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.
@cite_24: The study of complex activities such as scientific production and software development often require modeling connections among heterogeneous entities including people, institutions and artifacts. Despite numerous advances in algorithms and visualization techniques for understanding such social networks, the process of constructing network models and performing exploratory analysis remains difficult and time-consuming. In this paper we present Orion, a system for interactive modeling, transformation and visualization of network data. Orion's interface enables the rapid manipulation of large graphs—including the specification of complex linking relationships—using simple drag-and-drop operations with desired node types. Orion maps these user interactions to statements in a declarative workflow language that incorporates both relational operators (e.g., selection, aggregation and joins) and network analytics (e.g., centrality measures). We demonstrate how these features enable analysts to flexibly construct and compare networks in domains such as online health communities, academic collaboration and distributed software development.
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.