DevLan/vispubs huggingface dataset:
The Effect of Proximity in Social Data Charts on Perceived Unity
Authors: Marlen Promann, Sabine Brunswicker
Published at VAST, 2018

Social data charts - visual presentations of quantitative data about peer behaviors - may offer new means to motivate individuals to participate in group goals. However, to do so these charts need to create a semantic response of `unity' among the chart viewers in order to overcome the problems of social loafing where people act selfishly and undervalue the group's goal. In this paper, we focus on two properties of social data charts that may affect a viewer's perceptions of unity: (1) The skewness in the data structure - the statistical distribution of the social data, and (2) the proximity in the visual structure of the chart - the spatial organization of the data points. We performed a controlled perceptual experiment to examine the effect of proximity and skewness on four different semantic facets of perceived group unity: similarity, entitativity, rapport, and centrality. We exposed 179 participants on Amazon Mechanical Turk to different group charts using a 2 x 2 factorial design, varying both proximity and skewness. Our two-way ANCOVA analyses reveal three important findings: (1) Across all conditions, proximity has a strong positive effect on perceived group unity and conveys a social meaning of group entitativity, as well as, member similarity and rapport; (2) Skewness and proximity interact in a non-linear way, suggesting that skewness creates a negative force that modifies the semantic responses to high proximity; and (3) The perceptual responses to proximity have different semantic facets that are either stable or sensitive to skewness. These findings contribute to perceptual as well as social InfoVis literature.
The Effect of Semantic Interaction on Foraging in Text Analysis
Authors: John E. Wenskovitch, Lauren Bradel, Michelle Dowling, Leanna House, Chris North 0001
Published at VAST, 2018

Completing text analysis tasks is a continuous sensemaking loop of foraging for information and incrementally synthesizing it into hypotheses. Past research has shown the advantages of using spatial workspaces as a means for synthesizing information through externalizing hypotheses and creating spatial schemas. However, spatializing the entirety of datasets becomes prohibitive as the number of documents available to the analysts grows, particularly when only a small subset are relevant to the task at hand. StarSPIRE is a visual analytics tool designed to explore collections of documents, leveraging users' semantic interactions to steer (1) a synthesis model that aids in document layout, and (2) a foraging model to automatically retrieve new relevant information. In contrast to traditional keyword search foraging (KSF), “semantic interaction foraging” (SIF) occurs as a result of the user's synthesis actions. To quantify the value of semantic interaction foraging, we use StarSPIRE to evaluate its utility for an intelligence analysis sensemaking task. Semantic interaction foraging accounted for 26% of useful documents found, and it also resulted in increased synthesis interactions and improved sensemaking task performance by users in comparison to only using keyword search.
TPFlow: Progressive Partition and Multidimensional Pattern Extraction for Large-Scale Spatio-Temporal Data Analysis
Authors: Dongyu Liu, Panpan Xu, Ren Liu
Published at VAST, 2018

Consider a multi-dimensional spatio-temporal (ST) dataset where each entry is a numerical measure defined by the corresponding temporal, spatial and other domain-specific dimensions. A typical approach to explore such data utilizes interactive visualizations with multiple coordinated views. Each view displays the aggregated measures along one or two dimensions. By brushing on the views, analysts can obtain detailed information. However, this approach often cannot provide sufficient guidance for analysts to identify patterns hidden within subsets of data. Without a priori hypotheses, analysts need to manually select and iterate through different slices to search for patterns, which can be a tedious and lengthy process. In this work, we model multidimensional ST data as tensors and propose a novel piecewise rank-one tensor decomposition algorithm which supports automatically slicing the data into homogeneous partitions and extracting the latent patterns in each partition for comparison and visual summarization. The algorithm optimizes a quantitative measure about how faithfully the extracted patterns visually represent the original data. Based on the algorithm we further propose a visual analytics framework that supports a top-down, progressive partitioning workflow for level-of-detail multidimensional ST data exploration. We demonstrate the general applicability and effectiveness of our technique on three datasets from different application domains: regional sales trend analysis, customer traffic analysis in department stores, and taxi trip analysis with origin-destination (OD) data. We further interview domain experts to verify the usability of the prototype.
VIBR: Visualizing Bipartite Relations at Scale with the Minimum Description Length Principle
Authors: Gromit Yeuk-Yin Chan, Panpan Xu, Zeng Dai, Ren Liu
Published at VAST, 2018

