DevLan/vispubs huggingface dataset:
Orion: A system for modeling, transformation and visualization of multidimensional heterogeneous networks
Authors: Jeffrey Heer, Adam Perer
Published at VAST, 2011

The study of complex activities such as scientific production and software development often require modeling connections among heterogeneous entities including people, institutions and artifacts. Despite numerous advances in algorithms and visualization techniques for understanding such social networks, the process of constructing network models and performing exploratory analysis remains difficult and time-consuming. In this paper we present Orion, a system for interactive modeling, transformation and visualization of network data. Orion's interface enables the rapid manipulation of large graphs-including the specification of complex linking relationships-using simple drag-and-drop operations with desired node types. Orion maps these user interactions to statements in a declarative workflow language that incorporates both relational operators (e.g., selection, aggregation and joins) and network analytics (e.g., centrality measures). We demonstrate how these features enable analysts to flexibly construct and compare networks in domains such as online health communities, academic collaboration and distributed software development.
ParallelTopics: A probabilistic approach to exploring document collections
Authors: Wenwen Dou, Derek Xiaoyu Wang, Remco Chang, William Ribarsky
Published at VAST, 2011

Scalable and effective analysis of large text corpora remains a challenging problem as our ability to collect textual data continues to increase at an exponential rate. To help users make sense of large text corpora, we present a novel visual analytics system, Parallel-Topics, which integrates a state-of-the-art probabilistic topic model Latent Dirichlet Allocation (LDA) with interactive visualization. To describe a corpus of documents, ParallelTopics first extracts a set of semantically meaningful topics using LDA. Unlike most traditional clustering techniques in which a document is assigned to a specific cluster, the LDA model accounts for different topical aspects of each individual document. This permits effective full text analysis of larger documents that may contain multiple topics. To highlight this property of the model, ParallelTopics utilizes the parallel coordinate metaphor to present the probabilistic distribution of a document across topics. Such representation allows the users to discover single-topic vs. multi-topic documents and the relative importance of each topic to a document of interest. In addition, since most text corpora are inherently temporal, ParallelTopics also depicts the topic evolution over time. We have applied ParallelTopics to exploring and analyzing several text corpora, including the scientific proposals awarded by the National Science Foundation and the publications in the VAST community over the years. To demonstrate the efficacy of ParallelTopics, we conducted several expert evaluations, the results of which are reported in this paper.
Perception-based visual quality measures
Authors: Georgia Albuquerque, Martin Eisemann, Marcus A. Magnor
Published at VAST, 2011

In recent years diverse quality measures to support the exploration of high-dimensional data sets have been proposed. Such measures can be very useful to rank and select information-bearing projections of very high dimensional data, when the visual exploration of all possible projections becomes unfeasible. But even though a ranking of the low dimensional projections may support the user in the visual exploration task, different measures deliver different distances between the views that do not necessarily match the expectations of human perception. As an alternative solution, we propose a perception-based approach that, similar to the existing measures, can be used to select information bearing projections of the data. Specifically, we construct a perceptual embedding for the different projections based on the data from a psychophysics study and multi-dimensional scaling. This embedding together with a ranking function is then used to estimate the value of the projections for a specific user task in a perceptual sense.
Pexel and heatmap visual analysis of multidimensional gun/homicide data
Authors: Scott D. Rothenberger, John E. Wenskovitch, G. Elisabeta Marai
Published at VAST, 2011

We present a visual analysis tool for mining correlations in county-level, multidimensional gun/homicide data. The tool uses 2D pexels, heatmaps, linked-views, dynamic queries and details-on-demand to analyze annual county-level data on firearm homicide rates and gun availability, as well as various socio-demographic measures. A statistical significance filter was implemented as a visual means to validate exploratory hypotheses. Results from expert evaluations indicate that our methods outperform typical graphical techniques used by statisticians, such as bar graphs, scatterplots and residual plots, to show spatial and temporal relationships. Our visualization has the potential to convey the impact of gun availability on firearm homicides to the public health arena and the general public.
Pointwise local pattern exploration for sensitivity analysis
Authors: Zhenyu Guo, Matthew O. Ward, Elke A. Rundensteiner, Carolina Ruiz
Published at VAST, 2011

