Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items of interest. At the same time, relating an item to the rest of a potentially large table is important. In this work we present Taggle, a tabular visualization technique for exploring and presenting large and complex tables. Taggle takes an item-centric, spreadsheet-like approach, visualizing each row in the source data individually using visual encodings for the cells. At the same time, Taggle introduces data-driven aggregation of data subsets. The aggregation strategy is complemented by interaction methods tailored to answer specific analysis questions, such as sorting based on multiple columns and rich data selection and filtering capabilities. We demonstrate Taggle using a case study conducted by a domain expert on complex genomics data analysis for the purpose of drug discovery.
Pivot tables are another way of dealing with the analysis of large multidimensional data. They enable rotation, that is, pivoting of various data dimensions, and with use of data aggregation, such as sum, average, or count, they provide a quick data overview. Pivot tables are widely available in spreadsheet tools such as Excel and Google Sheets, and similar operations are available as part of libraries and programming languages used to create static tabular figures. Tableau @cite_24 employs pivot table principles for constructing nested matrices, where each matrix can then be represented visually in different ways. While pivot tables are well suited to, for instance, summing up the sales figures of multiple departments, they do not commonly support more nuanced aggregations, such as histograms and box plots.
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In the last several years, large multidimensional 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 multidimensional 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 he constructs complex queries and visualizations.