Program

We are excited to announce our three Keynote Speakers.

The conference will take place Wednesday to Thursday, October 17 to 18.

Also, we are planning to have now four Workshops running partly in parallel on Friday, October 19.

Reception on Tuesday Evening, 16.10., in Salzbüchsle!

The registration will take place at the Salzbüchsle from  6:00 to 7:30 pm!

http://www.salzbuechsle.de/DE/index.html

Preliminary Program

The main conference room is C 425. There will be signs starting from the main entrance where the buses line 9 and 11 arrive. Workshop rooms for Friday will differ, please check out the corresponding workshop websites.

 

 

Wednesday, 17.10.2018

08:30

Registration opens

09:15 – 09:30

Conference Opening

09:30 – 10:30

Keynote I: Nicolas Holliman

10:30 – 11:00

Morning Coffee Break

11:00 – 12:00

Immersive Navigation and Storytelling

11:00 – 11:30

Revealing the Invisible: Visual Analytics and Explanatory Storytelling for Advanced Team Sport Analysis – Manuel Stein, Thorsten Breitkreutz, Johannes Häußler, Daniel Seebacher, Christoph Niederberger, Tobias Schreck, Michael Grossniklaus, Daniel Keim and Halldor Janetzko

11:30 – 12:00

Evaluating Navigation Techniques for Large Data Visualizations in VR – Adam Drogemuller, Andrew Cunningham, James Walsh, Bruce Thomas and Maxime Cordeil

12:00 – 12:30

Visual Analytics: Exploration I

12:00 – 12:30

Casual Visual Exploration of Large Bipartite Graphs Using Hierarchical Aggregation and Filtering – Daniel
böck, Eduard Gröller and Manuela Waldner

12:30 – 14:00

Lunch

14:00 – 15:30

Visual Analytics: Applications

14:00 – 14:30

OCP – Operative Curricular Planning: A Visual Decision Support System for Planning Teaching Resources – Raphael Sahann and Torsten Möller

14:30 – 15:00

REMatch: Research Expert Matching System –
Md. Iqbal Hossain, Stephen Kobourov, Helen Purchase and Mihai Surdeanu

15:00 – 15:30

Visual Analytics for Building Management –
Arnaud Prouzeau, Tim Dwyer, Manivannan Balasubramaniam, Joshua Henry, Dharshini.M.B Anu and Ngọc Hoàng

15:30 – 16:00

Afternoon Coffee Break

16:00 – 17:00

Keynote II: Sheelagh Carpendale

17:00 – 17:30

Visual Analytics: Exploration II

17:00 – 17:30

Towards Visual Exploration of Large Temporal Datasets – Mohammed Ali, Mark Jones, Xianghua Xie and Mark Williams

19:00 – 22:00

Banquet in Constanzer Wirtshaus + Best Paper Award

Thursday, 18.10.2018

09:00 – 09:30

Theoretical Foundations

09:00 – 09:30

VISupply: A Supply Chain Framework for Visualization Guidelines – Ulrich Engelke, Alfie Abdul-Rahman and Min Chen

09:30 – 10:30

Text Media Analysis

09:30 – 10:00

LTMA: Layered Topic Matching for the Comparative Exploration, Evaluation, and Refinement of Topic Modeling – Mennatallah El-Assady, Fabian Sperrle, Rita Sevastjanova, Michael Sedlmair and Daniel Keim

10:00 – 10:30

LabelTransfer – Integrating Static and Dynamic Label Representation for Focus+Context Text Exploration – Qi Han, Markus John, Steffen Koch, Ivan Assenov and Thomas Ertl

10:30 – 11:00

Morning Coffee Break

11:00 – 12:30

Immersive Analytics: Display

11:00 – 11:30

Building Multiple Coordinated Spaces for Effective Immersive Analytics through Distributed Cognition – Tahir Mahmood, Erik Butler, Nicholas Davis, Jian Huang and Aidong Lu

11:30 – 12:00

Axes and Coordinate Systems Representations for Immersive Analytics of Multi-Dimensional Data – Adrien Fonnet, Toinon Vigier, Grégoire Cliquet, Fabien Picarougne and Yannick Prié

12:00 – 12:30

Visual Analytics on Large Displays: Exploring User Spatialization and How Size and Resolution Affect Task Performance – Gokhan Cetin, Wolfgang Stürzlinger and John Dill

