Visual‐Interactive Preprocessing of Multivariate Time Series Data

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.     

An insight-based longitudinal study of visual analytics

Visualization tools are typically evaluated in controlled studies that observe the short-term usage of these tools by participants on preselected data sets and benchmark tasks. Though such studies provide useful suggestions, they miss the long-term usage of the tools. A longitudinal study of a bioinformatics data set analysis is reported here. The main focus of this work is to capture the entire analysts process that an analyst goes through from a raw data set to the insights sought from the data. The study provides interesting observations about the use of visual representations and interaction mechanisms provided by the tools, and also about the process of insight generation in general. This deepens our understanding of visual analytics, guides visualization developers in creating more effective visualization tools in terms of user requirements, and guides evaluators in designing future studies that are more representative of insights sought by users from their data sets     

A Survey on Visual Analysis Approaches for Financial Data

Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision‐making processes based on Financial data have been a popular topic in industries. However, analyzing Financial data is a non‐trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for Financial data exploration. Often, the development of such systems requires researchers to collaborate with Financial domain experts to better extract requirements and challenges in their tasks. Work to systematically study and gather the task requirements and to acquire an overview of existing visualizations and visual analytics systems that have been applied in Financial domains with respect to real‐world data sets has not been completed. To this end, we perform a comprehensive survey of visualizations and visual analytics. In this work, we categorize Financial systems in terms of data sources, applied automated techniques, visualization techniques, interaction, and evaluation methods. For the categorization and characterization, we utilize existing taxonomies of visualization and interaction. In addition, we present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals.     

利用问题求解理论来研究交互式复杂信息的可视分析行为

面对大数据的挑战,力图将人的推理能力和计算系统的数据处理能力相结合的交 互式可视分析研究变得愈发重要。然而目前仍缺乏有效的认知理论来指导面向复杂信息的可视 分析系统的设计,诸如意义构建等现有的理论框架通常着眼于分析行为的外在特征,未能对此 类行为的内在认知机理进行深入研究。因此提出将问题求解作为一种理论框架来解释交互可视 分析行为的基本认知活动,并建议从非良构问题的角度来描述可视分析过程中用户所面临的主 要挑战,还从问题表征及问题求解策略等角度分析了可视分析系统对分析行为的影响。本研究 在理论上,将认知心理学领域的问题求解理论引入到交互可视分析行为的研究中,该方法对设 计面向复杂信息分析的其他类型交互系统也有启示作用;在实践层面上,从问题求解的支持角 度探索了可视分析系统的设计和评估问题。     

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

The analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense‐making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area. (see http://www.acm.org/about/class/class/2012 )     

VA + Embeddings STAR: A State-of-the-Art Report on the Use of Embeddings in Visual Analytics

Over the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term “embedding” when describing the computational approach. Within this context, embeddings are usually (relatively) low-dimensional, distributed representations of various data types (such as texts or graphs), and since they have proven to be extremely useful for a variety of data analysis tasks across various disciplines and fields, they have become widely used. Existing visualization approaches aim to either support exploration and interpretation of the embedding space through visual representation and interaction, or aim to use embeddings as part of the computational pipeline for addressing downstream analytical tasks. To the best of our knowledge, this is the first survey that takes a detailed look at embedding methods through the lens of visual analytics, and the purpose of our survey article is to provide a systematic overview of the state of the art within the emerging field of embedding visualization. We design a categorization scheme for our approach, analyze the current research frontier based on peer-reviewed publications, and discuss existing trends, challenges, and potential research directions for using embeddings in the context of visual analytics. Furthermore, we provide an interactive survey browser for the collected and categorized survey data, which currently includes 122 entries that appeared between 2007 and 2023.     

How information visualization novices construct visualizations

It remains challenging for information visualization novices to rapidly construct visualizations during exploratory data analysis. We conducted an exploratory laboratory study in which information visualization novices explored fictitious sales data by communicating visualization specifications to a human mediator, who rapidly constructed the visualizations using commercial visualization software. We found that three activities were central to the iterative visualization construction process: data attribute selection, visual template selection, and visual mapping specification. The major barriers faced by the participants were translating questions into data attributes, designing visual mappings, and interpreting the visualizations. Partial specification was common, and the participants used simple heuristics and preferred visualizations they were already familiar with, such as bar, line and pie charts. We derived abstract models from our observations that describe barriers in the data exploration process and uncovered how information visualization novices think about visualization specifications. Our findings support the need for tools that suggest potential visualizations and support iterative refinement, that provide explanations and help with learning, and that are tightly integrated into tool support for the overall visual analytics process.     

