Supporting the Integrated Visual Analysis of Input Parameters and Simulation Trajectories

The visualization of simulation trajectories is a well-established approach to analyze simulated processes. Likewise, the visualization of the parameter space that configures a simulation is a well-known method to get an overview of possible parameter combinations. This paper follows the premise that both of these approaches are actually two sides of the same coin: Since the input parameters influence the simulation outcome, it is desirable to visualize and explore both in a combined manner. The main challenge posed by such an integrated visualization is the combinatorial explosion of possible parameter combinations. It leads to insurmountably high simulation runtimes and screen space requirements for their visualization. The Visual Analytics approach presented in this paper targets this issue by providing a visualization of a coarsely sampled subspace of the parameter space and its corresponding simulation outcome. In this visual representation, the analyst can identify regions for further drill-down and thus finer subsampling. We aid this identification by providing visual cues based on heterogeneity metrics. These indicate in which regions of the parameter space deviating behavior occurs at a more fine-grained scale and thus warrants further investigation and possible re-computation. We demonstrate our approach in the domain of systems biology by a visual analysis of a rule-based model of the canonical Wnt signaling pathway that plays a major role in embryonic development. In this case, the aim of the domain experts was to systematically explore the parameter space to determine those parameter configurations that match experimental data sufficiently well

Grounded Visual Analytics: A New Approach to Discovering Phenomena in Data at Scale

We introduce Grounded Visual Analytics, a new method that integrates qualitative and quantitative approaches in order to help investigators discover patterns about human activity. Investigators who develop or study systems often use log data, which keeps track of interactions their participants perform. Discovering and characterizing patterns in this data is important because it can help guide interactive computing system design. This new approach integrates Visual Analytics, a field that investigates Information Visualization and interactive machine learning, and Grounded Theory, a rigorous qualitative research method for discovering nuanced understanding of qualitative data. This dissertation defines and motivates this new approach, reviews relevant existing tools, builds the Log Timelines system. We present and analyze six case studies that use Log Timelines, a probe that we created in order explore Grounded Visual Analytics. In a series of case studies, we collaborate with a participant-investigator on their own project and data. Their use of Grounded Visual Analytics generates ideas about how future research can bridge the gap between qualitative and quantitative methods

Dataremix: Aesthetic Experiences of Big Data and Data Abstraction

This PhD by published work expands on the contribution to knowledge in two recent large-scale transdisciplinary artistic research projects: ATLAS in silico and INSTRUMENT | One Antarctic Night and their exhibited and published outputs. The thesis reflects upon this practice-based artistic research that interrogates data abstraction: the digitization, datafication and abstraction of culture and nature, as vast and abstract digital data. The research is situated in digital arts practices that engage a combination of big (scientific) data as artistic material, embodied interaction in virtual environments, and poetic recombination. A transdisciplinary and collaborative artistic practice, x-resonance, provides a framework for the hybrid processes, outcomes, and contributions to knowledge from the research. These are purposefully and productively situated at the objective | subjective interface, have potential to convey multiple meanings simultaneously to a variety of audiences and resist disciplinary definition. In the course of the research, a novel methodology emerges, dataremix, which is employed and iteratively evolved through artistic practice to address the research questions: 1) How can a visceral and poetic experience of data abstraction be created? and 2) How would one go about generating an artistically-informed (scientific) discovery? Several interconnected contributions to knowledge arise through the first research question: creation of representational elements for artistic visualization of big (scientific) data that includes four new forms (genomic calligraphy, algorithmic objects as natural specimens, scalable auditory data signatures, and signal objects); an aesthetic of slowness that contributes an extension to the operative forces in Jevbratt’s inverted sublime of looking down and in to also include looking fast and slow; an extension of Corby’s objective and subjective image consisting of “informational and aesthetic components” to novel virtual environments created from big 3 (scientific) data that extend Davies’ poetic virtual spatiality to poetic objective | subjective generative virtual spaces; and an extension of Seaman’s embodied interactive recombinant poetics through embodied interaction in virtual environments as a recapitulation of scientific (objective) and algorithmic processes through aesthetic (subjective) physical gestures. These contributions holistically combine in the artworks ATLAS in silico and INSTRUMENT | One Antarctic Night to create visceral poetic experiences of big data abstraction. Contributions to knowledge from the first research question develop artworks that are visceral and poetic experiences of data abstraction, and which manifest the objective | subjective through art. Contributions to knowledge from the second research question occur through the process of the artworks functioning as experimental systems in which experiments using analytical tools from the scientific domain are enacted within the process of creation of the artwork. The results are “returned” into the artwork. These contributions are: elucidating differences in DNA helix bending and curvature along regions of gene sequences specified as either introns or exons, revealing nuanced differences in BLAST results in relation to genomics sequence metadata, and cross-correlation of astronomical data to identify putative variable signals from astronomical objects for further scientific evaluation

