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PAVED: Pareto Front visualization for Engineering Design

COMPUTER GRAPHICS FORUM 2020年第3期39卷 405-416页

作者： Cibulski, Lena Mitterhofer, Hubert May, Thorsten Kohlhammer, Joern Fraunhofer IGD Darmstadt Germany Linz Ctr Mech GmbH Linz Austria Tech Univ Darmstadt Darmstadt Germany

Design problems in engineering typically involve a large solution space and several potentially conflicting criteria. Selecting a compromise solution is often supported by optimization algorithms that compute hundreds of Pareto-optimal solutions, thus informing a decision by the engineer. However, the complexity of evaluating and comparing alternatives increases with the number of criteria that need to be considered at the same time. We present a design study on Pareto front visualization to support engineers in applying their expertise and subjective preferences for selection of the most-preferred solution. We provide a characterization of data and tasks from the parametric design of electric motors. The requirements identified were the basis for our development of PAVED, an interactive parallel coordinates visualization for exploration of multi-criteria alternatives. We reflect on our user-centered design process that included iterative refinement with real data in close collaboration with a domain expert as well as a summative evaluation in the field. The results suggest a high usability of our visualization as part of a real-world engineering design workflow. Our lessons learned can serve as guidance to future visualization developers targeting multi-criteria optimization problems in engineering design or alternative domains.

关键词： CCS Concepts • human‐centered computing → visual analytics Applied computing → Engineering

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SeqDynamics: visual analytics for Evaluating Online Problem-solving Dynamics

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COMPUTER GRAPHICS FORUM 2020年第3期39卷 511-522页

作者： Xia, Meng Xu, Min Lin, Chuan-en Cheng, Ta Ying Qu, Huamin Ma, Xiaojuan Hong Kong Univ Sci & Technol Hong Kong Peoples R China

Problem-solving dynamics refers to the process of solving a series of problems over time, from which a student's cognitive skills and non-cognitive traits and behaviors can be inferred. For example, we can derive a student's learning curve (an indicator of cognitive skill) from the changes in the difficulty level of problems solved, or derive a student's self-regulation patterns (an example of non-cognitive traits and behaviors) based on the problem-solving frequency over time. Few studies provide an integrated overview of both aspects by unfolding the problem-solving process. In this paper, we present a visual analytics system named SeqDynamics that evaluates students' problem-solving dynamics from both cognitive and non-cognitive perspectives. The system visualizes the chronological sequence of learners' problem-solving behavior through a set of novel visual designs and coordinated contextual views, enabling users to compare and evaluate problem-solving dynamics on multiple scales. We present three scenarios to demonstrate the usefulness of SeqDynamics on a real-world dataset which consists of thousands of problem-solving traces. We also conduct five expert interviews to show that SeqDynamics enhances domain experts' understanding of learning behavior sequences and assists them in completing evaluation tasks efficiently.

关键词： CCS Concepts • human‐centered computing → visual analytics • Applied computing → E‐learning

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Data Comets: Designing a visualization Tool for Analyzing Autonomous Aerial Vehicle Logs with Grounded Evaluation

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COMPUTER GRAPHICS FORUM 2020年第3期39卷 455-468页

作者： Saffo, David Leventidis, Aristotelis Jain, Twinkle Borkin, Michelle A. Dunne, Cody Northeastern Univ Khoury Coll Comp Sci Boston MA 02115 USA

Autonomous unmanned aerial vehicles are complex systems of hardware, software, and human input. Understanding this complexity is key to their development and operation. Information visualizations already exist for exploring flight logs but comprehensive analyses currently require several disparate and custom tools. This design study helps address the pain points faced by autonomous unmanned aerial vehicle developers and operators. We contribute: a spiral development process model for grounded evaluation visualization development focused on progressively broadening target user involvement and refining user goals;a demonstration of the model as part of developing a deployed and adopted visualization system;a data and task abstraction for developers and operators performing post-flight analysis of autonomous unmanned aerial vehicle logs;the design and implementation of DATA COMETS, an open-source and web-based interactive visualization tool for post-flight log analysis incorporating temporal, geospatial, and multivariate data;and the results of a summative evaluation of the visualization system and our abstractions based on in-the-wild usage. A free copy of this paper and source code are available at ***/h4p7g

关键词： CCS Concepts • human‐centered computing → visual analytics Geographic visualization visualization design and evaluation methods

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A visual analytics Approach to Facilitate Crime Hotspot Analysis

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COMPUTER GRAPHICS FORUM 2020年第3期39卷 139-151页

作者： de Queiroz Neto, Jose F. Santos, Emanuele Vidal, Creto Augusto Ebert, David S. Univ Fed Ceara Fortaleza Ceara Brazil Purdue Univ W Lafayette IN 47907 USA

Computer-based technology has played a significant role in crime prevention over the past 30 years, especially with the popularization of spatial databases and crime mapping systems. Police departments frequently use hotspot analysis to identify regions that should be a priority in receiving preventive resources. Practitioners and researchers agree that tracking crime over time and identifying its geographic patterns are vital information for planning efficiently. Frequently, police departments have access to systems that are too complicated and excessively technical, leading to modest usage. By working closely together with domain experts from police agencies of two different countries, we identified and characterized five domain tasks inherent to the hotspot analysis problem and developed SHOC, a visualization tool that strives for simplicity and ease of use in helping users to perform all the domain tasks. SHOC is included in a visual analytics system that allows users without technical expertise to annotate, save, and share analyses. We also demonstrate that our system effectively supports the completion of the domain tasks in two different real-world case studies.