The State of the Art in Sentiment Visualization

Visualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, including temporal, relational and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer‐reviewed publications together with an interactive web‐based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization. We expect this survey to be useful for visualization researchers whose interests include sentiment or other aspects of text data as well as researchers and practitioners from other disciplines in search of efficient visualization techniques applicable to their tasks and data.     

The State‐of‐the‐Art in Predictive Visual Analytics

Predictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent‐specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end‐users to understand and engage with the modeling process. In this state‐of‐the‐art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics (PVA) pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.     

Human–computer interaction in evolutionary visual software analytics

Software evolution is made up of changes carried out during software maintenance. Such accumulation of changes produces substantial modifications in software projects and therefore vast amounts of relevant facts that are useful for the understanding and comprehension of the software project for making additional changes. In this scenario, evolutionary visual software analytics is aimed to support software maintenance, with the active participation of users, through the understanding and comprehension of software evolution by means of visual analytics and human computer interaction. It is a complex process that takes into account the mining of evolutionary data, the subsequent analysis of the mining process results for producing evolution facts, the use of visualizations supported by interaction techniques and the active participation of users. Hence, this paper explains the evolutionary visual software analytics process, describes a framework proposal and validates such proposal through the definition and implementation of an architecture.     

Hub-based simulation and graphics hardware accelerated visualization for nanotechnology applications

The Network for Computational Nanotechnology (NCN) has developed a science gateway at nanoHUB.org for nanotechnology education and research. Remote users can browse through online seminars and courses, and launch sophisticated nanotechnology simulation tools, all within their web browser. Simulations are supported by a middleware that can route complex jobs to grid supercomputing resources. But what is truly unique about the middleware is the way that it uses hardware accelerated graphics to support both problem setup and result visualization. This paper describes the design and integration of a remote visualization framework into the nanoHUB for interactive visual analytics of nanotechnology simulations. Our services flexibly handle a variety of nanoscience simulations, render them utilizing graphics hardware acceleration in a scalable manner, and deliver them seamlessly through the middleware to the user. Rendering is done only on-demand, as needed, so each graphics hardware unit can simultaneously support many user sessions. Additionally, a novel node distribution scheme further improves our system's scalability. Our approach is not only efficient but also cost-effective. Only a half-dozen render nodes are anticipated to support hundreds of active tool sessions on the nanoHUB. Moreover, this architecture and visual analytics environment provides capabilities that can serve many areas of scientific simulation and analysis beyond nanotechnology with its ability to interactively analyze and visualize multivariate scalar and vector fields.     

A Survey of Visual Analytics for Public Health

We describe visual analytics solutions aiming to support public health professionals, and thus, preventive measures. Prevention aims at advocating behaviour and policy changes likely to improve human health. Public health strives to limit the outbreak of acute diseases as well as the reduction of chronic diseases and injuries. For this purpose, data are collected to identify trends in human health, to derive hypotheses, e.g. related to risk factors, and to get insights in the data and the underlying phenomena. Most public health data have a temporal character. Moreover, the spatial character, e.g. spatial clustering of diseases, needs to be considered for decision-making. Visual analytics techniques involve (subspace) clustering, interaction techniques to identify relevant subpopulations, e.g. being particularly vulnerable to diseases, imputation of missing values, visual queries as well as visualization and interaction techniques for spatio-temporal data. We describe requirements, tasks and visual analytics techniques that are widely used in public health before going into detail with respect to applications. These include outbreak surveillance and epidemiology research, e.g. cancer epidemiology. We classify the solutions based on the visual analytics techniques employed. We also discuss gaps in the current state of the art and resulting research opportunities in a research agenda to advance visual analytics support in public health.     

