Collaborative Visual Analytics Research Articles

A Study on Collaborative Visual Data Analysis in Augmented Reality with Asymmetric Display Types.

Collaboration is a key aspect of immersive visual data analysis. Due to its inherent benefit of seeing co-located collaborators, augmented reality is often useful in such collaborative scenarios. However, to enable the augmentation of the real environment, there are different types of technology available. While there are constant developments in specific devices, each of these device types provide different premises for collaborative visual data analysis. In our work we combine handheld, optical see-through and video see-through displays to explore and understand the impact of these different device types in collaborative immersive analytics. We conducted a mixed-methods collaborative user study where groups of three performed a shared data analysis task in augmented reality with each user working on a different device, to explore differences in collaborative behaviour, user experience and usage patterns. Both quantitative and qualitative data revealed differences in user experience and usage patterns. For collaboration, the different display types influenced how well participants could participate in the collaborative data analysis, nevertheless, there was no measurable effect in verbal communication.

Visual Analysis of Multisource Heterogeneous Data Based on Improved DPCA Algorithm

This paper proposes a multiview collaborative visual analysis system of network security based on a DPCA (clustering by fast search and find of density peaks) clustering algorithm called DPCANETVis, with network security analysis requirements for multisource heterogeneous data. Firstly, the system proposes an improved DPCA clustering algorithm based on the hierarchical relationship of mail sending and receiving to achieve the purpose of accurate classification. Secondly, a three-layer visual layout is designed to display relevant information such as data hierarchy, node relationship, behavior model, and other relevant information, by mixing a variety of interactive visual analysis methods such as tree diagram, word cloud, line diagram, subject river, and parallel coordinate. Lastly, based on the exploration of events, all suspicious nodes and their abnormal behaviors can be displayed in the system. Finally, the prototype system is used to analyze the network security log data set provided by the ChinaVis 2018, and the feasibility of the multilevel interactive visual analysis method for network security is verified through many experiments and discussions.

MDsrv: visual sharing and analysis of molecular dynamics simulations.

Molecular dynamics simulation is a proven technique for computing and visualizing the time-resolved motion of macromolecules at atomic resolution. The MDsrv is a tool that streams MD trajectories and displays them interactively in web browsers without requiring advanced skills, facilitating interactive exploration and collaborative visual analysis. We have now enhanced the MDsrv to further simplify the upload and sharing of MD trajectories and improve their online viewing and analysis. With the new instance, the MDsrv simplifies the creation of sessions, which allows the exchange of MD trajectories with preset representations and perspectives. An important innovation is that the MDsrv can now access and visualize trajectories from remote datasets, which greatly expands its applicability and use, as the data no longer needs to be accessible on a local server. In addition, initial analyses such as sequence or structure alignments, distance measurements, or RMSD calculations have been implemented, which optionally support visual analysis. Finally, based on Mol*, MDsrv now provides faster and more efficient visualization of even large trajectories compared to its predecessor tool NGL.

Reflections in water: Displaying political agency through costume, performance and video

This research revisits individual and collaborative artistic processes to articulate the combination of creative skills to produce and document research outcomes. Various creative processes, such as costume-making, performance-making, artistic video, photo documentation and editing, came about under particular circumstances and with different objectives. These processes, all with their unique and embedded stories, were brought together in a collaborative research outcome to create an original visual story with layered meanings – the video titled ‘Merelle (To the see)’ (). This video illustrates the connections between the different creative processes, and the memories, bodies, places and environments attached to them. However, some of the places and environments in which the costumes, performance and video came about were also implicit, only to be revealed in the research dissemination. The selected methodology entailed narrative accounts, reflexive research and collaborative visual analysis. The data were collected through storytelling and note taking of the events that enabled was re-narration of the three artistic processes described in this article. The reflexive research methodology and analysis drew on visual data from photography and the video to explore the outcomes of the collaborative work.

