DevLan/vispubs huggingface dataset:
Clustering Trajectories by Relevant Parts for Air Traffic Analysis
Authors: Gennady L. Andrienko, Natalia V. Andrienko, Georg Fuchs, Jose Manuel Cordero Garcia
Published at VAST, 2017

Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.
Clustervision: Visual Supervision of Unsupervised Clustering
Authors: Bum Chul Kwon, Benjamin Eysenbach, Janu Verma, Kenney Ng, Christopher deFilippi, Walter F. Stewart, Adam Perer
Published at VAST, 2017

Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.
Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study
Authors: Jürgen Bernard, Marco Hutter 0002, Matthias Zeppelzauer, Dieter W. Fellner, Michael Sedlmair
Published at VAST, 2017

Labeling data instances is an important task in machine learning and visual analytics. Both fields provide a broad set of labeling strategies, whereby machine learning (and in particular active learning) follows a rather model-centered approach and visual analytics employs rather user-centered approaches (visual-interactive labeling). Both approaches have individual strengths and weaknesses. In this work, we conduct an experiment with three parts to assess and compare the performance of these different labeling strategies. In our study, we (1) identify different visual labeling strategies for user-centered labeling, (2) investigate strengths and weaknesses of labeling strategies for different labeling tasks and task complexities, and (3) shed light on the effect of using different visual encodings to guide the visual-interactive labeling process. We further compare labeling of single versus multiple instances at a time, and quantify the impact on efficiency. We systematically compare the performance of visual interactive labeling with that of active learning. Our main findings are that visual-interactive labeling can outperform active learning, given the condition that dimension reduction separates well the class distributions. Moreover, using dimension reduction in combination with additional visual encodings that expose the internal state of the learning model turns out to improve the performance of visual-interactive labeling.
ConceptVector: Text Visual Analytics via Interactive Lexicon Building Using Word Embedding
Authors: Deok Gun Park 0001, Seungyeon Kim, Jurim Lee, Jaegul Choo, Nicholas Diakopoulos, Niklas Elmqvist
Published at VAST, 2017

Central to many text analysis methods is the notion of a concept: a set of semantically related keywords characterizing a specific object, phenomenon, or theme. Advances in word embedding allow building a concept from a small set of seed terms. However, naive application of such techniques may result in false positive errors because of the polysemy of natural language. To mitigate this problem, we present a visual analytics system called ConceptVector that guides a user in building such concepts and then using them to analyze documents. Document-analysis case studies with real-world datasets demonstrate the fine-grained analysis provided by ConceptVector. To support the elaborate modeling of concepts, we introduce a bipolar concept model and support for specifying irrelevant words. We validate the interactive lexicon building interface by a user study and expert reviews. Quantitative evaluation shows that the bipolar lexicon generated with our methods is comparable to human-generated ones.
CRICTO: Supporting Sensemaking through Crowdsourced Information Schematization
Authors: Haeyong Chung, Sai Prashanth Dasari, Santhosh Nandhakumar, Christopher Andrews
Published at VAST, 2017

We present CRICTO, a new crowdsourcing visual analytics environment for making sense of and analyzing text data, whereby multiple crowdworkers are able to parallelize the simple information schematization tasks of relating and connecting entities across documents. The diverse links from these schematization tasks are then automatically combined and the system visualizes them based on the semantic types of the linkages. CRICTO also includes several tools that allow analysts to interactively explore and refine crowdworkers' results to better support their own sensemaking processes. We evaluated CRICTO's techniques and analysis workflow with deployments of CRICTO using Amazon Mechanical Turk and a user study that assess the effect of crowdsourced schematization in sensemaking tasks. The results of our evaluation show that CRICTO's crowdsourcing approaches and workflow help analysts explore diverse aspects of datasets, and uncover more accurate hidden stories embedded in the text datasets.
CrystalBall: A Visual Analytic System for Future Event Discovery and Analysis from Social Media Data
Authors: Isaac Cho, Ryan Wesslen, Svitlana Volkova, William Ribarsky, Wenwen Dou
Published at VAST, 2017

Social media data bear valuable insights regarding events that occur around the world. Events are inherently temporal and spatial. Existing visual text analysis systems have focused on detecting and analyzing past and ongoing events. Few have leveraged social media information to look for events that may occur in the future. In this paper, we present an interactive visual analytic system, CrystalBall, that automatically identifies and ranks future events from Twitter streams. CrystalBall integrates new methods to discover events with interactive visualizations that permit sensemaking of the identified future events. Our computational methods integrate seven different measures to identify and characterize future events, leveraging information regarding time, location, social networks, and the informativeness of the messages. A visual interface is tightly coupled with the computational methods to present a concise summary of the possible future events. A novel connection graph and glyphs are designed to visualize the characteristics of the future events. To demonstrate the efficacy of CrystalBall in identifying future events and supporting interactive analysis, we present multiple case studies and validation studies on analyzing events derived from Twitter data.
DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks
Authors: Nicola Pezzotti, Thomas Höllt, Jan C. van Gemert, Boudewijn P. F. Lelieveldt, Elmar Eisemann, Anna Vilanova
Published at VAST, 2017

Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform due to the large datasets used for training. In this paper, we present DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems.
Do Convolutional Neural Networks Learn Class Hierarchy?
Authors: Bilal Alsallakh, Amin Jourabloo, Mao Ye, Xiaoming Liu 0002, Ren Liu
Published at VAST, 2017

Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.
Dynamic Influence Networks for Rule-Based Models
Authors: Angus G. Forbes, Andrew Thomas Burks, Kristine Lee, Xing Li, Pierre Boutillier, Jean Krivine, Walter Fontana
Published at VAST, 2017

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.
E-Map: A Visual Analytics Approach for Exploring Significant Event Evolutions in Social Media
Authors: Siming Chen 0001, Shuai Chen 0001, Lijing Lin, Xiaoru Yuan, Christy Jie Liang, Xiaolong Zhang 0001
Published at VAST, 2017

Significant events are often discussed and spread through social media, involving many people. Reposting activities and opinions expressed in social media offer good opportunities to understand the evolution of events. However, the dynamics of reposting activities and the diversity of user comments pose challenges to understand event-related social media data. We propose E-Map, a visual analytics approach that uses map-like visualization tools to help multi-faceted analysis of social media data on a significant event and in-depth understanding of the development of the event. E-Map transforms extracted keywords, messages, and reposting behaviors into map features such as cities, towns, and rivers to build a structured and semantic space for users to explore. It also visualizes complex posting and reposting behaviors as simple trajectories and connections that can be easily followed. By supporting multi-level spatial temporal exploration, E-Map helps to reveal the patterns of event development and key players in an event, disclosing the ways they shape and affect the development of the event. Two cases analysing real-world events confirm the capacities of E-Map in facilitating the analysis of event evolution with social media data.
EVA: Visual Analytics to Identify Fraudulent Events
Authors: Roger A. Leite, Theresia Gschwandtner, Silvia Miksch, Simone Kriglstein, Margit Pohl, Erich Gstrein, Johannes Kuntner
Published at VAST, 2017

Financial institutions are interested in ensuring security and quality for their customers. Banks, for instance, need to identify and stop harmful transactions in a timely manner. In order to detect fraudulent operations, data mining techniques and customer profile analysis are commonly used. However, these approaches are not supported by Visual Analytics techniques yet. Visual Analytics techniques have potential to considerably enhance the knowledge discovery process and increase the detection and prediction accuracy of financial fraud detection systems. Thus, we propose EVA, a Visual Analytics approach for supporting fraud investigation, fine-tuning fraud detection algorithms, and thus, reducing false positive alarms.
EventThread: Visual Summarization and Stage Analysis of Event Sequence Data
Authors: Shunan Guo, Ke Xu, Rongwen Zhao, David Gotz, Hongyuan Zha, Nan Cao
Published at VAST, 2017

Event sequence data such as electronic health records, a person's academic records, or car service records, are ordered series of events which have occurred over a period of time. Analyzing collections of event sequences can reveal common or semantically important sequential patterns. For example, event sequence analysis might reveal frequently used care plans for treating a disease, typical publishing patterns of professors, and the patterns of service that result in a well-maintained car. It is challenging, however, to visually explore large numbers of event sequences, or sequences with large numbers of event types. Existing methods focus on extracting explicitly matching patterns of events using statistical analysis to create stages of event progression over time. However, these methods fail to capture latent clusters of similar but not identical evolutions of event sequences. In this paper, we introduce a novel visualization system named EventThread which clusters event sequences into threads based on tensor analysis and visualizes the latent stage categories and evolution patterns by interactively grouping the threads by similarity into time-specific clusters. We demonstrate the effectiveness of EventThread through usage scenarios in three different application domains and via interviews with an expert user.
Graphiti: Interactive Specification of Attribute-Based Edges for Network Modeling and Visualization
Authors: Arjun Srinivasan, Hyunwoo Park, Alex Endert, Rahul C. Basole
Published at VAST, 2017

Network visualizations, often in the form of node-link diagrams, are an effective means to understand relationships between entities, discover entities with interesting characteristics, and to identify clusters. While several existing tools allow users to visualize pre-defined networks, creating these networks from raw data remains a challenging task, often requiring users to program custom scripts or write complex SQL commands. Some existing tools also allow users to both visualize and model networks. Interaction techniques adopted by these tools often assume users know the exact conditions for defining edges in the resulting networks. This assumption may not always hold true, however. In cases where users do not know much about attributes in the dataset or when there are several attributes to choose from, users may not know which attributes they could use to formulate linking conditions. We propose an alternate interaction technique to model networks that allows users to demonstrate to the system a subset of nodes and links they wish to see in the resulting network. The system, in response, recommends conditions that can be used to model networks based on the specified nodes and links. In this paper, we show how such a demonstration-based interaction technique can be used to model networks by employing it in a prototype tool, Graphiti. Through multiple usage scenarios, we show how Graphiti not only allows users to model networks from a tabular dataset but also facilitates updating a pre-defined network with additional edge types.
How Do Ancestral Traits Shape Family Trees Over Generations?
Authors: Siwei Fu, Hao Dong 0008, Weiwei Cui, Jian Zhao 0010, Huamin Qu
Published at VAST, 2017