Bipartite graphs model the key relations in many large scale real-world data: customers purchasing items, legislators voting for bills, people's affiliation with different social groups, faults occurring in vehicles, etc. However, it is challenging to visualize large scale bipartite graphs with tens of thousands or even more nodes or edges. In this paper, we propose a novel visual summarization technique for bipartite graphs based on the minimum description length (MDL) principle. The method simultaneously groups the two different set of nodes and constructs aggregated bipartite relations with balanced granularity and precision. It addresses the key trade-off that often occurs for visualizing large scale and noisy data: acquiring a clear and uncluttered overview while maximizing the information content in it. We formulate the visual summarization task as a co-clustering problem and propose an efficient algorithm based on locality sensitive hashing (LSH) that can easily scale to large graphs under reasonable interactive time constraints that previous related methods cannot satisfy. The method leads to the opportunity of introducing a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. In the framework, we also introduce a compact visual design inspired by adjacency list representation of graphs as the building block for a small multiples display to compare the bipartite relations for different subsets of data. We showcase the applicability and effectiveness of our approach by applying it on synthetic data with ground truth and performing case studies on real-world datasets from two application domains including roll-call vote record analysis and vehicle fault pattern analysis. Interviews with experts in the political science community and the automotive industry further highlight the benefits of our approach.
VIS Author Profiles: Interactive Descriptions of Publication Records Combining Text and Visualization
Authors: Shahid Latif, Fabian Beck 0001
Published at VAST, 2018

Publication records and collaboration networks are important for assessing the expertise and experience of researchers. Existing digital libraries show the raw publication lists in author profiles, whereas visualization techniques focus on specific subproblems. Instead, we look at publication records from various perspectives mixing low-level publication data with high-level abstractions and background information. This work presents VIS Author Profiles, a novel approach to generate integrated textual and visual descriptions to highlight patterns in publication records. We leverage template-based natural language generation to summarize notable publication statistics, evolution of research topics, and collaboration relationships. Seamlessly integrated visualizations augment the textual description and are interactively connected with each other and the text. The underlying publication data and detailed explanations of the analysis are available on demand. We compare our approach to existing systems by taking into account information needs of users and demonstrate its usefulness in two realistic application examples.
VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning
Authors: Dominik Sacha, Matthias Kraus, Daniel A. Keim, Min Chen 0001
Published at VAST, 2018

While many VA workflows make use of machine-learned models to support analytical tasks, VA workflows have become increasingly important in understanding and improving Machine Learning (ML) processes. In this paper, we propose an ontology (VIS4ML) for a subarea of VA, namely “VA-assisted ML”. The purpose of VIS4ML is to describe and understand existing VA workflows used in ML as well as to detect gaps in ML processes and the potential of introducing advanced VA techniques to such processes. Ontologies have been widely used to map out the scope of a topic in biology, medicine, and many other disciplines. We adopt the scholarly methodologies for constructing VIS4ML, including the specification, conceptualization, formalization, implementation, and validation of ontologies. In particular, we reinterpret the traditional VA pipeline to encompass model-development workflows. We introduce necessary definitions, rules, syntaxes, and visual notations for formulating VIS4ML and make use of semantic web technologies for implementing it in the Web Ontology Language (OWL). VIS4ML captures the high-level knowledge about previous workflows where VA is used to assist in ML. It is consistent with the established VA concepts and will continue to evolve along with the future developments in VA and ML. While this ontology is an effort for building the theoretical foundation of VA, it can be used by practitioners in real-world applications to optimize model-development workflows by systematically examining the potential benefits that can be brought about by either machine or human capabilities. Meanwhile, VIS4ML is intended to be extensible and will continue to be updated to reflect future advancements in using VA for building high-quality data-analytical models or for building such models rapidly.
Visual Abstraction of Large Scale Geospatial Origin-Destination Movement Data
Authors: Zhiguang Zhou, Linhao Meng, Cheng Tang, Ying Zhao 0001, Zhiyong Guo, Miaoxin Hu, Wei Chen 0001
Published at VAST, 2018

