DevLan/vispubs huggingface dataset:
Temporal MDS Plots for Analysis of Multivariate Data
Authors: Dominik Jäckle, Fabian Fischer 0001, Tobias Schreck, Daniel A. Keim
Published at VAST, 2015

Multivariate time series data can be found in many application domains. Examples include data from computer networks, healthcare, social networks, or financial markets. Often, patterns in such data evolve over time among multiple dimensions and are hard to detect. Dimensionality reduction methods such as PCA and MDS allow analysis and visualization of multivariate data, but per se do not provide means to explore multivariate patterns over time. We propose Temporal Multidimensional Scaling (TMDS), a novel visualization technique that computes temporal one-dimensional MDS plots for multivariate data which evolve over time. Using a sliding window approach, MDS is computed for each data window separately, and the results are plotted sequentially along the time axis, taking care of plot alignment. Our TMDS plots enable visual identification of patterns based on multidimensional similarity of the data evolving over time. We demonstrate the usefulness of our approach in the field of network security and show in two case studies how users can iteratively explore the data to identify previously unknown, temporally evolving patterns.
The Data Context Map: Fusing Data and Attributes into a Unified Display
Authors: Shenghui Cheng, Klaus Mueller 0001
Published at VAST, 2015

Numerous methods have been described that allow the visualization of the data matrix. But all suffer from a common problem - observing the data points in the context of the attributes is either impossible or inaccurate. We describe a method that allows these types of comprehensive layouts. We achieve it by combining two similarity matrices typically used in isolation - the matrix encoding the similarity of the attributes and the matrix encoding the similarity of the data points. This combined matrix yields two of the four submatrices needed for a full multi-dimensional scaling type layout. The remaining two submatrices are obtained by creating a fused similarity matrix - one that measures the similarity of the data points with respect to the attributes, and vice versa. The resulting layout places the data objects in direct context of the attributes and hence we call it the data context map. It allows users to simultaneously appreciate (1) the similarity of data objects, (2) the similarity of attributes in the specific scope of the collection of data objects, and (3) the relationships of data objects with attributes and vice versa. The contextual layout also allows data regions to be segmented and labeled based on the locations of the attributes. This enables, for example, the map's application in selection tasks where users seek to identify one or more data objects that best fit a certain configuration of factors, using the map to visually balance the tradeoffs.
The Role of Uncertainty, Awareness, and Trust in Visual Analytics
Authors: Dominik Sacha, Hansi Senaratne, Bum Chul Kwon, Geoffrey P. Ellis, Daniel A. Keim
Published at VAST, 2015

Visual analytics supports humans in generating knowledge from large and often complex datasets. Evidence is collected, collated and cross-linked with our existing knowledge. In the process, a myriad of analytical and visualisation techniques are employed to generate a visual representation of the data. These often introduce their own uncertainties, in addition to the ones inherent in the data, and these propagated and compounded uncertainties can result in impaired decision making. The user's confidence or trust in the results depends on the extent of user's awareness of the underlying uncertainties generated on the system side. This paper unpacks the uncertainties that propagate through visual analytics systems, illustrates how human's perceptual and cognitive biases influence the user's awareness of such uncertainties, and how this affects the user's trust building. The knowledge generation model for visual analytics is used to provide a terminology and framework to discuss the consequences of these aspects in knowledge construction and though examples, machine uncertainty is compared to human trust measures with provenance. Furthermore, guidelines for the design of uncertainty-aware systems are presented that can aid the user in better decision making.
The Visual Causality Analyst: An Interactive Interface for Causal Reasoning
Authors: Jun Wang, Klaus Mueller 0001
Published at VAST, 2015