12:30 – 14:00

Lunch Break

14:00 – 15:00

Keynote III: Paul Cairns

15:00 – 16:00

Visual Analytics: Pipeline

15:00 – 15:30

Pattern-Driven Exploration of Big Data – Michael Behrisch, Robert Krueger, Fritz Lekschas, Tobias Schreck, Nils Gehlenborg and Hanspeter Pfister

15:30 – 16:00

Multiple Workspaces in Visual Analytics – Maha El Meseery, Yuyao Wu and Wolfgang Stürzlinger

16:00 – 16:30

Afternoon Coffee Break

16:30 – 18:00

Geo-related Visual and Immersive Analytics

16:30 – 17:00

SocialOcean: Visual Analysis and Characterization of Social Media Bubbles – Alexandra Diehl, Michael Hundt, Johannes Häußler, Daniel Seebacher, Siming Chen, Nida Cilasun, Daniel Keim and Tobias Schreck

17:00 – 17:30

Immersive Visualisation of Geo-Temporal Narratives in Law Enforcement – Andrew Cunningham, James Walsh and Bruce Thomas

17:30 – 18:00

Tangible Braille Plot: Tangibly Exploring Geo-Temporal Data in Virtual Reality –
James Walsh, Andrew Cunningham, Ross Smith and Bruce Thomas

18:00 – 18:15

Conference Closing

 

Friday, 19.10.2018

08:30

Workshops Registration opens

09:00 – 11:30

Session 1

VGI Geovisual Analytics Workshop I

Introduction to IATK: An Immersive Visual Analytics Toolkit

TRR Workshop: Visualizations + Interactions + Workflows = Data Science for Everyone

Morning Coffee Break

VGI Geovisual Analytics Workshop I

Introduction to IATK: An Immersive Visual Analytics Toolkit

TRR Workshop: Visualizations + Interactions + Workflows = Data Science for Everyone

Lunch Break

13:00 – 15:30

Session 2

VGI Geovisual Analytics Workshop II

Glyphs-based visualization: Opportunities and Challenges

Immersive Molecular Modeling Workshop

Afternoon Coffee Break

VGI Geovisual Analytics Workshop II

Glyphs-based visualization: Opportunities and Challenges

Immersive Molecular Modeling Workshop

Accepted Paper Abstracts

Immersive Navigation and Storytelling

Manuel Stein, Thorsten Breitkreutz, Johannes Häußler, Daniel Seebacher, Christoph Niederberger, Tobias Schreck, Michael Grossniklaus, Daniel Keim and Halldor Janetzko.

Revealing the Invisible: Visual Analytics and Explanatory Storytelling for Advanced Team Sport Analysis

Abstract: The analysis of invasive team sports often concentrates on cooperative and competitive aspects of collective movement behavior. A main goal is the identification and explanation of strategies, and eventually the development of new strategies. In visual sports analytics, a range of different visual-interactive analysis techniques have been proposed, e.g., based on visualization using for example trajectories, graphs, heatmaps, and animations. Identifying suitable visualizations for a specific situation is key to a successful analysis. Existing systems enable the interactive selection of different visualization facets to support the analysis process. However, an interactive selection of appropriate visualizations is a difficult, complex, and time-consuming task. In this paper, we propose a four-step analytics methodology for an automatic selection of appropriate views for key situations in soccer games. Our methodology covers classification, specification, explanation, and alteration of match situations, effectively enabling the analysts to focus on important game situations and the determination of alternative moves. Combining abstract visualizations with real world video recordings by Immersive Visual Analytics and descriptive storylines, we support domain experts in understanding key situations. We demonstrate the usefulness of our proposed methodology via two proofs of concept and evaluate our system by comparing our results to manual video annotations by domain experts. Initial expert feedback shows that our proposed methodology improves the understanding of competitive sports and leads to a more efficient data analysis.

 

Adam Drogemuller, Andrew Cunningham, James Walsh, Bruce Thomas and Maxime Cordeil.