A variety of human movement datasets are represented in an Origin-Destination(OD) form, such as taxi trips, mobile phone locations, etc. As a commonly-used method to visualize OD data, flow map always fails to discover patterns of human mobility, due to massive intersections and occlusions of lines on a 2D geographical map. A large number of techniques have been proposed to reduce visual clutter of flow maps, such as filtering, clustering and edge bundling, but the correlations of OD flows are often neglected, which makes the simplified OD flow map present little semantic information. In this paper, a characterization of OD flows is established based on an analogy between OD flows and natural language processing (NPL) terms. Then, an iterative multi-objective sampling scheme is designed to select OD flows in a vectorized representation space. To enhance the readability of sampled OD flows, a set of meaningful visual encodings are designed to present the interactions of OD flows. We design and implement a visual exploration system that supports visual inspection and quantitative evaluation from a variety of perspectives. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system in reducing the visual clutter and enhancing correlations of OD flows.
Visual Analysis of the Temporal Evolution of Ensemble Forecast Sensitivities
Authors: Alexander Kumpf, Marc Rautenhaus, Michael Riemer, Rüdiger Westermann
Published at VAST, 2018

Ensemble sensitivity analysis (ESA) has been established in the atmospheric sciences as a correlation-based approach to determine the sensitivity of a scalar forecast quantity computed by a numerical weather prediction model to changes in another model variable at a different model state. Its applications include determining the origin of forecast errors and placing targeted observations to improve future forecasts. We - a team of visualization scientists and meteorologists - present a visual analysis framework to improve upon current practice of ESA. We support the user in selecting regions to compute a meaningful target forecast quantity by embedding correlation-based grid-point clustering to obtain statistically coherent regions. The evolution of sensitivity features computed via ESA are then traced through time, by integrating a quantitative measure of feature matching into optical-flow-based feature assignment, and displayed by means of a swipe-path showing the geo-spatial evolution of the sensitivities. Visualization of the internal correlation structure of computed features guides the user towards those features robustly predicting a certain weather event. We demonstrate the use of our method by application to real-world 2D and 3D cases that occurred during the 2016 NAWDEX field campaign, showing the interactive generation of hypothesis chains to explore how atmospheric processes sensitive to each other are interrelated.
Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution
Authors: Mennatallah El-Assady, Fabian Sperrle, Oliver Deussen, Daniel A. Keim, Christopher Collins 0001
Published at VAST, 2018

To effectively assess the potential consequences of human interventions in model-driven analytics systems, we establish the concept of speculative execution as a visual analytics paradigm for creating user-steerable preview mechanisms. This paper presents an explainable, mixed-initiative topic modeling framework that integrates speculative execution into the algorithmic decision-making process. Our approach visualizes the model-space of our novel incremental hierarchical topic modeling algorithm, unveiling its inner-workings. We support the active incorporation of the user's domain knowledge in every step through explicit model manipulation interactions. In addition, users can initialize the model with expected topic seeds, the backbone priors. For a more targeted optimization, the modeling process automatically triggers a speculative execution of various optimization strategies, and requests feedback whenever the measured model quality deteriorates. Users compare the proposed optimizations to the current model state and preview their effect on the next model iterations, before applying one of them. This supervised human-in-the-Ioop process targets maximum improvement for minimum feedback and has proven to be effective in three independent studies that confirm topic model quality improvements.
Visual Progression Analysis of Event Sequence Data
Authors: Shunan Guo, Zhuochen Jin, David Gotz, Fan Du, Hongyuan Zha, Nan Cao
Published at VAST, 2018

Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.
Vulnus: Visual Vulnerability Analysis for Network Security
Authors: Marco Angelini, Graziano Blasilli, Tiziana Catarci, Simone Lenti, Giuseppe Santucci
Published at VAST, 2018

