al 1996] is one exception: they discuss how to support accessing large data sets that do not fit in memory and how to connect to both databases and alternative data sources.…”

Section: Generalized Interface For Data Accessmentioning

confidence: 99%

“…For example, COUNT(DISTINCT(Country)) is an ordered, integral field while the source field, Country, is an unordered, nominal field. Related Work: Many existing systems, such as Snap-Together Visualizations [North et al 2002], DEVise [Livny et al 1997] [Livny et al 1996], and Tioga-2 / DataSplash [Aiken et al 1996] [Woodruff et al 2001], use the meta-data information provided in the database schema, which typically consists of the field name, storage type, actual domains, and hierarchical relationships. Exbase [Lee and Grinstein 1995] discusses the need for additional meta-data information, but focuses on information that can be automatically inferred from the database or the query.…”

Section: Sophisticated Meta-datamentioning

confidence: 99%

“…Related Work: Many systems, including Sage , DEVise [Livny et al 1997] [Livny et al 1996], and Tioga-2 / DataSplash [Aiken et al 1996] [Woodruff et al 2001], use the idea of visual encodings to create an abstraction between the data and the corresponding visual representations.…”

Section: Visual Encodingsmentioning

confidence: 99%

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Design choices when architecting visualizations

Tang

Stolte

Bosche

IEEE Symposium on Information Visualization 2003 (IEEE Cat. No.03TH8714)

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In this paper, we focus on some of the key design decisions we faced during the process of architecting a visualization system and present some possible choices, with their associated advantages and disadvantages. We frame this discussion within the context of Rivet, our general visualization environment designed for rapidly prototyping interactive, exploratory visualization tools for analysis. As we designed increasingly sophisticated visualizations, we needed to refine Rivet in order to be able to create these richer displays for larger and more complex data sets.The design decisions we discuss in this paper include: the internal data model, data access, semantic meta-data information the visualization can use to create effective visual encodings, the need for data transformations in a visualization tool, modular objects for flexibility, and the tradeoff between simplicity and expressiveness when providing methods for creating visualizations.

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Section: What Issues Are Involved When Implementing Data Transformatimentioning

confidence: 99%