Domino: Extracting, Comparing, and Manipulating Subsets Across Multiple Tabular Datasets

Abstract

Answering questions about complex issues often requires analysts to take into account information contained in multiple interconnected datasets. A common strategy in analyzing and visualizing large and heterogeneous data is dividing it into meaningful subsets. Interesting subsets can then be selected and the associated data and the relationships between the subsets visualized. However, neither the extraction and manipulation nor the comparison of subsets is well supported by state-of-the-art techniques. In this paper we present Domino, a novel multiform visualization technique for effectively representing subsets and the relationships between them. By providing comprehensive tools to arrange, combine, and extract subsets, Domino allows users to create both common visualization techniques and advanced visualizations tailored to specific use cases. In addition to the novel technique, we present an implementation that enables analysts to manage the wide range of options that our approach offers. Innovative interactive features such as placeholders and live previews support rapid creation of complex analysis setups. We introduce the technique and the implementation using a simple example and demonstrate scalability and effectiveness in a use case from the field of cancer genomics.

Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations

“…Other multi-typed attributes are represented using techniques such as bar chart and box plot. Domino [16] relates closely to our work as it provides ways of including associated data and their relationships for multiple interconnected heterogeneous datasets. The idea is to use blocks of established visualizations for representing subsets of data that can be assembled through block relationships.…”

Building applications for interactive data exploration in systems biology

“…The sigmajs visualization library has functionality for generating a layout for large networks, but we generate this layout serverside to reduce the computational load on the client. To generate this layout we use the GGally package 20 . By generating the network layout using the compute service we relieve the clients.…”

“…Users can add additional features, such as databases connections or new layouts, through Apps. One such app is cyREST which allows external network creation and analysis through a REST API [13] [14]; Pathfinder for exploring paths in large multivariate graphs [15]; UpSet to visualize and analyse sets, their intersections and aggregates [16]; Entourage and enRoute to explore and visualize biological pathways [17][18]; LineUp to explore rankings of items based on a set of attributes [19]; and Domino for exploring subsets across multiple tabular datasets [20].…”

Getting the Message?

“…This is partly due to a realization that most visualization systems are overlyspecific, and thus not agile or adaptive enough to handle nondeterministic, open-ended data exploration with a higher level goal of decision making or learning [2] [1]. Though recent systems have become substantially more expressive [22] [11], the question of how to effectively evaluate the usefulness of such systems is still open. Researchers recommend that participants in experimental visualization tasks should be allowed to explore the data in any way they choose, creating as many insights as possible, and then measuring their insight with a think-aloud protocol or qualitative measures, such as quantity estimation or distribution characterization.…”