Sensitivity analysis is a powerful method for discovering the significant factors that contribute to targets and understanding the interaction between variables in multivariate datasets. A number of sensitivity analysis methods fall into the class of local analysis, in which the sensitivity is defined as the partial derivatives of a target variable with respect to a group of independent variables. Incorporating sensitivity analysis in visual analytic tools is essential for multivariate phenomena analysis. However, most current multivariate visualization techniques do not allow users to explore local patterns individually for understanding the sensitivity from a pointwise view. In this paper, we present a novel pointwise local pattern exploration system for visual sensitivity analysis. Using this system, analysts are able to explore local patterns and the sensitivity at individual data points, which reveals the relationships between a focal point and its neighbors. During exploration, users are able to interactively change the derivative coefficients to perform sensitivity analysis based on different requirements as well as their domain knowledge. Each local pattern is assigned an outlier factor, so that users can quickly identify anomalous local patterns that do not conform with the global pattern. Users can also compare the local pattern with the global pattern both visually and statistically. Finally, the local pattern is integrated into the original attribute space using color mapping and jittering, which reveals the distribution of the partial derivatives. Case studies with real datasets are used to investigate the effectiveness of the visualizations and interactions.
PORGY: Interactive and visual reasoning with graph rewriting systems
Authors: Bruno Pinaud, Jonathan Dubois, Guy Melançon
Published at VAST, 2011

Graph rewriting systems are easily described and explained. They can be seen as a game where one iterates transformation rules on an initial graph, until some condition is met. A rule describes a local pattern (i.e. a subgraph) that must be identified in a graph and specifies how to transform this subgraph. The graph rewriting formalism is at the same time extremely rich and complex, making the study of a model expressed in terms of graph rewriting quite challenging. For instance, predicting whether rules can be applied in any order is often difficult. When modelling complex systems, graphical formalisms have clear advantages: they are more intuitive and make it easier to visualize a system and convey intuitions about it. This work focuses on the design of an interactive visual graph rewriting system which supports graphical manipulations and computation to reason and simulate on a system. PORGY has been designed based on regular exchanges with graph rewriting systems experts and users over the past three years. The design choices relied on a careful methodology inspired from Munzner's nested process model for visualization design and validation [4].
Query-based coordinated multiple views with Feature Similarity Space for visual analysis of MRI repositories
Authors: Ian Bowman, Shantanu H. Joshi, John D. Van Horn
Published at VAST, 2011

It is a laborious process to quantify relationship patterns within a feature-rich archive. For example, understanding the degree of neuroanatomical similarity between the scanned subjects of a Magnetic Resonance Imaging (MRI) repository is a nontrivial task. In this work we present a Coordinated Multiple View (CMV) system for visually analyzing collections of feature-rich datasets. A query-based user interface operates on a feature-respective data scheme, and is geared towards domain experts that are non-specialists in informatics and analytics. We employ multi-dimensional scaling (MDS) to project feature surface representations into three-dimensions, where proximity in location is proportional to the feature similarity. Through query feedback and environment navigation, the user groups clusters that exhibit probable trends across feature and attribute. The system provides supervised classification methods for determining attribute classes within the user selected groups. Finally, using visual or analytical feature-wise exploration the user determines intra-group feature commonality.
Reasonable abstractions: Semantics for dynamic data visualization
Authors: Joseph A. Cottam, Andrew Lumsdaine
Published at VAST, 2011

Chi showed how to treat visualization programing models abstractly. This provided a firm theoretical basis for the data-state model of visualization. However, Chi's models did not look deeper into fine-grained program properties, such as execution semantics. We present conditionally deterministic and resource bounded semantics for the data flow model of visualization based on E-FRP. These semantics are used in the Stencil system to move between data state and data flow execution, build task-based parallelism, and build complex analysis chains for dynamic data. This initial work also shows promise for other complex operators, compilation techniques to enable efficient use of time and space, and mixing task and data parallelism.
SAVE: Sensor anomaly visualization engine
Authors: Lei Shi 0002, Qi Liao 0002, Yuan He 0004, Rui Li 0047, Aaron Striegel, Zhong Su
Published at VAST, 2011

Diagnosing a large-scale sensor network is a crucial but challenging task. Particular challenges include the resource and bandwidth constraints on sensor nodes, the spatiotemporally dynamic network behaviors, and the lack of accurate models to understand such behaviors in a hostile environment. In this paper, we present the Sensor Anomaly Visualization Engine (SAVE), a system that fully leverages the power of both visualization and anomaly detection analytics to guide the user to quickly and accurately diagnose sensor network failures and faults. SAVE combines customized visualizations over separate sensor data facets as multiple coordinated views. Temporal expansion model, correlation graph and dynamic projection views are proposed to effectively interpret the topological, correlational and dimensional sensor data dynamics and their anomalies. Through a case study with real-world sensor network system and administrators, we demonstrate that SAVE is able to help better locate the system problem and further identify the root cause of major sensor network failure scenarios.
ScatterBlogs: Geo-spatial document analysis
Authors: Harald Bosch, Dennis Thom, Michael Wörner 0001, Steffen Koch 0001, Edwin Puttmann, Dominik Jäckle, Thomas Ertl
Published at VAST, 2011