Evaluating Navigation Techniques for Large Data Visualizations in VR

Abstract: For new and complex emerging data visualizations, research has been ongoing into how Virtual Reality can be a beneficial tool for Data Scientists and Analysts to review and visualize large sets of data. The purpose of this paper is to review approaches made in the past to navigate 3-D visualizations in Virtual Reality and test their effectiveness with a Large Graph Visualization as a use case for a large data visualization. We evaluate two prominent navigation techniques employed in VR (Teleportation and One-Handed Flying) against more obscure navigations (Two-Handed Flying and Worlds In Miniature) and evaluate their performance and effectiveness through a series of tasks through a Large Graph Visualization.

Immersive Analytics: Display 

Tahir Mahmood, Erik Butler, Nicholas Davis, Jian Huang and Aidong Lu.

Building Multiple Coordinated Spaces for Effective Immersive Analytics through Distributed Cognition

Abstract: Multiple coordinated views (MCV) has been widely used in visualization techniques. This work explores the 3D version, multiple coordinated spaces (MCS), for utilizing a large physical environment that integrates various 2D displays as the analysis workspace. Built upon the rich background of distributed and embodied cognition, we provide MCS to support interactive analysis in a connected, distributed set of subspaces. We also provide visualization and interactive techniques for coordinating classical WIMP GUIs systems and augmented reality devices. Using a multivariate, geo-spatial application of biodiversity, we demonstrate the flexibility of MCS on revealing various complex data correlations. The major advantage of MCS is a flexible coordination framework for creating new immersive analytics methods through mixing visualizations from different devices, as well as physical and virtual operations from different environments.

 

Adrien Fonnet, Toinon Vigier, Grégoire Cliquet, Fabien Picarougne and Yannick Prié.

Axes and Coordinate Systems Representations for Immersive Analytics of Multi-Dimensional Data

Abstract: Axes are the main components of coordinate systems representations. They play a critical role for the visual analysis of multi-dimensional data. However their representation seems to have always be considered self evident, with oriented lines crossing at an origin, completed with labels such as ticks and names. Such classical representation show limits when it comes 3D visualization and immersive analytic (IA), mainly because orthogonal projection of points on linear axes is hard in a 3d environment, and because the user can move therefore the axes can get out of his field of view. In this paper we propose a task-based definition of axes and coordinate systems representation, as well as a tentative design space for coordinates systems representation in immersion. We also present an exploratory user study we carried out to compare three grid-based representations of coordinate systems for multidimensional data analysis with 3D scatterplots.

 

Gokhan Cetin, Wolfgang Stuerzlinger and John Dill.

Visual Analytics on Large Displays: Exploring User Spatialization and How Size and Resolution Affect Task Performance

Abstract: Large, high-resolution displays (LHRDs) have been shown to enable increased productivity over conventional monitors. Previous work has identified the benefits of LHRDs for Visual Analytics tasks, where the user is analyzing complex data sets. However, LHRDs are fundamentally different from desktop and mobile computing environments, presenting some unique usability challenges and opportunities, and need to be better understood. There is thus a need for additional studies to analyze the impact of LHRD size and display resolution on content spatialization strategies and Visual Analytics task performance. We present the results of two studies of the effects of physical display size and resolution on analytical task successes and also analyze how participants spatially cluster visual content in different display conditions. Overall, we found that navigation technique preferences differ significantly among users, that the wide range of observed spatialization types suggest several different analysis techniques are adopted, and that display size affects clustering task performance whereas display resolution does not.

 

Visual Analytics: Applications

Raphael Sahann and Torsten Möller.

OCP – Operative Curricular Planning: A Visual Decision Support System for Planning Teaching Resources

Abstract: We conducted a design study to do an in-depth analysis of the problem of operative planning at universities and designed a decision support tool for that problem, called Operative Curricular Planning (OCP). Based on our observations we abstracted the planning process into separate tasks. Focusing on a subset of tasks that we characterized, we present the OCP tool for visually supporting decision making in the process of planning teaching resources. We show the steps leading to the final design of our visual decision support system and discuss the design decisions made while building the tool. Finally, we present an evaluation with four domain experts in a real-world scenario and talk about lessons learned from building the OCP tool, including the issue of integration and adoption of the system.

 

Md. Iqbal Hossain, Stephen Kobourov, Helen Purchase and Mihai Surdeanu.

