http://www.cs.unibo.it/~montesi/CBD/Articoli/SurvyBigData2.pdf

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

Data visualization is regularly promoted for its ability to reveal stories within data, yet these “data stories” differ in important ways from traditional forms of storytelling. Storytellers, especially online journalists, have increasingly been integrating visualizations into their narratives, in some cases allowing the visualization to function in place of a written story. In this paper, we systematically review the design space of this emerging class of visualizations. Drawing on case studies from news media to visualization research, we identify distinct genres of narrative visualization. We characterize these design differences, together with interactivity and messaging, in terms of the balance between the narrative flow intended by the author (imposed by graphical elements and the interface) and story discovery on the part of the reader (often through interactive exploration). Our framework suggests design strategies for narrative visualization, including promising under-explored approaches to journalistic storytelling and educational media.

/pdf/narrative-visualization-telling-stories-with-data-28jrfdhr69.pdf

Narrative Visualization: Telling Stories with Data

Big data

Understanding perception is critical to effective visualization design. With its low cost and scalability, crowdsourcing presents an attractive option for evaluating the large design space of visualizations; however, it first requires validation. In this paper, we assess the viability of Amazon's Mechanical Turk as a platform for graphical perception experiments. We replicate previous studies of spatial encoding and luminance contrast and compare our results. We also conduct new experiments on rectangular area perception (as in treemaps or cartograms) and on chart size and gridline spacing. Our results demonstrate that crowdsourced perception experiments are viable and contribute new insights for visualization design. Lastly, we report cost and performance data from our experiments and distill recommendations for the design of crowdsourced studies.

http://www.cs.kent.edu/~javed/class-P2P12F/papers-2012/PAPER2012-2010-MTurk-CHI.pdf

Crowdsourcing graphical perception: using mechanical turk to assess visualization design

The considerable previous work characterizing visualization usage has focused on low-level tasks or interactions and high-level tasks, leaving a gap between them that is not addressed. This gap leads to a lack of distinction between the ends and means of a task, limiting the potential for rigorous analysis. We contribute a multi-level typology of visualization tasks to address this gap, distinguishing why and how a visualization task is performed, as well as what the task inputs and outputs are. Our typology allows complex tasks to be expressed as sequences of interdependent simpler tasks, resulting in concise and flexible descriptions for tasks of varying complexity and scope. It provides abstract rather than domain-specific descriptions of tasks, so that useful comparisons can be made between visualization systems targeted at different application domains. This descriptive power supports a level of analysis required for the generation of new designs, by guiding the translation of domain-specific problems into abstract tasks, and for the qualitative evaluation of visualization usage. We demonstrate the benefits of our approach in a detailed case study, comparing task descriptions from our typology to those derived from related work. We also discuss the similarities and differences between our typology and over two dozen extant classification systems and theoretical frameworks from the literatures of visualization, human-computer interaction, information retrieval, communications, and cartography.

/pdf/a-multi-level-typology-of-abstract-visualization-tasks-3h04dmobom.pdf

A Multi-Level Typology of Abstract Visualization Tasks

This paper describes Show Me, an integrated set of user interface commands and defaults that incorporate automatic presentation into a commercial visual analysis system called Tableau. A key aspect of Tableau is VizQL, a language for specifying views, which is used by Show Me to extend automatic presentation to the generation of tables of views (commonly called small multiple displays). A key research issue for the commercial application of automatic presentation is the user experience, which must support the flow of visual analysis. User experience has not been the focus of previous research on automatic presentation. The Show Me user experience includes the automatic selection of mark types, a command to add a single field to a view, and a pair of commands to build views for multiple fields. Although the use of these defaults and commands is optional, user interface logs indicate that Show Me is used by commercial users.

/pdf/show-me-automatic-presentation-for-visual-analysis-5cot642rd2.pdf

Show Me: Automatic Presentation for Visual Analysis

Interactive history tools, ranging from basic undo and redo to branching timelines of user actions, facilitate iterative forms of interaction. In this paper, we investigate the design of history mechanisms for information visualization. We present a design space analysis of both architectural and interface issues, identifying design decisions and associated trade-offs. Based on this analysis, we contribute a design study of graphical history tools for Tableau, a database visualization system. These tools record and visualize interaction histories, support data analysis and communication of findings, and contribute novel mechanisms for presenting, managing, and exporting histories. Furthermore, we have analyzed aggregated collections of history sessions to evaluate Tableau usage. We describe additional tools for analyzing userspsila history logs and how they have been applied to study usage patterns in Tableau.

/pdf/graphical-histories-for-visualization-supporting-analysis-4b4p4171iv.pdf

Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation

A computer-implemented method of generating marks in a graphical view includes receiving a first user request to associate a first set of data samples with a first visual mark property. In response to the first user request, area marks are displayed in a graphical view. Respective area marks correspond to respective data samples in the first set. A second user request is received to associate the first set of data samples with a second visual mark property. In response to the second user request, point marks are displayed in the graphical view. Respective point marks correspond to respective data samples in the first set.

Methods and systems of automatically generating marks in a graphical view

A method for automatically forming the clearest and most useful visual plot for a given dataset of tuples. A best view type is selected for a view that includes a subsequently added new field. The visual plot is populated with the data in the view and then automatically rendered for the user. A dataset that is retrieved from a storage is analyzed to identify all the data types found in the dataset, and to determine the best view type to assign to the dataset's views. The visual plot is then populated with the data according to this best view type, and is automatically rendered for the user.

Computer systems and methods for automatically viewing multidimensional databases

A method executes at an electronic device with a display, concurrently displaying a chart that displays visual marks representing a set of data and a plurality of analytic icons. The method detects a first portion of an input on a first analytic icon, and in response displays one or more option icons that correspond to options for performing a first analytical operation that corresponds to the first analytic icon. The method also detects a second portion of the input on the first analytic icon and in response moves the first analytic icon over a respective option icon such that the first analytic icon is over the respective option icon immediately prior to ceasing to detect the input. In addition, the method adds one or more graphics to the chart that correspond to the first analytical operation and a respective option that corresponds to the respective option icon.

Christopher Richard Stolte

Papers

Show Me: Automatic Presentation for Visual Analysis

Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation

Methods and systems of automatically generating marks in a graphical view

Computer systems and methods for automatically viewing multidimensional databases

Systems and Methods for Providing Drag and Drop Analytics in a Dynamic Data Visualization Interface