Vulnerabilities represent one of the main weaknesses of IT systems and the availability of consolidated official data, like CVE (Common Vulnerabilities and Exposures), allows for using them to compute the paths an attacker is likely to follow. However, even if patches are available, business constraints or lack of resources create obstacles to their straightforward application. As a consequence, the security manager of a network needs to deal with a large number of vulnerabilities, making decisions on how to cope with them. This paper presents VULNUS (VULNerabilities visUal aSsessment), a visual analytics solution for dynamically inspecting the vulnerabilities spread on networks, allowing for a quick understanding of the network status and visually classifying nodes according to their vulnerabilities. Moreover, VULNUS computes the approximated optimal sequence of patches able to eliminate all the attack paths and allows for exploring sub-optimal patching strategies, simulating the effect of removing one or more vulnerabilities. VULNUS has been evaluated by domain experts using a lab-test experiment, investigating the effectiveness and efficiency of the proposed solution.
VUSphere: Visual Analysis of Video Utilization in Online Distance Education
Authors: Huan He, Qinghua Zheng, Bo Dong 0001
Published at VAST, 2018

Online Distance Education (ODE) provides massive course videos of various specialties for students across the country to learn professional knowledge anytime and anywhere. Analyzing the utilization of these videos from user log data can help academics better understand the learning process of students, evaluate the quality of service provided by regional learning centers, and improve the quality of program curriculum in the future. However, due to the lack of comparable indicators, it is a great challenge to discover the utilization patterns of massive videos and analyze the learning process of large-scale student population from learning log data. In this paper, we introduce a visual analytics system, called VUSphere, to explore the video utilization from multiple perspectives with two proposed indicators. This system offers three coordinated views: a spherical layout overview to depict the overall utilization distribution of videos, courses, and students; a detailed statistics view with four panels to present video utilization statistics of each element from multiple perspectives; and a comparison view to examine the differences in individual elements. Based on the real dataset from our ODE school, several patterns related to video utilization and enrollment are found in the case study with our domain experts.
A Declarative Grammar of Flexible Volume Visualization Pipelines
Authors: Min Shih, Charles Rozhon, Kwan-Liu Ma
Published at SciVis, 2018

This paper presents a declarative grammar for conveniently and effectively specifying advanced volume visualizations. Existing methods for creating volume visualizations either lack the flexibility to specify sophisticated visualizations or are difficult to use for those unfamiliar with volume rendering implementation and parameterization. Our design provides the ability to quickly create expressive visualizations without knowledge of the volume rendering implementation. It attempts to capture aspects of those difficult but powerful methods while remaining flexible and easy to use. As a proof of concept, our current implementation of the grammar allows users to combine multiple data variables in various ways and define transfer functions for diverse input data. The grammar also has the ability to describe advanced shading effects and create animations. We demonstrate the power and flexibility of our approach using multiple practical volume visualizations.
A Study of the Trade-off Between Reducing Precision and Reducing Resolution for Data Analysis and Visualization
Authors: Duong Hoang, Pavol Klacansky, Harsh Bhatia, Peer-Timo Bremer, Peter Lindstrom 0001, Valerio Pascucci
Published at SciVis, 2018

There currently exist two dominant strategies to reduce data sizes in analysis and visualization: reducing the precision of the data, e.g., through quantization, or reducing its resolution, e.g., by subsampling. Both have advantages and disadvantages and both face fundamental limits at which the reduced information ceases to be useful. The paper explores the additional gains that could be achieved by combining both strategies. In particular, we present a common framework that allows us to study the trade-off in reducing precision and/or resolution in a principled manner. We represent data reduction schemes as progressive streams of bits and study how various bit orderings such as by resolution, by precision, etc., impact the resulting approximation error across a variety of data sets as well as analysis tasks. Furthermore, we compute streams that are optimized for different tasks to serve as lower bounds on the achievable error. Scientific data management systems can use the results presented in this paper as guidance on how to store and stream data to make efficient use of the limited storage and bandwidth in practice.