Uncovering the causal relations that exist among variables in multivariate datasets is one of the ultimate goals in data analytics. Causation is related to correlation but correlation does not imply causation. While a number of casual discovery algorithms have been devised that eliminate spurious correlations from a network, there are no guarantees that all of the inferred causations are indeed true. Hence, bringing a domain expert into the casual reasoning loop can be of great benefit in identifying erroneous casual relationships suggested by the discovery algorithm. To address this need we present the Visual Causal Analyst - a novel visual causal reasoning framework that allows users to apply their expertise, verify and edit causal links, and collaborate with the causal discovery algorithm to identify a valid causal network. Its interface consists of both an interactive 2D graph view and a numerical presentation of salient statistical parameters, such as regression coefficients, p-values, and others. Both help users in gaining a good understanding of the landscape of causal structures particularly when the number of variables is large. Our framework is also novel in that it can handle both numerical and categorical variables within one unified model and return plausible results. We demonstrate its use via a set of case studies using multiple practical datasets.
TimeLineCurator: Interactive Authoring of Visual Timelines from Unstructured Text
Authors: Johanna Fulda, Matthew Brehmer, Tamara Munzner
Published at VAST, 2015

We present TimeLineCurator, a browser-based authoring tool that automatically extracts event data from temporal references in unstructured text documents using natural language processing and encodes them along a visual timeline. Our goal is to facilitate the timeline creation process for journalists and others who tell temporal stories online. Current solutions involve manually extracting and formatting event data from source documents, a process that tends to be tedious and error prone. With TimeLineCurator, a prospective timeline author can quickly identify the extent of time encompassed by a document, as well as the distribution of events occurring along this timeline. Authors can speculatively browse possible documents to quickly determine whether they are appropriate sources of timeline material. TimeLineCurator provides controls for curating and editing events on a timeline, the ability to combine timelines from multiple source documents, and export curated timelines for online deployment. We evaluate TimeLineCurator through a benchmark comparison of entity extraction error against a manual timeline curation process, a preliminary evaluation of the user experience of timeline authoring, a brief qualitative analysis of its visual output, and a discussion of prospective use cases suggested by members of the target author communities following its deployment.
TimeStitch: Interactive multi-focus cohort discovery and comparison
Authors: Peter J. Polack Jr., Shang-Tse Chen, Minsuk Kahng, Moushumi Sharmin, Polo Chau
Published at VAST, 2015

Whereas event-based timelines for healthcare enable users to visualize the chronology of events surrounding events of interest, they are often not designed to aid the discovery, construction, or comparison of associated cohorts. We present TimeStitch, a system that helps health researchers discover and understand events that may cause abstinent smokers to lapse. TimeStitch extracts common sequences of events performed by abstinent smokers from large amounts of mobile health sensor data, and offers a suite of interactive and visualization techniques to enable cohort discovery, construction, and comparison, using extracted sequences as interactive elements. We are extending TimeStitch to support more complex health conditions with high mortality risk, such as reducing hospital readmission in congestive heart failure.
Topicks: Visualizing complex topic models for user comprehension
Authors: Jessica Peter, Steve James Szigeti, Ana Jofre, Sara Diamond
Published at VAST, 2015

The interactive visualization of topic models is a promising approach to summarizing large sets of textual data. Topicks is the working title for a means to visualize topic modelling outputs. Incorporating a radial layout, users can view the relationships between topics, terms and the corpus as a whole. Interacting with topic and term nodes, as well as a related bar chart, provides the user with various ways to manipulate the visualization and explore the data. We describe the visualization and potential user interactions before discussing future work.
TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data
Authors: Xiaoke Huang, Ye Zhao 0003, Chao Ma 0023, Jing Yang 0001, Xinyue Ye, Chong Zhang
Published at VAST, 2015

We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraph's capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis.
Trending pool: Visual analytics for trending event compositions for time-series categorical log data
Authors: Yi-Chih Tsai, Liang-Chi Hsieh, Wen-Feng Cheng, Yin-Hsi Kuo, Winston H. Hsu, Wen-Chin Chen
Published at VAST, 2015

Although many visualization tools provide us plenty of ways to view the data, users can not easily find the trending events and their explanation from the data. In this work, we address the issue by leveraging the real music streaming log data as an example to better understand a million-scale dataset. Trending event explanation turns out to be challenging when it comes to categorical log data. Therefore, we propose to use a learning-based method with an interface design to uncover the trending event compositions for time-series categorical log data, which can be extend to other datasets, e.g., the hashtags in social media. First, we perform “trending pool” operation to save the memory and time cost. Second, we apply sparse coding to learn important trending candidate combination sets instead of traditional brute-force way or manual investigation for generating combinations. Besides the contributions above, we also observe some interesting user behaviors by exploring detected trending candidate combinations visually through our interface.
uRank: Visual analytics approach for search result exploration
Authors: Cecilia di Sciascio, Vedran Sabol, Eduardo E. Veas
Published at VAST, 2015