REMatch: Research Expert Matching System

Abstract: We describe a system designed to process, analyze and visualize academic data, from research papers and research proposals to list of courses taught, consulting, internal and external service. This can be helpful in identifying experts in a given field for future collaborations, as well as in putting together strong multi-disciplinary teams to apply for future research funding. Our REMatch system aims to support such tasks by leveraging natural language processing, machine learning, and interactive visualization. Specifically, REMatch provides a functional system that implements in-the-browser, map-based interactive navigation of a large underlying network, supporting semantic zooming, panning, searching, and map overlays. A prototype of the system is evaluated with a small-scale case study.

 

Arnaud Prouzeau, Tim Dwyer, Manivannan Balasubramaniam, Joshua Henry, Dharshini.M.B Anu and Ngọc Hoàng.

Visual Analytics for Building Management

Abstract: Building management systems (BMS) provide monitoring and control of most of large-building assets (heating, ventilation, air conditioning, lighting, security systems, and so on). With the recent advancement of the Internet of Things and data management systems, BMS must gather and manage increasingly detailed data coming from a greater number and diversity of sources. The availability of such data should help building managers optimize the energy consumption of buildings. However, current BMS don’t allow efficient visualization of such data, which means that even if the data is available, it is not used to its full potential. In this paper, we describe a prototype BMS interface providing interactive visualizations of traditional building data (temperature, energy consumption), as well as more novel data (comfort feedback from occupants and live occupancy). We evaluate this prototype by first showing how it could be used to plan a long-term energy saving strategy, and then in a feedback session involving facility managers at a university.

 

Visual Analytics: Exploration

Daniel Steinböck, Eduard Gröller and Manuela Waldner.

Casual Visual Exploration of Large Bipartite Graphs Using Hierarchical Aggregation and Filtering

Abstract: Bipartite graphs are typically visualized using linked lists or matrices. However, these classic visualization techniques do not scale well with the number of nodes. Biclustering has been used to aggregate edges, but not to create linked lists with thousands of nodes. In this paper, we present a new casual exploration interface for large, weighted bipartite graphs, which allows for multi-scale exploration through hierarchical aggregation of nodes and edges using biclustering in linked lists. We demonstrate the usefulness of the technique using two data sets: a database of media advertising expenses of public authorities and author-keyword co-occurrences from the IEEE Visualization Publication collection. Through an insight-based study with lay users, we show that the biclustering interface leads to longer exploration times, more insights, and more unexpected findings than a baseline interface using only filtering. However, users also perceive the biclustering interface as more complex.

 

 

Mohammed Ali, Mark Jones, Xianghua Xie and Mark Williams.

Towards Visual Exploration of Large Temporal Datasets

Abstract: Visual analytics for time series data has received considerable attention in previous literature, and different approaches have been developed to understand the characteristics of the data and to obtain meaningful information. Visualizing, analyzing and presenting large temporal datasets are important tasks to understand, navigate and explore such data. Onedimensional time-series charts are usually used to visualize time series data but if the dataset contains multiple time series with a large number of observations a high degree of overlap will occur which may obscure important information. This problem has become a vital challenge in many domains such as finance, biological systems, and meteorology. The need for analyzing and exploring large time-series data led researchers to develop various interactive visualization tools and analytical algorithms which aim to give insight into the data, and most of them either focus on a small number of tasks or a specific domain. We propose a visual analytics system and approach which aims to visualize, analyze, present and explore large temporal datasets. Our approach consists of three main stages which are preprocessing, dimensionality reduction, and visual exploration. It assists with finding the interesting features in the data which are often obscured in the line chart or the visual compression that is required to render the large datasets on a small screen. Also, it helps to obtain an overview of the entire dataset and track changes over time. Moreover, it enables the user to detect clusters and outliers and observe the transitions between data. The juxtaposed views are used to visualize and interact both with raw time series data and projection data. Different time series datasets are deployed on our system, and we demonstrate the utility and evaluate the results using a case study with two different datasets which show the effectiveness of our system.

 

Visual Analytics: Pipelines

Michael Behrisch, Robert Krueger, Fritz Lekschas, Tobias Schreck, Nils Gehlenborg and Hanspeter Pfister.