CoUX: Collaborative Visual Analysis of Think-Aloud Usability Test Videos for Digital Interfaces

Reviewing a think-aloud video is both time-consuming and demanding as it requires UX (user experience) professionals to attend to many behavioral signals of the user in the video. Moreover, challenges arise when multiple UX professionals need to collaborate to reduce bias and errors. We propose a collaborative visual analytics tool, CoUX, to facilitate UX evaluators collectively reviewing think-aloud usability test videos of digital interfaces. CoUX seamlessly supports usability problem identification, annotation, and discussion in an integrated environment. To ease the discovery of usability problems, CoUX visualizes a set of problem-indicators based on acoustic, textual, and visual features extracted from the video and audio of a think-aloud session with machine learning. CoUX further enables collaboration amongst UX evaluators for logging, commenting, and consolidating the discovered problems with a chatbox-like user interface. We designed CoUX based on a formative study with two UX experts and insights derived from the literature. We conducted a user study with six pairs of UX practitioners on collaborative think-aloud video analysis tasks. The results indicate that CoUX is useful and effective in facilitating both problem identification and collaborative teamwork. We provide insights into how different features of CoUX were used to support both independent analysis and collaboration. Furthermore, our work highlights opportunities to improve collaborative usability test video analysis.

The MADE-Axis

Tangible controls-especially sliders and rotary knobs-have been explored in a wide range of interactive applications for desktop and immersive environments. Studies have shown that they support greater precision and provide proprioceptive benefits, such as support for eyes-free interaction. However, such controls tend to be expressly designed for specific applications. We draw inspiration from a bespoke controller for immersive data visualisation, but decompose this design into a simple, wireless, composable unit featuring two actuated sliders and a rotary encoder. Through these controller units, we explore the interaction opportunities around actuated sliders; supporting precise selection, infinite scrolling, adaptive data representations, and rich haptic feedback; all within a mode-less interaction space. We demonstrate the controllers' use for simple, ad hoc desktop interaction,before moving on to more complex, multi-dimensional interactions in VR and AR. We show that the flexibility and composability of these actuated controllers provides an emergent design space which covers the range of interactive dynamics for visual analysis. In a user study involving pairs performing collaborative visual analysis tasks in mixed-reality, our participants were able to easily compose rich visualisations, make insights and discuss their findings.

Shared Surfaces and Spaces: Collaborative Data Visualisation in a Co-located Immersive Environment.

Immersive technologies offer new opportunities to support collaborative visual data analysis by providing each collaborator a personal, high-resolution view of a flexible shared visualisation space through a head mounted display. However, most prior studies of collaborative immersive analytics have focused on how groups interact with surface interfaces such as tabletops and wall displays. This paper reports on a study in which teams of three co-located participants are given flexible visualisation authoring tools to allow a great deal of control in how they structure their shared workspace. They do so using a prototype system we call FIESTA: the Free-roaming Immersive Environment to Support Team-based Analysis. Unlike traditional visualisation tools, FIESTA allows users to freely position authoring interfaces and visualisation artefacts anywhere in the virtual environment, either on virtual surfaces or suspended within the interaction space. Our participants solved visual analytics tasks on a multivariate data set, doing so individually and collaboratively by creating a large number of 2D and 3D visualisations. Their behaviours suggest that the usage of surfaces is coupled with the type of visualisation used, often using walls to organise 2D visualisations, but positioning 3D visualisations in the space around them. Outside of tightly-coupled collaboration, participants followed social protocols and did not interact with visualisations that did not belong to them even if outside of its owner's personal workspace.

Uplift: A Tangible and Immersive Tabletop System for Casual Collaborative Visual Analytics.

