In the last several years, large multi-dimensional

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.

Abstract-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 users' history logs and how they have been applied to study usage patterns in Tableau.Index Terms-Visualization, history, undo, analysis, presentation, evaluation. INTRODUCTIONWhen investigating data with visualizations, users regularly traverse the space of views in an iterative fashion. Exploratory analysis may result in a number of hypotheses, leading to multiple rounds of question-answering. Analysts can generate unexpected questions that may be investigated immediately or revisited later. After conducting analysis, users may need to review, summarize, and communicate their findings, often in the form of reports or presentations. By surfacing users' interaction history, we can facilitate analysis and communication. History mechanisms such as undo or "timetravel" enable revisitation in a variety of applications (e.g., [1-4, 6, 8, 12-14, 16-20, 22, 24-26]). As noted by Shneiderman [27], such history tools can play an important part in the visualization process, supporting iterative analysis by enabling users to review, retrieve, and revisit visualization states. Moreover, history tools can help users create reports or presentations, facilitating communication.Interaction histories can also benefit research and development. History log analysis of both individual and aggregate usage can identify common usage patterns and thereby assist usability evaluation. Researchers can also study interaction patterns to better understand and model analysts' sense-making process [13].However, the best history mechanisms for achieving these benefits are not always clear. Designers of visualization tools must consider a large design space of potential features and system architectures when designing history tools. These design decisions entail trade-offs in the types of history representations and operations that can be provided.For example, while it is easy to log low-level input events such as key presses and mouse clicks [25], users can more readily take advantage of semantically meaningful models. Many operations might be performed on an interaction history, including editing, aggregation, bookmarking, annotation, and search. Architecture and interface design need to account for such operations. ...

In the last several years, large multi-dimensional databases have become common in a variety of applications such as data warehousing and scientific computing. Analysis and exploration tasks place significant demands on the interfaces to these databases. Because of the size of the data sets, dense graphical representations are more effective for exploration than spreadsheets and charts. Furthermore, because of the exploratory nature of the analysis, it must be possible for the analysts to change visualizations rapidly as they pursue a cycle involving first hypothesis and then experimentation. In this paper we present Polaris, an interface for exploring large multi-dimensional databases that extends the well-known Pivot Table interface. The novel features of Polaris include an interface for constructing visual specifications of table-based graphical displays and the ability to generate a precise set of relational queries from the visual specifications. The visual specifications can be rapidly and incrementally developed, giving the analyst visual feedback as they construct complex queries and visualizations.

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Most analysts start with an overview of the data before gradually refining their view to be more focused and detailed. Multiscale panand-zoom

Most analysts start with an overview of the data before gradually refining their view to be more focused and detailed. Multiscale panand-zoom

Tableau is a commercial business intelligence (BI) software tool that supports interactive, visual analysis of data. Armed with a visual interface to data and a focus on usability, Tableau enables a wide audience of end-users to gain insight into their datasets. The user experience is a fluid process of interaction in which exploring and visualizing data takes just a few simple drag-and-drop operations (no programming or DB experience necessary). In this context of exploratory, ad-hoc visual analysis, we describe a novel approach to integrating large, heterogeneous data sources. We present a new feature in Tableau called data blending, which gives users the ability to create data visualization mashups from structured, heterogeneous data sources dynamically without any upfront integration effort. Users can author visualizations that automatically integrate data from a variety of sources, including data warehouses, data marts, text files, spreadsheets, and data cubes. Because our data blending system is workload driven, we are able to bypass many of the painpoints and uncertainty in creating mediated schemas and schema-mappings in current pay-as-you-go integration systems.