关键词： CCS Concepts • human‐centered computing → visual analytics visualization systems and tools

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DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning

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COMPUTER GRAPHICS FORUM 2020年第3期39卷 49-61页

作者： Jaunet, T. Vuillemot, R. Wolf, C. INSA Lyon LIRIS Lyon France Ecole Cent Lyon LIRIS Lyon France INRIA CITI Rocquencourt France

We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co-correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explain-ability in the field of visual analytics.

关键词： CCS Concepts • human‐centered computing → visual analytics • Theory of computation → Reinforcement learning

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QUESTO: Interactive Construction of Objective Functions for Classification Tasks

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COMPUTER GRAPHICS FORUM 2020年第3期39卷 153-165页

作者： Das, Subhajit Xu, Shenyu Gleicher, Michael Chang, Remco Endert, Alex Georgia Inst Technol Atlanta GA 30332 USA Univ Wisconsin Madison WI USA Tufts Univ Medford MA 02155 USA

Building effective classifiers requires providing the modeling algorithms with information about the training data and modeling goals in order to create a model that makes proper tradeoffs. Machine learning algorithms allow for flexible specification of such meta-information through the design of the objective functions that they solve. However, such objective functions are hard for users to specify as they are a specific mathematical formulation of their intents. In this paper, we present an approach that allows users to generate objective functions for classification problems through an interactive visual interface. Our approach adopts a semantic interaction design in that user interactions over data elements in the visualization are translated into objective function terms. The generated objective functions are solved by a machine learning solver that provides candidate models, which can be inspected by the user, and used to suggest refinements to the specifications. We demonstrate a visual analytics system QUESTO for users to manipulate objective functions to define domain-specific constraints. Through a user study we show that QUESTO helps users create various objective functions that satisfy their goals.

关键词： CCS Concepts • computing methodologies → Model construction and selection • Mathematics of computing → Interactive objective functions • human‐centered computing → visual analytics • Machine learning task → Classification

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VA-TRAC: Geospatial Trajectory Analysis for Monitoring, Identification, and Verification in Fishing Vessel Operations

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COMPUTER GRAPHICS FORUM 2020年第3期39卷 101-114页

作者： Storm-Furru, S. Bruckner, S. Univ Bergen Bergen Norway

In order to ensure sustainability, fishing operations are governed by many rules and regulations that restrict the use of certain techniques and equipment, specify the species and size of fish that can be harvested, and regulate commercial activities based on licensing schemes. As the world's second largest exporter of fish and seafood products, Norway invests a significant amount of effort into maintaining natural ecosystem dynamics by ensuring compliance with its constantly evolving science-based regulatory body. This paper introduces VA-TRAC, a geovisual analytics application developed in collaboration with the Norwegian Directorate of Fisheries in order to address this complex task. Our approach uses automatic methods to identify possible catch operations based on fishing vessel trajectories, embedded in an interactive web-based visual interface used to explore the results, compare them with licensing information, and incorporate the analysts' domain knowledge into the decision making process. We present a data and task analysis based on a close collaboration with domain experts, and the design and implementation of VA-TRAC to address the identified requirements.

关键词： CCS Concepts • human‐centered computing → visual analytics Geographic visualization

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visualizing for the Non-visual: Enabling the visually Impaired to Use visualization

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COMPUTER GRAPHICS FORUM 2019年第3期38卷 249-260页

作者： Choi, Jinho Jung, Sanghun Park, Deok Gun Cho, Jaegul Elmqvist, Niklas Korea Univ Seoul South Korea Univ Texas Arlington Arlington TX 76019 USA Univ Maryland College Pk MD 20742 USA

The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep-neural-network-based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back-end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.

关键词： CCS Concepts • human‐centered computing → visual analytics visualization toolkits

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visual-Interactive Preprocessing of Multivariate Time Series Data

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COMPUTER GRAPHICS FORUM 2019年第3期38卷 401-412页

作者： Bernard, Juergen Hutter, Marco Reinemuth, Heiko Pfeifer, Hendrik Bors, Christian Kohlhammer, Joern Tech Univ Darmstadt Darmstadt Germany TU Wien Vienna Austria Fraunhofer IGD Darmstadt Germany

Pre-processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre-processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre-processing pipelines, human-in-the-loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in-depth research in visual analytics. We present a visual-interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre-processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty-aware pre-processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre-processing in general and for uncertainty-aware pre-processing of multivariate time series in particular.

关键词： CCS Concepts Mathematics of computing → Time series analysis • human‐centered computing → visual analytics

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ClustMe: A visual Quality Measure for Ranking Monochrome Scatterplots based on Cluster Patterns

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COMPUTER GRAPHICS FORUM 2019年第3期38卷 225-236页

作者： Abbas, Mostafa M. Aupetit, Michael Sedlmair, Michael Bensmail, Halima HBKU QCRI Doha Qatar Univ Stuttgart VISUS Stuttgart Germany

We propose ClustMe, a new visual quality measure to rank monochrome scatterplots based on cluster patterns. ClustMe is based on data collected from a human-subjects study, in which 34 participants judged synthetically generated cluster patterns in 1000 scatterplots. We generated these patterns by carefully varying the free parameters of a simple Gaussian Mixture Model with two components, and asked the participants to count the number of clusters they could see (1 or more than 1). Based on the results, we form ClustMe by selecting the model that best predicts these human judgments among 7 different state-of-the-art merging techniques (Demp). To quantitatively evaluate ClustMe, we conducted a second study, in which 31 human subjects ranked 435 pairs of scatterplots of real and synthetic data in terms of cluster patterns complexity. We use this data to compare ClustMe's performance to 4 other state-of-the-art clustering measures, including the well-known Clumpiness scagnostics. We found that of all measures, ClustMe is in strongest agreement with the human rankings.

关键词： CCS Concepts • human‐centered computing → visual analytics Empirical studies in visualization • computing methodologies → Cluster analysis Mixture modeling

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