Collaborative visual analytics leverages social interaction to support data exploration and sensemaking. These processes are typically imagined as formalised, extended activities, between groups of dedicated experts, requiring expertise with sophisticated data analysis tools. However, there are many professional domains that benefit from support for short 'bursts' of data exploration between a subset of stakeholders with a diverse breadth of knowledge. Such 'casual collaborative' scenarios will require engaging features to draw users' attention, with intuitive, 'walk-up and use' interfaces. This paper presents Uplift, a novel prototype system to support 'casual collaborative visual analytics' for a campus microgrid, co-designed with local stakeholders. An elicitation workshop with key members of the building management team revealed relevant knowledge is distributed among multiple experts in their team, each using bespoke analysis tools. Uplift combines an engaging 3D model on a central tabletop display with intuitive tangible interaction, as well as augmented-reality, mid-air data visualisation, in order to support casual collaborative visual analytics for this complex domain. Evaluations with expert stakeholders from the building management and energy domains were conducted during and following our prototype development and indicate that Uplift is successful as an engaging backdrop for casual collaboration. Experts see high potential in such a system to bring together diverse knowledge holders and reveal complex interactions between structural, operational, and financial aspects of their domain. Such systems have further potential in other domains that require collaborative discussion or demonstration of models, forecasts, or cost-benefit analyses to high-level stakeholders.

Resolving Conflicting Insights in Asynchronous Collaborative Visual Analysis

AbstractAnalyzing large and complex datasets for critical decision making can benefit from a collective effort involving a team of analysts. However, insights and findings from different analysts are often incomplete, disconnected, or even conflicting. Most existing analysis tools lack proper support for examining and resolving the conflicts among the findings in order to consolidate the results of collaborative data analysis. In this paper, we present CoVA, a visual analytics system incorporating conflict detection and resolution for supporting asynchronous collaborative data analysis. By using a declarative visualization language and graph representation for managing insights and insight provenance, CoVA effectively leverages distributed revision control workflow from software engineering to automatically detect and properly resolve conflicts in collaborative analysis results. In addition, CoVA provides an effective visual interface for resolving conflicts as well as combining the analysis results. We conduct a user study to evaluate CoVA for collaborative data analysis. The results show that CoVA allows better understanding and use of the findings from different analysts.

Tuoris: A middleware for visualizing dynamic graphics in scalable resolution display environments

In the era of big data, large-scale information visualization has become an important challenge. Scalable resolution display environments (SRDEs) have emerged as a technological solution for building high-resolution display systems by tiling lower resolution screens. These systems bring serious advantages, including lower construction cost and better maintainability compared to other alternatives. However, they require specialized software but also purpose-built content to suit the inherently complex underlying systems. This creates several challenges when designing visualizations for big data, such that can be reused across several SRDEs of varying dimensions. This is not yet a common practice but is becoming increasingly popular among those who engage in collaborative visual analytics in data observatories. In this paper, we define three key requirements for systems suitable for such environments, point out limitations of existing frameworks, and introduce Tuoris, a novel open-source middleware for visualizing dynamic graphics in SRDEs. Tuoris manages the complexity of distributing and synchronizing the information among different components of the system, eliminating the need for purpose-built content. This makes it possible for users to seamlessly port existing graphical content developed using standard web technologies, and simplifies the process of developing advanced, dynamic and interactive web applications for large-scale information visualization. Tuoris is designed to work with Scalable Vector Graphics (SVG), reducing bandwidth consumption and achieving high frame rates in visualizations with dynamic animations. It scales independent of the display wall resolution and contrasts with other frameworks that transmit visual information as blocks of images.

InsideInsights: Integrating Data‐Driven Reporting in Collaborative Visual Analytics

AbstractAnalyzing complex data is a non‐linear process that alternates between identifying discrete facts and developing overall assessments and conclusions. In addition, data analysis rarely occurs in solitude; multiple collaborators can be engaged in the same analysis, or intermediate results can be reported to stakeholders. However, current data‐driven communication tools are detached from the analysis process and promote linear stories that forego the hierarchical and branching nature of data analysis, which leads to either too much or too little detail in the final report. We propose a conceptual design for integrated data‐driven reporting that allows for iterative structuring of insights into hierarchies linked to analytic provenance and chosen analysis views. The hierarchies become dynamic and interactive reports where collaborators can review and modify the analysis at a desired level of detail. Our web‐based InsideInsights system provides interaction techniques to annotate states of analytic components, structure annotations, and link them to appropriate presentation views. We demonstrate the generality and usefulness of our system with two use cases and a qualitative expert review.

