Arjun Srinivasan

Natural Language Interfaces for Data Analysis with Visualization: Considering What Has and Could Be Asked

Natural language is emerging as a promising interaction paradigm for data analysis with visualization. Designing and implementing Natural Language Interfaces (NLIs) is a challenging task, however. In addition to being able to process and understand natural language expressions, NLIs for data visuailzation must consider other factors including input modalities, providing input affordances, and explaining system results, among others. In this article, we examine existing NLIs for data analysis with visualization, and compare and contrast them based on the tasks they allow people to perform. We discuss open research opportunities and themes for emerging NLIs in the visualization community. We also provide examples from the existing literature in the broader HCI community that may help explore some of the highlighted themes for future work. Our goal is to assist readers to understand the subtleties and challenges in designing NLIs and encourage the community to think further about NLIs for data analysis with visualization.

ecoxight: Discovery, Exploration, and Analysis of Business Ecosystems Using Interactive Visualization

The term ecosystem is used pervasively in industry, government, and academia to describe the complex, dynamic, hyperconnected nature of many social, economic, and technical systems that exist today. Ecosystems are characterized by a large, dynamic, and heterogeneous set of geospatially distributed entities that are interconnected through various types of relationships. This study describes the design and development of ecoxight, a Web-based visualization platform that provides multiple coordinated views of multipartite, multiattribute, dynamic, and geospatial ecosystem data with novel and rich interaction capabilities to augment decision makers ecosystem intelligence. The design of ecoxight was informed by an extensive multiphase field study of executives. The ecoxight platform not only provides capabilities to interactively explore and make sense of ecosystems but also provides rich visual construction capabilities to help decision makers align their mental model. We demonstrate the usability, utility, and value of our system using multiple evaluation studies with practitioners using socially curated data on the emerging application programming interface ecosystem. We report on our findings and conclude with research implications. Collectively, our study contributes to design science research at the intersection of information systems and strategy and the rapidly emerging field of visual enterprise analytics.

Graphiti: Interactive Specification of Attribute-Based Edges for Network Modeling and Visualization

Network visualizations, often in the form of node-link diagrams, are an effective means to understand relationships between entities, discover entities with interesting characteristics, and to identify clusters. While several existing tools allow users to visualize pre-defined networks, creating these networks from raw data remains a challenging task, often requiring users to program custom scripts or write complex SQL commands. Some existing tools also allow users to both visualize and model networks. Interaction techniques adopted by these tools often assume users know the exact conditions for defining edges in the resulting networks. This assumption may not always hold true, however. In cases where users do not know much about attributes in the dataset or when there are several attributes to choose from, users may not know which attributes they could use to formulate linking conditions. We propose an alternate interaction technique to model networks that allows users to demonstrate to the system a subset of nodes and links they wish to see in the resulting network. The system, in response, recommends conditions that can be used to model networks based on the specified nodes and links. In this paper, we show how such a demonstration-based interaction technique can be used to model networks by employing it in a prototype tool, Graphiti. Through multiple usage scenarios, we show how Graphiti not only allows users to model networks from a tabular dataset but also facilitates updating a pre-defined network with additional edge types.

Understanding Alliance Portfolios Using Visual Analytics

In an increasingly global and competitive business landscape, firms must collaborate and partner with others to ensure survival, growth, and innovation. Understanding the evolutionary composition of a firm’s relationship portfolio and the underlying formation strategy is a difficult task given the multidimensional, temporal, and geospatial nature of the data. In collaboration with senior executives, we iteratively determine core design requirements and then design and implement an interactive visualization system that enables decision makers to gain both systemic (macro) and detailed (micro) insights into a firm’s alliance activities and discover patterns of multidimensional relationship formation. Our system provides both sequential and temporal representation modes, a rich set of additive cross-linked filters, the ability to stack multiple alliance portfolios, and a dynamically updated activity state model visualization to inform decision makers of past and likely future relationship moves. We illustrate our tool with examples of alliance activities of firms listed on the S8P 500. A controlled experiment and real-world evaluation with practitioners and researchers reveals significant evidence of the value of our visual analytic tool. Our design study contributes to design science by addressing a known problem (i.e., alliance portfolio analysis) with a novel solution (interactive, pixel-based multivariate visualization) and to the rapidly emerging area of data-driven visual decision support in corporate strategy contexts. We conclude with implications and future research opportunities.

Tangraphe: interactive exploration of network visualizations using single hand, multi-touch gestures

Touch-based displays are becoming a popular medium for interacting with visualizations. Network visualizations are a frequently used class of visualizations across domains to explore entities and relationships between them. However, little work has been done in exploring the design of network visualizations and corresponding interactive tasks such as selection, browsing, and navigation on touch-based displays. Network visualizations on touch-based displays are usually implemented by porting the conventional pointer based interactions as-is to a touch environment and replacing the mouse cursor with a finger. However, this approach does not fully utilize the potential of naturalistic multi-touch gestures afforded by touch displays. We present a set of single hand, multi-touch gestures for interactive exploration of network visualizations and employ these in a prototype system, Tangraphe. We discuss the proposed interactions and how they facilitate a variety of commonly performed network visualization tasks including selection, navigation, adjacency-based exploration, and layout modification. We also discuss advantages of and potential extensions to the proposed set of one-handed interactions including leveraging the non-dominant hand for enhanced interaction, incorporation of additional input modalities, and integration with other devices.

Multimodal interaction for data visualization

Multimodal interaction offers many potential benefits for data visualization. It can help people stay in the flow of their visual analysis and presentation, with the strengths of one interaction modality offsetting the weaknesses of others. Furthermore, multimodal interaction offers strong promise for leveraging data visualization on diverse display hardware including mobile, AR/VR, and large displays. However, prior research on visualization and interaction techniques has mostly explored a single input modality such as mouse, touch, pen, or more recently, natural language. The unique challenges and opportunities of synergistic multimodal interaction for data visualization have yet to be investigated. This workshop will bring together researchers with expertise in visualization, interaction design, and natural user interfaces. We aim to build a community of researchers focusing on multimodal interaction for data visualization, explore opportunities and challenges in our research, and establish an agenda for multimodal interaction research specifically for data visualization.