DevLan/vispubs huggingface dataset:
What Happened in my Home?: An End-User Development Approach for Smart Home Data Visualization
Authors: Nico Castelli, Corinna Ogonowski, Timo Jakobi, Martin Stein, Gunnar Stevens, Volker Wulf
Published at CHI, 2017

Smart home systems change the way we experience the home. While there are established research fields within HCI for visualizing specific use cases of a smart home, studies targeting user demands on visualizations spanning across multiple use cases are rare. Especially, individual data-related demands pose a challenge for usable visualizations. To investigate potentials of an end-user development (EUD) approach for flexibly supporting such demands, we developed a smart home system featuring both pre-defined visualizations and a visualization creation tool. To evaluate our concept, we installed our prototype in 12 households as part of a Living Lab study. Results are based on three interview studies, a design workshop and system log data. We identified eight overarching interests in home data and show how participants used pre-defined visualizations to get an overview and the creation tool to not only address specific use cases but also to answer questions by creating temporary visualizations.
Where No One Has Gone Before: A Meta-Dataset of the World's Largest Fanfiction Repository
Authors: Kodlee Yin, Cecilia R. Aragon, Sarah Evans, Katie Davis 0001
Published at CHI, 2017

With its roots dating to popular television shows of the 1960s such as Star Trek, fanfiction has blossomed into an extremely widespread form of creative expression. The transition from printed zines to online fanfiction repositories has facilitated this growth in popularity, with millions of fans writing stories and adding daily to sites such as Archive Of Our Own, Fanfiction.net, FIMfiction.net, and many others. Enthusiasts are sharing their writing, reading stories written by others, and helping each other to grow as writers. Yet, this domain is often undervalued by society and understudied by researchers. To facilitate the study of this large but often marginalized community, we present a fully anonymized data release (via differential privacy) of the metadata from a large fanfiction site (to protect author privacy, story, profile, and review text is excluded, and only metadata is provided). We use visual analytics techniques to draw several intriguing insights from the data and show the potential for future research. We hope other researchers can use this data to explore further questions related to online fanfiction communities.
A Grammar-based Approach for Modeling User Interactions and Generating Suggestions During the Data Exploration Process
Authors: Filip Dabek, Jesus J. Caban
Published at VAST, 2016

Despite the recent popularity of visual analytics focusing on big data, little is known about how to support users that use visualization techniques to explore multi-dimensional datasets and accomplish specific tasks. Our lack of models that can assist end-users during the data exploration process has made it challenging to learn from the user's interactive and analytical process. The ability to model how a user interacts with a specific visualization technique and what difficulties they face are paramount in supporting individuals with discovering new patterns within their complex datasets. This paper introduces the notion of visualization systems understanding and modeling user interactions with the intent of guiding a user through a task thereby enhancing visual data exploration. The challenges faced and the necessary future steps to take are discussed; and to provide a working example, a grammar-based model is presented that can learn from user interactions, determine the common patterns among a number of subjects using a K-Reversible algorithm, build a set of rules, and apply those rules in the form of suggestions to new users with the goal of guiding them along their visual analytic process. A formal evaluation study with 300 subjects was performed showing that our grammar-based model is effective at capturing the interactive process followed by users and that further research in this area has the potential to positively impact how users interact with a visualization system.
A Visual Analytics Approach for Categorical Joint Distribution Reconstruction from Marginal Projections
Authors: Cong Xie, Wen Zhong, Klaus Mueller 0001
Published at VAST, 2016

Oftentimes multivariate data are not available as sets of equally multivariate tuples, but only as sets of projections into subspaces spanned by subsets of these attributes. For example, one may find data with five attributes stored in six tables of two attributes each, instead of a single table of five attributes. This prohibits the visualization of these data with standard high-dimensional methods, such as parallel coordinates or MDS, and there is hence the need to reconstruct the full multivariate (joint) distribution from these marginal ones. Most of the existing methods designed for this purpose use an iterative procedure to estimate the joint distribution. With insufficient marginal distributions and domain knowledge, they lead to results whose joint errors can be large. Moreover, enforcing smoothness for regularizations in the joint space is not applicable if the attributes are not numerical but categorical. We propose a visual analytics approach that integrates both anecdotal data and human experts to iteratively narrow down a large set of plausible solutions. The solution space is populated using a Monte Carlo procedure which uniformly samples the solution space. A level-of-detail high dimensional visualization system helps the user understand the patterns and the uncertainties. Constraints that narrow the solution space can then be added by the user interactively during the iterative exploration, and eventually a subset of solutions with narrow uncertainty intervals emerges.
A Visual Analytics Approach for Understanding Reasons behind Snowballing and Comeback in MOBA Games
Authors: Quan Li, Peng Xu, Yeukyin Chan, Yun Wang 0012, Zhipeng Wang, Huamin Qu, Xiaojuan Ma
Published at VAST, 2016