Pattern-Driven Exploration of Big Data

Abstract: Abstract—Pattern extraction algorithms are enabling insights into the ever-growing amount of today’s datasets by translating reoccurring data properties into compact representations. Yet, a practical problem arises: With increasing data volumes and complexity also the number of patterns increases, leaving the analyst with a vast result space. Current algorithmic and especially visualization approaches oftentimes fail to answer central overview questions essential for a comprehensive understanding of pattern distributions and -support, their quality, and relevance to the analysis task. To address these challenges, we contribute a visual analytics pipeline targeted on the pattern-driven exploration of result spaces in a semi-automatic fashion. Specifically, we combine image feature analysis and unsupervised learning to partition the pattern space into interpretable, coherent chunks, which should be given priority in a subsequent in-depth analysis. In our analysis scenarios, no ground-truth is given. Thus, we employ and evaluate novel quality metrics derived from the distance distributions of our image feature vectors and the derived cluster model to guide the feature selection process. We visualize our results interactively, allowing the user to drill down from overview to detail into the pattern space and demonstrate our techniques in a case study on biomedical genomic data.

 

Maha El Meseery, Yuyao Wu and Wolfgang Stürzlinger.

Multiple Workspaces in Visual Analytics

Abstract: Exploratory visual analysis is an iterative process, where analysts often start from an overview of the data. Subsequently, they often pursue different hypotheses through multiple rounds of interaction and analysis. Commercial visualization packages support mostly a model with a single analysis path, where the system view represents only the final state of the users’ current analysis. In this paper, we investigate the benefit of using multiple workspaces to support alternative analyses, enabling users to create different workspaces to pursue multiple analysis paths at the same time. We implemented a prototype for multiple workspaces using a multi-tab design in a visual analytics system. The results of our user studies show that multiple workspaces: enable analysts to work on concurrent tasks, work well for organizing an analysis, and make it easy to revisit previous parts of their work.

 

Theoretical Foundations

Ulrich Engelke, Alfie Abdul-Rahman and Min Chen.

VISupply: A Supply Chain Framework for Visualization Guidelines

Abstract: Visualization is widely accepted as an effective medium to communicate complex data to a human observer. To do this effectively, visualizations have to be carefully designed to achieve a certain intent. Visualization guidelines are proposed by the academic research community and practitioners to facilitate effective visualization design. A few guidelines have been received a fair amount of attention, and effort has been made to study, discuss, validate, falsify, adopt, adapt, or extend them. However, many guidelines have not received adequate exposure or have not had the opportunities to undergone a similar level of scrutiny. When some of these guidelines managed to emerge or resurface, it is often not clear about their scientific rationale and the state of play in their validation. In this paper, we juxtapose the development and consumption of visualization guidelines with that of consumer products. We outline a conceptual model for a Visualization Guidelines Supply Chain, VISupply. It describes an idealized loop of actions for formulating, curating, using, and improving guidelines systematically. By enabling an ecosystem for visualization guidelines, the community can collectively optimize these guidelines and adopt them with confidence in a given context. We examine the current and potential roles of different stakeholders in this ecosystem.

Text Media Analysis

Mennatallah El-Assady, Fabian Sperrle, Rita Sevastjanova, Michael Sedlmair and Daniel Keim.

LTMA: Layered Topic Matching for the Comparative Exploration, Evaluation, and Refinement of Topic Modeling

Abstract: We present LTMA, a Layered Topic Matching approach for the unsupervised comparative analysis of topic modeling results. Due to the vast number of available modeling algorithms, an efficient and effective comparison of their results is detrimental to a data- and task-driven selection of a model. LTMA automates this comparative analysis by providing topic matching based on two layers (document-overlap and keyword-similarity), creating a novel topic-match data structure. This data structure builds a basis for model exploration and optimization, thus, allowing for an efficient evaluation of their performance in the context of a given type of text data and task. This is especially important for text types where an annotated gold standard dataset is not readily available and, therefore, quantitative evaluation methods are not applicable. We confirm the usefulness of our technique based on three use cases, namely: (1) the automatic comparative evaluation of topic models, (2) the visual exploration of topic modeling differences, and (3) the optimization of topic modeling results through combining matches.

 

Qi Han, Markus John, Steffen Koch, Ivan Assenov and Thomas Ertl.