Whether and how does the structure of family trees differ by ancestral traits over generations? This is a fundamental question regarding the structural heterogeneity of family trees for the multi-generational transmission research. However, previous work mostly focuses on parent-child scenarios due to the lack of proper tools to handle the complexity of extending the research to multi-generational processes. Through an iterative design study with social scientists and historians, we develop TreeEvo that assists users to generate and test empirical hypotheses for multi-generational research. TreeEvo summarizes and organizes family trees by structural features in a dynamic manner based on a traditional Sankey diagram. A pixel-based technique is further proposed to compactly encode trees with complex structures in each Sankey Node. Detailed information of trees is accessible through a space-efficient visualization with semantic zooming. Moreover, TreeEvo embeds Multinomial Logit Model (MLM) to examine statistical associations between tree structure and ancestral traits. We demonstrate the effectiveness and usefulness of TreeEvo through an in-depth case-study with domain experts using a real-world dataset (containing 54,128 family trees of 126,196 individuals).
Interactive Visual Alignment of Medieval Text Versions
Authors: Stefan Jänicke, David Joseph Wrisley
Published at VAST, 2017

Textual criticism consists of the identification and analysis of variant readings among different versions of a text. Being a relatively simple task for modern languages, the collation of medieval text traditions ranges from the complex to the virtually impossible depending on the degree of instability of textual transmission. We present a visual analytics environment that supports computationally aligning such complex textual differences typical of orally inflected medieval poetry. For the purpose of analyzing alignment, we provide interactive visualizations for different text hierarchy levels, specifically, a meso reading view to support investigating repetition and variance at the line level across text segments. In addition to outlining important aspects of our interdisciplinary collaboration, we emphasize the utility of the proposed system by various usage scenarios in medieval French literature.
LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets
Authors: Jiazhi Xia, Fenjin Ye, Wei Chen 0001, Yusi Wang, Weifeng Chen 0002, Yuxin Ma, Anthony K. H. Tung
Published at VAST, 2017

Many approaches for analyzing a high-dimensional dataset assume that the dataset contains specific structures, e.g., clusters in linear subspaces or non-linear manifolds. This yields a trial-and-error process to verify the appropriate model and parameters. This paper contributes an exploratory interface that supports visual identification of low-dimensional structures in a high-dimensional dataset, and facilitates the optimized selection of data models and configurations. Our key idea is to abstract a set of global and local feature descriptors from the neighborhood graph-based representation of the latent low-dimensional structure, such as pairwise geodesic distance (GD) among points and pairwise local tangent space divergence (LTSD) among pointwise local tangent spaces (LTS). We propose a new LTSD-GD view, which is constructed by mapping LTSD and GD to the$x$axis and$y$axis using 1D multidimensional scaling, respectively. Unlike traditional dimensionality reduction methods that preserve various kinds of distances among points, the LTSD-GD view presents the distribution of pointwise LTS ($x$axis) and the variation of LTS in structures (the combination of$x$axis and$y$axis). We design and implement a suite of visual tools for navigating and reasoning about intrinsic structures of a high-dimensional dataset. Three case studies verify the effectiveness of our approach.
Pattern Trails: Visual Analysis of Pattern Transitions in Subspaces
Authors: Dominik Jäckle, Michael Blumenschein, Michael Behrisch 0001, Daniel A. Keim, Tobias Schreck
Published at VAST, 2017

Subspace analysis methods have gained interest for identifying patterns in subspaces of high-dimensional data. Existing techniques allow to visualize and compare patterns in subspaces. However, many subspace analysis methods produce an abundant amount of patterns, which often remain redundant and are difficult to relate. Creating effective layouts for comparison of subspace patterns remains challenging. We introduce Pattern Trails, a novel approach for visually ordering and comparing subspace patterns. Central to our approach is the notion of pattern transitions as an interpretable structure imposed to order and compare patterns between subspaces. The basic idea is to visualize projections of subspaces side-by-side, and indicate changes between adjacent patterns in the subspaces by a linked representation, hence introducing pattern transitions. Our contributions comprise a systematization for how pairs of subspace patterns can be compared, and how changes can be interpreted in terms of pattern transitions. We also contribute a technique for visual subspace analysis based on a data-driven similarity measure between subspace representations. This measure is useful to order the patterns, and interactively group subspaces to reduce redundancy. We demonstrate the usefulness of our approach by application to several use cases, indicating that data can be meaningfully ordered and interpreted in terms of pattern transitions.