To design a successful Multiplayer Online Battle Arena (MOBA) game, the ratio of snowballing and comeback occurrences to all matches played must be maintained at a certain level to ensure its fairness and engagement. Although it is easy to identify these two types of occurrences, game developers often find it difficult to determine their causes and triggers with so many game design choices and game parameters involved. In addition, the huge amounts of MOBA game data are often heterogeneous, multi-dimensional and highly dynamic in terms of space and time, which poses special challenges for analysts. In this paper, we present a visual analytics system to help game designers find key events and game parameters resulting in snowballing or comeback occurrences in MOBA game data. We follow a user-centered design process developing the system with game analysts and testing with real data of a trial version MOBA game from NetEase Inc. We apply novel visualization techniques in conjunction with well-established ones to depict the evolution of players' positions, status and the occurrences of events. Our system can reveal players' strategies and performance throughout a single match and suggest patterns, e.g., specific player' actions and game events, that have led to the final occurrences. We further demonstrate a workflow of leveraging human analyzed patterns to improve the scalability and generality of match data analysis. Finally, we validate the usability of our system by proving the identified patterns are representative in snowballing or comeback matches in a one-month-long MOBA tournament dataset.
An Analysis of Machine- and Human-Analytics in Classification
Authors: Gary K. L. Tam, Vivek Kothari, Min Chen 0001
Published at VAST, 2016

In this work, we present a study that traces the technical and cognitive processes in two visual analytics applications to a common theoretic model of soft knowledge that may be added into a visual analytics process for constructing a decision-tree model. Both case studies involved the development of classification models based on the “bag of features” approach. Both compared a visual analytics approach using parallel coordinates with a machine-learning approach using information theory. Both found that the visual analytics approach had some advantages over the machine learning approach, especially when sparse datasets were used as the ground truth. We examine various possible factors that may have contributed to such advantages, and collect empirical evidence for supporting the observation and reasoning of these factors. We propose an information-theoretic model as a common theoretic basis to explain the phenomena exhibited in these two case studies. Together we provide interconnected empirical and theoretical evidence to support the usefulness of visual analytics.
AnaFe: Visual Analytics of Image-derived Temporal Features Focusing on the Spleen
Authors: Ievgeniia Gutenko, Konstantin Dmitriev, Arie E. Kaufman, Matthew A. Barish
Published at VAST, 2016

We present a novel visualization framework, AnaFe, targeted at observing changes in the spleen over time through multiple image-derived features. Accurate monitoring of progressive changes is crucial for diseases that result in enlargement of the organ. Our system is comprised of multiple linked views combining visualization of temporal 3D organ data, related measurements, and features. Thus it enables the observation of progression and allows for simultaneous comparison within and between the subjects. AnaFe offers insights into the overall distribution of robustly extracted and reproducible quantitative imaging features and their changes within the population, and also enables detailed analysis of individual cases. It performs similarity comparison of temporal series of one subject to all other series in both sick and healthy groups. We demonstrate our system through two use case scenarios on a population of 189 spleen datasets from 68 subjects with various conditions observed over time.
Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data Based on User-Authored Annotations
Authors: Jian Zhao 0010, Michael Glueck, Simon Breslav, Fanny Chevalier, Azam Khan
Published at VAST, 2016

User-authored annotations of data can support analysts in the activity of hypothesis generation and sensemaking, where it is not only critical to document key observations, but also to communicate insights between analysts. We present annotation graphs, a dynamic graph visualization that enables meta-analysis of data based on user-authored annotations. The annotation graph topology encodes annotation semantics, which describe the content of and relations between data selections, comments, and tags. We present a mixed-initiative approach to graph layout that integrates an analyst's manual manipulations with an automatic method based on similarity inferred from the annotation semantics. Various visual graph layout styles reveal different perspectives on the annotation semantics. Annotation graphs are implemented within C8, a system that supports authoring annotations during exploratory analysis of a dataset. We apply principles of Exploratory Sequential Data Analysis (ESDA) in designing C8, and further link these to an existing task typology in the visualization literature. We develop and evaluate the system through an iterative user-centered design process with three experts, situated in the domain of analyzing HCI experiment data. The results suggest that annotation graphs are effective as a method of visually extending user-authored annotations to data meta-analysis for discovery and organization of ideas.
AxiSketcher: Interactive Nonlinear Axis Mapping of Visualizations through User Drawings
Authors: Bum Chul Kwon, Hannah Kim, Emily Wall, Jaegul Choo, Haesun Park, Alex Endert
Published at VAST, 2016