We presented Scatterblogs, a system for microblog analysis that seamlessly integrates search backend and visual frontend. It provides powerful, automatic algorithms for detecting spatio-temporal `anomalies' within blog entries as well as corresponding visual representations and interaction facilities for inspecting anomalies or exploiting them in further analytic steps. Apart from that, we consider the system's combinatoric facilities for building complex hypotheses from temporal, spatial, and content-related aspects an important feature. This was the key for creating a cross-checked analysis for MC1.
SensePlace2: GeoTwitter analytics support for situational awareness
Authors: Alan M. MacEachren, Anuj R. Jaiswal, Anthony C. Robinson, Scott Pezanowski, Alexander Savelyev, Prasenjit Mitra, Xiao Zhang 0019, Justine I. Blanford
Published at VAST, 2011

Geographically-grounded situational awareness (SA) is critical to crisis management and is essential in many other decision making domains that range from infectious disease monitoring, through regional planning, to political campaigning. Social media are becoming an important information input to support situational assessment (to produce awareness) in all domains. Here, we present a geovisual analytics approach to supporting SA for crisis events using one source of social media, Twitter. Specifically, we focus on leveraging explicit and implicit geographic information for tweets, on developing place-time-theme indexing schemes that support overview+detail methods and that scale analytical capabilities to relatively large tweet volumes, and on providing visual interface methods to enable understanding of place, time, and theme components of evolving situations. Our approach is user-centered, using scenario-based design methods that include formal scenarios to guide design and validate implementation as well as a systematic claims analysis to justify design choices and provide a framework for future testing. The work is informed by a structured survey of practitioners and the end product of Phase-I development is demonstrated / validated through implementation in SensePlace2, a map-based, web application initially focused on tweets but extensible to other media.
Supporting effective common ground construction in Asynchronous Collaborative Visual Analytics
Authors: Yang Chen, Jamal Alsakran, Scott Barlowe, Jing Yang 0001, Ye Zhao 0003
Published at VAST, 2011

Asynchronous Collaborative Visual Analytics (ACVA) leverages group sensemaking by releasing the constraints on when, where, and who works collaboratively. A significant task to be addressed before ACVA can reach its full potential is effective common ground construction, namely the process in which users evaluate insights from individual work to develop a shared understanding of insights and collectively pool them. This is challenging due to the lack of instant communication and scale of collaboration in ACVA. We propose a novel visual analytics approach that automatically gathers, organizes, and summarizes insights to form common ground with reduced human effort. The rich set of visualization and interaction techniques provided in our approach allows users to effectively and flexibly control the common ground construction and review, explore, and compare insights in detail. A working prototype of the approach has been implemented. We have conducted a case study and a user study to demonstrate its effectiveness.
TreeVersity: Comparing tree structures by topology and node's attributes differences
Authors: John Alexis Guerra Gómez, Audra Buck-Coleman, Catherine Plaisant, Ben Shneiderman
Published at VAST, 2011

It is common to classify data in hierarchies, they provide a comprehensible way of understanding big amounts of data. From budgets to organizational charts or even the stock market, trees are everywhere and people find them easy to use. However when analysts need to compare two versions of the same tree structure, or two related taxonomies, the task is not so easy. Much work has been done on this topic, but almost all of it has been restricted to either compare the trees by topology, or by the node attribute values. With this project we are proposing TreeVersity, a framework for comparing tree structures, both by structural changes and by differences in the node attributes. This paper is based on our previous work on comparing traffic agencies using LifeFlow [1, 2] and on a first prototype of TreeVersity.
Using random projections to identify class-separating variables in high-dimensional spaces
Authors: Anushka Anand, Leland Wilkinson, Tommy Dang
Published at VAST, 2011

Projection Pursuit has been an effective method for finding interesting low-dimensional (usually 2D) projections in multidimensional spaces. Unfortunately, projection pursuit is not scalable to high-dimensional spaces. We introduce a novel method for approximating the results of projection pursuit to find class-separating views by using random projections. We build an analytic visualization platform based on this algorithm that is scalable to extremely large problems. Then, we discuss its extension to the recognition of other noteworthy configurations in high-dimensional spaces.
Visual analysis of route diversity
Authors: He Liu, Yuan Gao, Lu Lu, Siyuan Liu, Huamin Qu, Lionel M. Ni
Published at VAST, 2011

Route suggestion is an important feature of GPS navigation systems. Recently, Microsoft T-drive has been enabled to suggest routes chosen by experienced taxi drivers for given source/destination pairs in given time periods, which often take less time than the routes calculated according to distance. However, in real environments, taxi drivers may use different routes to reach the same destination, which we call route diversity. In this paper we first propose a trajectory visualization method that examines the regions where the diversity exists and then develop several novel visualization techniques to display the high dimensional attributes and statistics associated with different routes to help users analyze diversity patterns. Our techniques have been applied to the real trajectory data of thousands of taxis and some interesting findings about route diversity have been obtained. We further demonstrate that our system can be used not only to suggest better routes for drivers but also to analyze traffic bottlenecks for transportation management.
Visual analytic roadblocks for novice investigators
Authors: Bum Chul Kwon, Brian D. Fisher, Ji Soo Yi
Published at VAST, 2011