GeoAnnotator: A Collaborative Semi-Automatic Platform for Constructing Geo-Annotated Text Corpora

Ground-truth datasets are essential for the training and evaluation of any automated algorithm. As such, gold-standard annotated corpora underlie most advances in natural language processing (NLP). However, only a few relatively small (geo-)annotated datasets are available for geoparsing, i.e., the automatic recognition and geolocation of place references in unstructured text. The creation of geoparsing corpora that include both the recognition of place names in text and matching of those names to toponyms in a geographic gazetteer (a process we call geo-annotation), is a laborious, time-consuming and expensive task. The field lacks efficient geo-annotation tools to support corpus building and lacks design guidelines for the development of such tools. Here, we present the iterative design of GeoAnnotator, a web-based, semi-automatic and collaborative visual analytics platform for geo-annotation. GeoAnnotator facilitates collaborative, multi-annotator creation of large corpora of geo-annotated text by generating computationally-generated pre-annotations that can be improved by human-annotator users. The resulting corpora can be used in improving and benchmarking geoparsing algorithms as well as various other spatial language-related methods. Further, the iterative design process and the resulting design decisions can be used in annotation platforms tailored for other application domains of NLP.

A User‐Centered Multi‐space Collaborative Visual Analysis for Cyber Security

Based on a university's practice in upgrading its network management platform, presents a visual analytic system which integrates network topological space, IP space and network geographical space into a collaborative solution to help network administrators address difficulties in locating end user and troubleshooting. Throughout the development cycle, we worked alongside with users to clarify their actual demands and habitual operations through scenario application, interviews and a variety of evaluation. This user-centered approach guides us step by step to apply cyber security visualization and visual analytic technology to actual use.

Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention.

Background: Accurate understanding of complex health data is critical in order to deal with wicked health problems and make timely decisions. Wicked problems refer to ill-structured and dynamic problems that combine multidimensional elements, which often preclude the conventional problem solving approach. This pilot study introduces visual analytics (VA) methods to multi-stakeholder decision-making sessions about child injury prevention; Methods: Inspired by the Delphi method, we introduced a novel methodology—group analytics (GA). GA was pilot-tested to evaluate the impact of collaborative visual analytics on facilitating problem solving and supporting decision-making. We conducted two GA sessions. Collected data included stakeholders’ observations, audio and video recordings, questionnaires, and follow up interviews. The GA sessions were analyzed using the Joint Activity Theory protocol analysis methods; Results: The GA methodology triggered the emergence of ‘common ground’ among stakeholders. This common ground evolved throughout the sessions to enhance stakeholders’ verbal and non-verbal communication, as well as coordination of joint activities and ultimately collaboration on problem solving and decision-making; Conclusions: Understanding complex health data is necessary for informed decisions. Equally important, in this case, is the use of the group analytics methodology to achieve ‘common ground’ among diverse stakeholders about health data and their implications.

Virtual Valcamonica: Collaborative Exploration of Prehistoric Petroglyphs and Their Surrounding Environment in Multi-User Virtual Reality

Abstract In this article, we present a novel, multi-user, virtual reality environment for the interactive, collaborative 3D analysis of large 3D scans and the technical advancements that were necessary to build it: a multi-view rendering system for large 3D point clouds, a suitable display infrastructure, and a suite of collaborative 3D interaction techniques. The cultural heritage site of Valcamonica in Italy with its large collection of prehistoric rock-art served as an exemplary use case for evaluation. The results show that our output-sensitive level-of-detail rendering system is capable of visualizing a 3D dataset with an aggregate size of more than 14 billion points at interactive frame rates. The system design in this exemplar application results from close exchange with a small group of potential users: archaeologists with expertise in rockart. The system allows them to explore the prehistoric art and its spatial context with highly realistic appearance. A set of dedicated interaction techniques was developed to facilitate collaborative visual analysis. A multi-display workspace supports the immediate comparison of geographically distributed artifacts. An expert review of the final demonstrator confirmed the potential for added value in rock-art research and the usability of our collaborative interaction techniques.