Evaluating human-centered approaches for geovisualization

Working with two small group of domain experts I evaluate human-centered approaches to application development which are applicable to geovisualization, following an ISO13407 taxonomy that covers context of use, eliciting requirements, and design. These approaches include field studies and contextual analysis of subjects' context; establishing requirements using a template, via a lecture to communicate geovisualization to subjects and by communicating subjects' context to geovisualization experts with a scenario; autoethnography to understand the geovisualization design process; wireframe, paper and digital interactive prototyping with alternative protocols; and a decision making process for prioritising application improvement. I find that the acquisition and use of real user data is key; that a template approach and teaching subjects about visualization tools and interactions both fail to elicit useful requirements for a visualization application. Consulting geovisualization experts with a scenario of user context and samples of user data does yield suggestions for tools and interactions of use to a visualization designer. The complex and composite natures of both visualization and human-centered domains, incorporating learning from both domains, with user context, makes design challenging. Wireframe, paper and digital interactive prototypes mediate between the user and visualization domains successfully, eliciting exploratory behaviour and suggestions to improve prototypes. Paper prototypes are particularly successful at eliciting suggestions and especially novel visualization improvements. Decision-making techniques prove useful for prioritising different possible improvements, although domain subjects select data-related features over more novel alternative and rank these more inconsistently. The research concludes that understanding subject context of use and data is important and occurs throughout the process of engagement with domain experts, and that standard requirements elicitation techniques are unsuccessful for geovisualization. Engagement with subjects at an early stage with simple prototypes incorporating real subject data and moving to successively more complex prototypes holds the best promise for creating successful geovisualization applications

Sustainable Tourism Empowered by Social Network Analysis to Gain a Competitive Edge at a Historic Site.

Social media has had a strong presence in many people’s lives over the last decade. In addition, social media platforms have allowed people to share opinions, provide advice on numerous factors, including where to visit, as well as to stay connected and maintain friendships. The hospitality and tourism industry, however, can make effective use of these powerful tools for marketing purposes, collaboration and information sharing, and service offerings. Reviewing social media followers’ behaviors and interests offers a wealth of information and valuable data for a variety of tourism organizations. This case study focuses on an analysis of the social networks applied to the fortified town of Fredrikstad in Norway. The data used in this research study were collected from the Facebook site of the tourist authority. The results of this research project demonstrate the strengths of applying a social network analysis to a dataset, which can aid in the strategic direction of a tourism destination. The conversations of the greatest interest can successfully be identified as well as the growth of the online network. This paper adds knowledge to the literature through the application of a social network analysis regarding the success of a tourism destination and its future potential.publishedVersio

Visual Analysis of Large, Time-Dependent, Multi-Dimensional Smart Sensor Tracking Data