We have observed increasing interest in visual analytics tools and their applications in investigative analysis. Despite the growing interest and substantial studies regarding the topic, understanding the major roadblocks of using such tools from novice users' perspectives is still limited. Therefore, we attempted to identify such “visual analytic roadblocks” for novice users in an investigative analysis scenario. To achieve this goal, we reviewed the existing models, theories, and frameworks that could explain the cognitive processes of human-visualization interaction in investigative analysis. Then, we conducted a qualitative experiment with six novice participants, using a slightly modified version of pair analytics, and analyzed the results through the open-coding method. As a result, we came up with four visual analytic roadblocks and explained these roadblocks using existing cognitive models and theories. We also provided design suggestions to overcome these roadblocks.
Visual analytical approaches to evaluating uncertainty and bias in crowd sourced crisis information
Authors: Iain Dillingham, Jason Dykes, Jo Wood
Published at VAST, 2011

Concerns about verification mean the humanitarian community are reluctant to use information collected during crisis events, even though such information could potentially enhance the response effort. Consequently, a program of research is presented that aims to evaluate the degree to which uncertainty and bias are found in public collections of incident reports gathered during crisis events. These datasets exemplify a class whose members have spatial and temporal attributes, are gathered from heterogeneous sources, and do not have readily available attribution information. An interactive software prototype, and existing software, are applied to a dataset related to the current armed conflict in Libya to identify `intrinsic' characteristics against which uncertainty and bias can be evaluated. Requirements on the prototype are identified, which in time will be expanded into full research objectives.
Visual analytics decision support environment for epidemic modeling and response evaluation
Authors: Shehzad Afzal, Ross Maciejewski, David S. Ebert
Published at VAST, 2011

In modeling infectious diseases, scientists are studying the mechanisms by which diseases spread, predicting the future course of the outbreak, and evaluating strategies applied to control an epidemic. While recent work has focused on accurately modeling disease spread, less work has been performed in developing interactive decision support tools for analyzing the future course of the outbreak and evaluating potential disease mitigation strategies. The absence of such tools makes it difficult for researchers, analysts and public health officials to evaluate response measures within outbreak scenarios. As such, our research focuses on the development of an interactive decision support environment in which users can explore epidemic models and their impact. This environment provides a spatiotemporal view where users can interactively utilize mitigative response measures and observe the impact of their decision over time. Our system also provides users with a linked decision history visualization and navigation tool that support the simultaneous comparison of mortality and infection rates corresponding to different response measures at different points in time.
Visual analytics of terrorist activities related to epidemics
Authors: Enrico Bertini, Juri Buchmüller, Fabian Fischer 0001, Stephan Huber, Thomas Lindemeier, Fabian Maass, Florian Mansmann, Thomas Ramm, Michael Regenscheit, Christian Rohrdantz, Christian Scheible, Tobias Schreck, Stephan Sellien, Florian Stoffel, Mark Tautzenberger, Matthias Zieker, Daniel A. Keim
Published at VAST, 2011

The task of the VAST 2011 Grand Challenge was to investigate potential terrorist activities and their relation to the spread of an epidemic. Three different data sets were provided as part of three Mini Challenges (MCs). MC 1 was about analyzing geo-tagged microblogging (Twitter) messages to characterize the spread of an epidemic. MC 2 required analyzing threats to a computer network using a situational awareness approach. In MC 3 possible criminal and terrorist activities were to be analyzed based on a collection of news articles. To solve the Grand Challenge, insight from each of the individual MCs had to be integrated appropriately.
Visual sentiment analysis on twitter data streams
Authors: Ming C. Hao, Christian Rohrdantz, Halldór Janetzko, Umeshwar Dayal, Daniel A. Keim, Lars-Erik Haug, Meichun Hsu
Published at VAST, 2011

Twitter currently receives about 190 million tweets (small text-based Web posts) a day, in which people share their comments regarding a wide range of topics. A large number of tweets include opinions about products and services. However, with Twitter being a relatively new phenomenon, these tweets are underutilized as a source for evaluating customer sentiment. To explore high-volume twitter data, we introduce three novel time-based visual sentiment analysis techniques: (1) topic-based sentiment analysis that extracts, maps, and measures customer opinions; (2) stream analysis that identifies interesting tweets based on their density, negativity, and influence characteristics; and (3) pixel cell-based sentiment calendars and high density geo maps that visualize large volumes of data in a single view. We applied these techniques to a variety of twitter data, (e.g., movies, amusement parks, and hotels) to show their distribution and patterns, and to identify influential opinions.