Collaborative Visual Analytics of Multi-dimensional and Spatio-temporal Data

Collaborative Visual Analytics of Multi-dimensional and Spatio-temporal Data

OnGraX: A Web-Based System for the Collaborative Visual Analysis of Graphs

The visual analysis of complex networks is a challenging task in many fields, such as systems biology or social sciences. Often, various domain experts work together to improve the analysis time or the quality of the analysis results. Collaborative visualization tools can facilitate the analysis process in such situations. We propose a new web-based visualization environment which supports distributed, synchronous and asynchronous collaboration. In addition to standard collaboration features like event tracking or synchronizing, our client/server-based system provides a rich set of visualization and interaction techniques for better navigation and overview of the input network. Changes made by specific analysts or even just visited network elements are highlighted on demand by heat maps. They enable us to visualize user behavior data without affecting the original graph visualization, are robust against layout changes, and are user-sensitive in a sense that the current analyst is able to perceive which changes were made by others in asynchronous collaboration. In case of synchronous collaboration, an analyst can see where and what others are currently analyzing in the network visualization. Thus, our approach addresses critical collaborative visualization challenges, for instance, awareness and coordination of user activities or pointing to interesting objects. We evaluated the usability of the heat map approach against two alternatives in a controlled user experiment. In addition, the results of a domain expert review are described in this article.

Collaborative visual analytics of radio surveys in the Big Data era

AbstractRadio survey datasets comprise an increasing number of individual observations stored as sets of multidimensional data. In large survey projects, astronomers commonly face limitations regarding: 1) interactive visual analytics of sufficiently large subsets of data; 2) synchronous and asynchronous collaboration; and 3) documentation of the discovery workflow. To support collaborative data inquiry, we present encube, a large-scale comparative visual analytics framework. encube can utilise advanced visualization environments such as the CAVE2 (a hybrid 2D and 3D virtual reality environment powered with a 100 Tflop/s GPU-based supercomputer and 84 million pixels) for collaborative analysis of large subsets of data from radio surveys. It can also run on standard desktops, providing a capable visual analytics experience across the display ecology. encube is composed of four primary units enabling compute-intensive processing, advanced visualisation, dynamic interaction, parallel data query, along with data management. Its modularity will make it simple to incorporate astronomical analysis packages and Virtual Observatory capabilities developed within our community. We discuss how encube builds a bridge between high-end display systems (such as CAVE2) and the classical desktop, preserving all traces of the work completed on either platform – allowing the research process to continue wherever you are.

Visually Framing Press Freedom and Responsibility of a Massacre: Photographic and Graphic Images in Charlie Hebdo's Newspaper Front Pages Around the World

This research examines 441 front-page images published in 367 newspapers on the day following the shooting in Paris of 12 people at or near the Charlie Hebdo office. The aim of this study is to understand how mainstream media visually framed responsibility for the Charlie Hebdo massacre and how these visual frames coalesced to represent collective narratives about press freedom. Through a collaborative visual analysis, this study attempts to understand how the selected visual frames worked to communicate the causes, effects, and responses to the massacre and also to press freedom—an ideological construct that news media had a vested interest in advancing.

Using visual analytics to support online community development

Question and answer communities are an integral part of the web, enabling individuals to find answers to difficult technical questions while reducing the need for companies to host expensive technical support centres. Developing and maintaining these communities is a challenge given how online communities can scale considerably and are subject to continuous change. In this article, we present two case studies in which we investigate how visual analytics could be designed to support different online question and answer communities. In the first study, we explore how collaborative visual analytics can encourage analysis and conversation in the Super User online community while in the second study we investigate how visual analytics can be designed to support a strategic approach to online community management at Symantec. Our findings suggest that visualisation promotes discussion and reflection but does not readily produce actionable or repeatable insights. More effective solutions require a deeper understanding of community processes, enabling more predictive or actionable insights for different community members.