Visual analytics techniques help users explore high-dimensional data. However, it is often challenging for users to express their domain knowledge in order to steer the underlying data model, especially when they have little attribute-level knowledge. Furthermore, users' complex, high-level domain knowledge, compared to low-level attributes, posits even greater challenges. To overcome these challenges, we introduce a technique to interpret a user's drawings with an interactive, nonlinear axis mapping approach called AxiSketcher. This technique enables users to impose their domain knowledge on a visualization by allowing interaction with data entries rather than with data attributes. The proposed interaction is performed through directly sketching lines over the visualization. Using this technique, users can draw lines over selected data points, and the system forms the axes that represent a nonlinear, weighted combination of multidimensional attributes. In this paper, we describe our techniques in three areas: 1) the design space of sketching methods for eliciting users' nonlinear domain knowledge; 2) the underlying model that translates users' input, extracts patterns behind the selected data points, and results in nonlinear axes reflecting users' complex intent; and 3) the interactive visualization for viewing, assessing, and reconstructing the newly formed, nonlinear axes.
Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation
Authors: Xinsong Yang, Lei Shi 0002, Madelaine Daianu, Hanghang Tong, Qingsong Liu, Paul M. Thompson
Published at VAST, 2016

Visually comparing human brain networks from multiple population groups serves as an important task in the field of brain connectomics. The commonly used brain network representation, consisting of nodes and edges, may not be able to reveal the most compelling network differences when the reconstructed networks are dense and homogeneous. In this paper, we leveraged the block information on the Region Of Interest (ROI) based brain networks and studied the problem of blockwise brain network visual comparison. An integrated visual analytics framework was proposed. In the first stage, a two-level ROI block hierarchy was detected by optimizing the anatomical structure and the predictive comparison performance simultaneously. In the second stage, the NodeTrix representation was adopted and customized to visualize the brain network with block information. We conducted controlled user experiments and case studies to evaluate our proposed solution. Results indicated that our visual analytics method outperformed the commonly used node-link graph and adjacency matrix design in the blockwise network comparison tasks. We have shown compelling findings from two real-world brain network data sets, which are consistent with the prior connectomics studies.
C2A: Crowd consensus analytics for virtual colonoscopy
Authors: Ji Hwan Park, Saad Nadeem, Seyedkoosha Mirhosseini, Arie E. Kaufman
Published at VAST, 2016

We present a medical crowdsourcing visual analytics platform called C2A to visualize, classify and filter crowdsourced clinical data. More specifically, C2A is used to build consensus on a clinical diagnosis by visualizing crowd responses and filtering out anomalous activity. Crowdsourcing medical applications have recently shown promise where the non-expert users (the crowd) were able to achieve accuracy similar to the medical experts. This has the potential to reduce interpretation/reading time and possibly improve accuracy by building a consensus on the findings beforehand and letting the medical experts make the final diagnosis. In this paper, we focus on a virtual colonoscopy (VC) application with the clinical technicians as our target users, and the radiologists acting as consultants and classifying segments as benign or malignant. In particular, C2A is used to analyze and explore crowd responses on video segments, created from fly-throughs in the virtual colon. C2A provides several interactive visualization components to build crowd consensus on video segments, to detect anomalies in the crowd data and in the VC video segments, and finally, to improve the non-expert user's work quality and performance by A/B testing for the optimal crowdsourcing platform and application-specific parameters. Case studies and domain experts feedback demonstrate the effectiveness of our framework in improving workers' output quality, the potential to reduce the radiologists' interpretation time, and hence, the potential to improve the traditional clinical workflow by marking the majority of the video segments as benign based on the crowd consensus.
Characterizing Guidance in Visual Analytics
Authors: Davide Ceneda, Theresia Gschwandtner, Thorsten May, Silvia Miksch, Hans-Jörg Schulz, Marc Streit, Christian Tominski
Published at VAST, 2016