LabelTransfer – Integrating Static and Dynamic Label Representation for Focus+Context Text Exploration

Abstract: In recent years, interactive visualization to analyze text documents has gained an impressive momentum. This is not surprising considering the fast increase of electronically available textual documents of various kinds. These include, for example, patents, scholarly documents, social media messages, and many other sources that contain valuable knowledge and insights for many stakeholders. Interactive text visualization turned out to be an important means for exploring and gaining insights into complex and often large document collections. An established visualization strategy to represent such collections are projection-based techniques that visualize documents as glyphs in a 2D representation aiming to reflect the semantic similarity of documents by the proximity of their placement. Static labels have been suggested to characterize the overall topics contained in the projected data to improve the effectiveness of such visualization techniques. Other approaches employ magic lenses that enable users to freely explore the 2D spatialization on various granularity levels. In this work, we propose a visual exploration approach that combines cluster-based labeling of projected documents with an interaction concept for magic lens-based techniques. We offer a set of novel interactive features to support a smooth transition between static labels and the magic lens approach while exploiting the different levels of visual abstraction of both techniques without introducing additional clutter through overdraw. Finally, we provide insights gained from a preliminary user study and present the benefits of our approach.

 

Geo-related Visual and Immersive Analytics

Alexandra Diehl, Michael Hundt, Johannes Häußler, Daniel Seebacher, Siming Chen, Nida Cilasun, Daniel Keim and Tobias Schreck.

SocialOcean: Visual Analysis and Characterization of Social Media Bubbles

Abstract: Social media allows citizens, corporations, and authorities to create, post, and exchange information. The study of its dynamics will enable analysts to understand user activities and social group characteristics such as connectedness, geospatial distribution, and temporal behavior. In this context, social media bubbles can be defined as social groups that exhibit certain biases in social media. These biases strongly depend on the dimensions selected in the analysis, for example, topic affinity, credibility, sentiment, and geographic distribution.

In this paper, we present SocialOcean, a visual analytics system that allows for the investigation of social media bubbles. There exists a large body of research in social sciences which identifies important dimensions of social media bubbles (SMBs). While such dimensions have been studied separately, and also some of them in combination, it is still an open question which dimensions play the most important role in defining SMBs. Since the concept of SMBs is fairly recent, there are many unknowns regarding their characterization. We investigate the thematic and spatio-temporal characteristics of SMBs and present a visual analytics system to address questions such as: What are the most important dimensions that characterize SMBs? and How SMBs embody in the presence of specific events that resonate with them?

We illustrate our approach using three different real scenarios related to the single event of Boston Marathon Bombing, and political news about Global Warming. We perform an expert evaluation, analyze the experts’ feedback, and present the lessons learned.

 

Andrew Cunningham, James Walsh and Bruce Thomas.

Immersive Visualisation of Geo-Temporal Narratives in Law Enforcement

Abstract: Recent advances in virtual reality technologies enable high-fidelity exploration of data in an immersive environment. This is potentially advantageous for professional applications of high-dimensional datasets (such as geo-temporal narratives), as we can leverage all three spatial axes while immersing the user in the information itself. Geo-temporal narratives tell a story of entities, their movements, and as a result, their potential relationships, thereby defining the who, what, where, and when that define a story; everything except the why. This paper describes an immersive virtual reality system we have developed to convey these narratives, specifically focusing on the law enforcement domain. The system lets users not only view who was where and when, but also view explicit and implicit relationships between entities, repeated visits to recurring locations, as well as the crucial descriptive information explaining the why. We present the results of an expert review of the system from federal law enforcement and defence agencies that validate our approach.

 

James Walsh, Andrew Cunningham, Ross Smith and Bruce Thomas.

Tangible Braille Plot: Tangibly Exploring Geo-Temporal Data in Virtual Reality

Abstract: Despite the resurgence of virtual reality (VR), the primary method of interacting with the environment is using generic controllers. Given the often-purpose-built nature of applications within VR, this is surprising, as despite the effort put into the design of the application itself, the same attention is not paid to the input control. This is despite the advantages that tangible interfaces have for user understanding, especially in the context of visualization, where user understanding is paramount. This paper presents the adaptation of a previous 2D temporal-geospatial visualization into VR, and more importantly, describes the development of a novel 8DOF TUI developed to support the exploration of that data. For our application, this centers around the exploration of geospatial data to explore colocation and divergence of entities, but could easily be extended to other domains. We present our novel controller as an example of the benefits of the utilization of purpose built physical controllers as a first-tier method of enabling immersive analytics. We describe the immersive system and controller, followed by an example use case and other applications encouraging further development of novel tangibles as a key component of immersive data analytics.