Exploratory Visualization System Research Articles

HiePaCo: Scalable Hierarchical Exploration in Abstract Parallel Coordinates Under Budget Constraints

Abstract In exploratory visualization systems, interactions allow to manipulate a visual representation and thereby gain insight into its supporting data. The responsiveness of these interactions is crucial, but achieving it on common hardware becomes increasingly difficult with the ever-growing size of datasets. Moreover, the representation of a large dataset itself is challenging since screen space is limited and, past a certain size, the number of items exceeds the number of pixels available or may render the representation unhelpful. The focus of this paper is on multidimensional data and parallel coordinates. For the system to be scalable, we propose a multiscale representation based on hierarchical aggregation on the client-side and distributed computing on a horizontally scalable infrastructure on the server-side. Multiscale visualization builds on several levels of abstraction to provide interactive and incremental changes in the level of detail. Horizontal scalability refers to the ability to increase the resources of the computing infrastructure by connecting additional computers. This paper presents: (1) a graph-based formalism for describing multiscale representations of parallel coordinates and their interactions and (2) a client-server system with a focus+context representation for multiscale parallel coordinates and distributed computation on a remote data-intensive infrastructure. We leverage the proposed formalism to describe several design possibilities for usual interactions in parallel coordinates, hierarchical navigation, and edition. We illustrated the scalability and usage of the representation in a real-world case. Performance experiments demonstrate that on a 15-computer cluster, the prototype system can scale to billion-item datasets while preserving the interactivity for analysis.

Evaluating exploratory visualization systems: A user study on how clustering-based visualization systems support information seeking from large document collections

Iterative, opportunistic and evolving visual sense-making has been an important research topic as it assists users in overcoming ever-increasing information overload. Exploratory visualization systems (EVSs) maximize the amount of information users can gain through learning and have been widely used in scientific discovery and decision-making contexts. Although many EVSs have been developed recently, there is a lack of general guidance on how to evaluate such systems. Researchers face challenges such as understanding the cognitive learning process supported by these systems. In this paper, we present a formal user study on Newdle, a clustering-based EVS for large news collections, shedding light on a general methodology for EVS evaluation. Our approach is built upon cognitive load theory, which takes the user as well as the system as the focus of evaluation. The carefully designed procedures allow us to thoroughly examine the user’s cognitive process as well as control the variability among human subjects. Through this study, we analyse how and why clustering-based EVSs benefit (or hinder) users in a variety of information-seeking tasks. We also summarize leverage points for designing clustering-based EVSs.

Storylines: Visual exploration and analysis in latent semantic spaces

Tasks in visual analytics differ from typical information retrieval tasks in fundamental ways. A critical part of a visual analytics is to ask the right questions when dealing with a diverse collection of information. In this article, we introduce the design and application of an integrated exploratory visualization system called Storylines. Storylines provides a framework to enable analysts visually and systematically explore and study a body of unstructured text without prior knowledge of its thematic structure. The system innovatively integrates latent semantic indexing, natural language processing, and social network analysis. The contributions of the work include providing an intuitive and directly accessible representation of a latent semantic space derived from the text corpus, an integrated process for identifying salient lines of stories, and coordinated visualizations across a spectrum of perspectives in terms of people, locations, and events involved in each story line. The system is tested with the 2006 VAST contest data, in particular, the portion of news articles.

Exploratory visualization research at the University of Massachusetts at Lowell

This paper describes research activities in the University of Massachusetts—Lowell's ongoing Exvis project, a perception-driven approach to the visualization and exploration of data. We discuss the evolution of the Exvis exploratory visualization systems and provide a variety of examples of multidimensional data representations based on visual perception of line texture and color, and on auditory perception of aggregations of complex time-varying sounds. We also discuss future activities.