Visual analytics (VA) is typically applied in scenarios where complex data has to be analyzed. Unfortunately, there is a natural correlation between the complexity of the data and the complexity of the tools to study them. An adverse effect of complicated tools is that analytical goals are more difficult to reach. Therefore, it makes sense to consider methods that guide or assist users in the visual analysis process. Several such methods already exist in the literature, yet we are lacking a general model that facilitates in-depth reasoning about guidance. We establish such a model by extending van Wijk's model of visualization with the fundamental components of guidance. Guidance is defined as a process that gradually narrows the gap that hinders effective continuation of the data analysis. We describe diverse inputs based on which guidance can be generated and discuss different degrees of guidance and means to incorporate guidance into VA tools. We use existing guidance approaches from the literature to illustrate the various aspects of our model. As a conclusion, we identify research challenges and suggest directions for future studies. With our work we take a necessary step to pave the way to a systematic development of guidance techniques that effectively support users in the context of VA.
D-Map: Visual Analysis of Ego-centric Information Diffusion Patterns in Social Media
Authors: Siming Chen 0001, Shuai Chen 0001, Zhenhuang Wang, Christy Jie Liang, Xiaoru Yuan, Nan Cao, Yadong Wu
Published at VAST, 2016

Popular social media platforms could rapidly propagate vital information over social networks among a significant number of people. In this work we present D-Map (Diffusion Map), a novel visualization method to support exploration and analysis of social behaviors during such information diffusion and propagation on typical social media through a map metaphor. In D-Map, users who participated in reposting (i.e., resending a message initially posted by others) one central user's posts (i.e., a series of original tweets) are collected and mapped to a hexagonal grid based on their behavior similarities and in chronological order of the repostings. With additional interaction and linking, D-Map is capable of providing visual portraits of the influential users and describing their social behaviors. A comprehensive visual analysis system is developed to support interactive exploration with D-Map. We evaluate our work with real world social media data and find interesting patterns among users. Key players, important information diffusion paths, and interactions among social communities can be identified.
Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis
Authors: Cagatay Turkay, Erdem Kaya, Selim Balcisoy, Helwig Hauser
Published at VAST, 2016

In interactive data analysis processes, the dialogue between the human and the computer is the enabling mechanism that can lead to actionable observations about the phenomena being investigated. It is of paramount importance that this dialogue is not interrupted by slow computational mechanisms that do not consider any known temporal human-computer interaction characteristics that prioritize the perceptual and cognitive capabilities of the users. In cases where the analysis involves an integrated computational method, for instance to reduce the dimensionality of the data or to perform clustering, such non-optimal processes are often likely. To remedy this, progressive computations, where results are iteratively improved, are getting increasing interest in visual analytics. In this paper, we present techniques and design considerations to incorporate progressive methods within interactive analysis processes that involve high-dimensional data. We define methodologies to facilitate processes that adhere to the perceptual characteristics of users and describe how online algorithms can be incorporated within these. A set of design recommendations and according methods to support analysts in accomplishing high-dimensional data analysis tasks are then presented. Our arguments and decisions here are informed by observations gathered over a series of analysis sessions with analysts from finance. We document observations and recommendations from this study and present evidence on how our approach contribute to the efficiency and productivity of interactive visual analysis sessions involving high-dimensional data.
DimScanner: A Relation-based Visual Exploration Approach Towards Data Dimension Inspection
Authors: Jing Xia, Wei Chen 0001, Yumeng Hou, Wanqi Hu, Xinxin Huang, David S. Ebert
Published at VAST, 2016

Exploring multi-dimensional datasets can be cumbersome if data analysts have little knowledge about the data. Various dimension relation inspection tools and dimension exploration tools have been proposed for efficient data examining and understanding. However, the needed workload varies largely with respect to data complexity and user expertise, which can only be reduced with rich background knowledge over the data. In this paper we address the workload challenge with a data structuring and exploration scheme that affords dimension relation detection and that serves as the background knowledge for further investigation. We contribute a novel data structuring scheme that leverages an information-theoretic view structuring algorithm to uncover information-aware relations among different data views, and thereby discloses redundancy and other relation patterns among dimensions. The integrated system, DimScanner, empowers analysts with rich user controls and assistance widgets to interactively detect the relations of multi-dimensional data.