Technological advancements over the past decade have increased our ability to collect data to previously unimaginable volumes [Kei02]. Understanding temporal patterns is key to gaining knowledge and insight. However, our capacity to store data now far exceeds the rate at which we are able to understand it [KKEM10]. This phenomenon has led to a growing need for advanced solutions to make sense and use of an ever-increasing data space. Abstract temporal data provides additional challenges in its, representation, size, and scalability, high dimensionality, and unique structure.One instance of such temporal data is acquired from smart sensor tags attached to freely roaming animals recording multiple parameters at infra-second rates which are becoming commonplace, and are transforming biologists understanding of the way wild animals behave.The excitement at the potential inherent in sophisticated tracking devices has, however, been limited by a lack of available software to advance research in the field. This thesis introduces methodologies to deal with the problem of the analysis of the large, multi-dimensional, time-dependent data acquired. Interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information.We present several contributions to the field of time-series visualisation, that is, the visualisation of ordered collections of real value data attributes at successive points in time sampled at uniform time intervals. Traditionally, time-series graphs have been used for temporal data. However, screen resolution is small in comparison to the large information space commonplace today. In such cases, we can only render a proportion of the data.It is widely accepted that the effective interpretation of large temporal data sets requires advanced methods and interaction techniques. In this thesis, we address these issues to enhance the exploration, analysis, and presentation of time-series data for movement ecologists in their smart sensor data analysis

Visual analytics for relationships in scientific data

Domain scientists hope to address grand scientific challenges by exploring the abundance of data generated and made available through modern high-throughput techniques. Typical scientific investigations can make use of novel visualization tools that enable dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters. These general tools should be applicable to many disciplines: allowing biologists to develop an intuitive understanding of the structure of coexpression networks and discover genes that reside in critical positions of biological pathways, intelligence analysts to decompose social networks, and climate scientists to model extrapolate future climate conditions. By using a graph as a universal data representation of correlation, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool integrates techniques such as graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized B-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using real-world workflows from several large-scale studies. Parallel coordinates has proven to be a scalable visualization and navigation framework for multivariate data. However, when data with thousands of variables are at hand, we do not have a comprehensive solution to select the right set of variables and order them to uncover important or potentially insightful patterns. We present algorithms to rank axes based upon the importance of bivariate relationships among the variables and showcase the efficacy of the proposed system by demonstrating autonomous detection of patterns in a modern large-scale dataset of time-varying climate simulation

Multimodal Biomedical Data Visualization: Enhancing Network, Clinical, and Image Data Depiction

In this dissertation, we present visual analytics tools for several biomedical applications. Our research spans three types of biomedical data: reaction networks, longitudinal multidimensional clinical data, and biomedical images. For each data type, we present intuitive visual representations and efficient data exploration methods to facilitate visual knowledge discovery. Rule-based simulation has been used for studying complex protein interactions. In a rule-based model, the relationships of interacting proteins can be represented as a network. Nevertheless, understanding and validating the intended behaviors in large network models are ineffective and error prone. We have developed a tool that first shows a network overview with concise visual representations and then shows relevant rule-specific details on demand. This strategy significantly improves visualization comprehensibility and disentangles the complex protein-protein relationships by showing them selectively alongside the global context of the network. Next, we present a tool for analyzing longitudinal multidimensional clinical datasets, that we developed for understanding Parkinson's disease progression. Detecting patterns involving multiple time-varying variables is especially challenging for clinical data. Conventional computational techniques, such as cluster analysis and dimension reduction, do not always generate interpretable, actionable results. Using our tool, users can select and compare patient subgroups by filtering patients with multiple symptoms simultaneously and interactively. Unlike conventional visualizations that use local features, many targets in biomedical images are characterized by high-level features. We present our research characterizing such high-level features through multiscale texture segmentation and deep-learning strategies. First, we present an efficient hierarchical texture segmentation approach that scales up well to gigapixel images to colorize electron microscopy (EM) images. This enhances visual comprehensibility of gigapixel EM images across a wide range of scales. Second, we use convolutional neural networks (CNNs) to automatically derive high-level features that distinguish cell states in live-cell imagery and voxel types in 3D EM volumes. In addition, we present a CNN-based 3D segmentation method for biomedical volume datasets with limited training samples. We use factorized convolutions and feature-level augmentations to improve model generalization and avoid overfitting