uRank is a Web-based tool combining lightweight text analytics and visual methods for topic-wise exploration of document sets. It includes a view summarizing the content of the document set in meaningful terms, a dynamic document ranking view and a detailed view for further inspection of individual documents. Its major strength lies in how it supports users in reorganizing documents on-the-fly as their information interests change. We present a preliminary evaluation showing that uRank helps to reduce cognitive load compared to a traditional list-based representation.
Urbane: A 3D framework to support data driven decision making in urban development
Authors: Nivan Ferreira, Marcos Lage, Harish Doraiswamy, Huy T. Vo, Luc Wilson, Heidi Werner, Muchan Park, Cláudio T. Silva
Published at VAST, 2015

Architects working with developers and city planners typically rely on experience, precedent and data analyzed in isolation when making decisions that impact the character of a city. These decisions are critical in enabling vibrant, sustainable environments but must also negotiate a range of complex political and social forces. This requires those shaping the built environment to balance maximizing the value of a new development with its impact on the character of a neighborhood. As a result architects are focused on two issues throughout the decision making process: a) what defines the character of a neighborhood? and b) how will a new development change its neighborhood? In the first, character can be influenced by a variety of factors and understanding the interplay between diverse data sets is crucial; including safety, transportation access, school quality and access to entertainment. In the second, the impact of a new development is measured, for example, by how it impacts the view from the buildings that surround it. In this paper, we work in collaboration with architects to design Urbane, a 3-dimensional multi-resolution framework that enables a data-driven approach for decision making in the design of new urban development. This is accomplished by integrating multiple data layers and impact analysis techniques facilitating architects to explore and assess the effect of these attributes on the character and value of a neighborhood. Several of these data layers, as well as impact analysis, involve working in 3-dimensions and operating in real time. Efficient computation and visualization is accomplished through the use of techniques from computer graphics. We demonstrate the effectiveness of Urbane through a case study of development in Manhattan depicting how a data-driven understanding of the value and impact of speculative buildings can benefit the design-development process between architects, planners and developers.
Using visualization and analysis with efficient dimension Reduction to determine underlying factors in hospital inpatient procedure costs
Authors: Miriam Perkins, Yanlai Chen
Published at VAST, 2015

The Centers for Medicare and Medicaid Services (CMS) has made public a data set showing what hospitals charged and what Medicare paid for the one hundred most common inpatient stays. Here we present the application of Reduced Basis Decomposition (RBD), an efficient novel dimension reduction algorithm for data processing, to the CMS data. This was paired with a comparative visual exploration of the results when put into context with characteristics of the hospitals and marketplaces in which they operate. We used Weave Analyst, a new web-based analysis and visualization environment, to visualize the relationship between the hospital groups, their charge levels, and distinguishing indicator variables. Particular insights to the relatively small number of underlying factors that exert greatest influence on hospital pricing surfaced thanks to the combined synergetic integration of the modeling, reduction, and visualization techniques.
VA2: A Visual Analytics Approach for Evaluating Visual Analytics Applications
Authors: Tanja Blascheck, Markus John, Kuno Kurzhals, Steffen Koch 0001, Thomas Ertl
Published at VAST, 2015

Evaluation has become a fundamental part of visualization research and researchers have employed many approaches from the field of human-computer interaction like measures of task performance, thinking aloud protocols, and analysis of interaction logs. Recently, eye tracking has also become popular to analyze visual strategies of users in this context. This has added another modality and more data, which requires special visualization techniques to analyze this data. However, only few approaches exist that aim at an integrated analysis of multiple concurrent evaluation procedures. The variety, complexity, and sheer amount of such coupled multi-source data streams require a visual analytics approach. Our approach provides a highly interactive visualization environment to display and analyze thinking aloud, interaction, and eye movement data in close relation. Automatic pattern finding algorithms allow an efficient exploratory search and support the reasoning process to derive common eye-interaction-thinking patterns between participants. In addition, our tool equips researchers with mechanisms for searching and verifying expected usage patterns. We apply our approach to a user study involving a visual analytics application and we discuss insights gained from this joint analysis. We anticipate our approach to be applicable to other combinations of evaluation techniques and a broad class of visualization applications.
VAiRoma: A Visual Analytics System for Making Sense of Places, Times, and Events in Roman History
Authors: Isaac Cho, Wenwen Dou, Derek Xiaoyu Wang, Eric Sauda, William Ribarsky
Published at VAST, 2015

Learning and gaining knowledge of Roman history is an area of interest for students and citizens at large. This is an example of a subject with great sweep (with many interrelated sub-topics over, in this case, a 3,000 year history) that is hard to grasp by any individual and, in its full detail, is not available as a coherent story. In this paper, we propose a visual analytics approach to construct a data driven view of Roman history based on a large collection of Wikipedia articles. Extracting and enabling the discovery of useful knowledge on events, places, times, and their connections from large amounts of textual data has always been a challenging task. To this aim, we introduce VAiRoma, a visual analytics system that couples state-of-the-art text analysis methods with an intuitive visual interface to help users make sense of events, places, times, and more importantly, the relationships between them. VAiRoma goes beyond textual content exploration, as it permits users to compare, make connections, and externalize the findings all within the visual interface. As a result, VAiRoma allows users to learn and create new knowledge regarding Roman history in an informed way. We evaluated VAiRoma with 16 participants through a user study, with the task being to learn about roman piazzas through finding relevant articles and new relationships. Our study results showed that the VAiRoma system enables the participants to find more relevant articles and connections compared to Web searches and literature search conducted in a roman library. Subjective feedback on VAiRoma was also very positive. In addition, we ran two case studies that demonstrate how VAiRoma can be used for deeper analysis, permitting the rapid discovery and analysis of a small number of key documents even when the original collection contains hundreds of thousands of documents.
VEEVVIE: Visual Explorer for Empirical Visualization, VR and Interaction Experiments
Authors: Charilaos Papadopoulos, Ievgeniia Gutenko, Arie E. Kaufman
Published at VAST, 2015

Empirical, hypothesis-driven, experimentation is at the heart of the scientific discovery process and has become commonplace in human-factors related fields. To enable the integration of visual analytics in such experiments, we introduce VEEVVIE, the Visual Explorer for Empirical Visualization, VR and Interaction Experiments. VEEVVIE is comprised of a back-end ontology which can model several experimental designs encountered in these fields. This formalization allows VEEVVIE to capture experimental data in a query-able form and makes it accessible through a front-end interface. This front-end offers several multi-dimensional visualization widgets with built-in filtering and highlighting functionality. VEEVVIE is also expandable to support custom experimental measurements and data types through a plug-in visualization widget architecture. We demonstrate VEEVVIE through several case studies of visual analysis, performed on the design and data collected during an experiment on the scalability of high-resolution, immersive, tiled-display walls.
VisOHC: Designing Visual Analytics for Online Health Communities
Authors: Bum Chul Kwon, Sung-Hee Kim, Sukwon Lee, Jaegul Choo, Jina Huh, Ji Soo Yi
Published at VAST, 2015

Through online health communities (OHCs), patients and caregivers exchange their illness experiences and strategies for overcoming the illness, and provide emotional support. To facilitate healthy and lively conversations in these communities, their members should be continuously monitored and nurtured by OHC administrators. The main challenge of OHC administrators' tasks lies in understanding the diverse dimensions of conversation threads that lead to productive discussions in their communities. In this paper, we present a design study in which three domain expert groups participated, an OHC researcher and two OHC administrators of online health communities, which was conducted to find with a visual analytic solution. Through our design study, we characterized the domain goals of OHC administrators and derived tasks to achieve these goals. As a result of this study, we propose a system called VisOHC, which visualizes individual OHC conversation threads as collapsed boxes-a visual metaphor of conversation threads. In addition, we augmented the posters' reply authorship network with marks and/or beams to show conversation dynamics within threads. We also developed unique measures tailored to the characteristics of OHCs, which can be encoded for thread visualizations at the users' requests. Our observation of the two administrators while using VisOHC showed that it supports their tasks and reveals interesting insights into online health communities. Finally, we share our methodological lessons on probing visual designs together with domain experts by allowing them to freely encode measurements into visual variables.