Human–Computer Collaboration for Visual Analytics: an Agent-based Framework

The visual analytics community has long aimed to understand users better and assist them in their analytic endeavors. As a result, numerous conceptual models of visual analytics aim to formalize common workflows, techniques, and goals leveraged by analysts. While many of the existing approaches are rich in detail, they each are specific to a particular aspect of the visual analytic process. Furthermore, with an ever-expanding array of novel artificial intelligence techniques and advances in visual analytic settings, existing conceptual models may not provide enough expressivity to bridge the two fields. In this work, we propose an agent-based conceptual model for the visual analytic process by drawing parallels from the artificial intelligence literature. We present three examples from the visual analytics literature as case studies and examine them in detail using our framework. Our simple yet robust framework unifies the visual analytic pipeline to enable researchers and practitioners to reason about scenarios that are becoming increasingly prominent in the field, namely mixed-initiative, guided, and collaborative analysis. Furthermore, it will allow us to characterize analysts, visual analytic settings, and guidance from the lenses of human agents, environments, and artificial agents, respectively.     

A bidirectional-transformation-based framework for software visualization and visual editing

Software visualization and visual editing are important and practical techniques to improve the development of complex software systems. A challenge when applying the two technologies is how to realize the correspondence, a bidirectional relationship, between the data and its visual representation correctly. Although many tools and frameworks have been developed to support the construction of visual tools, it is still compli- cated and error-prone to realize the bidirectional relationship. In this paper, we propose a model-driven and bidirectional-transformation-based framework for data visualization and visual editing. Our approach mainly focuses on 1） how to define and manage graphical symbols in the model form and 2） how to specify and im- plement the bidirectional relationship based on the technique of bidirectional model transformation. Then, a prototype tool and four case studies are presented to evaluate the feasibility of our work.     

Interactive visualization of hyperspectral images of historical documents

This paper presents an interactive visualization tool to study and analyze hyperspectral images (HSI) of historical documents. This work is part of a collaborative effort with the Nationaal Archief of the Netherlands (NAN) and Art Innovation, a manufacturer of hyperspectral imaging hardware designed for old and fragile documents. The NAN is actively capturing HSI of historical documents for use in a variety of tasks related to the analysis and management of archival collections, from ink and paper analysis to monitoring the effects of environmental aging. To assist their work, we have developed a comprehensive visualization tool that offers an assortment of visualization and analysis methods, including interactive spectral selection, spectral similarity analysis, time-varying data analysis and visualization, and selective spectral band fusion. This paper describes our visualization software and how it is used to facilitate the tasks needed by our collaborators. Evaluation feedback from our collaborators on how this tool benefits their work is included.     

Art and Chartjunk: A Guide for NEUVis

In the fast-changing and multi-disciplinary practice of artful information visualization, the act of translating data into an image can be fraught with peril. There is considerable debate around modes of visualization and their relationships with the underlying data. This paper outlines the debate between the opposing ideologies through assessment of design considerations and comparisons of creative practice and visual analytics. The authors summarise the current nexus of influences and circumstances and proceed to formulate a set of guidelines for creative practitioners developing visualizations for Non-Expert Users (NEUVis).     

工业数据可视分析综述

自 2013 年工业 4.0 的概念被提出以来,全世界的工业进程都飞速奔向智能制造时代。而数据感知技术的发展进一步帮助收集海量工业数据,工业信息化革新正是机遇。但是,工业数据具有规模大、维度高、结构多变和内容复杂的特性,对其进行分析是一项严峻的挑战。多变的应用场景又导致分析的灵活度要求提高,往往需要专家参与分析循环,因此可视化在工业数据分析中有了更广泛的应用。该综述首先按生产阶段及属性分别总结了工业场景下常用的数据类型;其次,根据数据属性,按时间、空间、时空结合分类,介绍了对应的可视化方法;再次,总结了可视分析在工业场景下的应用,并讨论如何合理地集成自动化分析方法以提高分析能力;最后,展望了工业数据可视分析的发展前景,并提出未来的研究方向。     

大屏数据可视化易用工具的研究与开发

大屏数据可视化是对数据分析结果的表达,是数据赋能决策的重要环节.针对大屏数据可视化软件开发周期较长、成本较高等问题,本文基于Vue前端框架及Echarts可视化组件,研制开发了一个大屏数据可视化易用工具C317DataUI,通过对可视化组件拖拽式操作进行界面布局,使用组件的数据连接面板进行数据配置管理,并提供了部分场景...     

Factors influencing beliefs for adoption of a learning analytics tool: An empirical study

Present research and development offer various learning analytics tools providing insights into different aspects of learning processes. Adoption of a specific tool for practice is based on how its learning analytics are perceived by educators to support their pedagogical and organizational goals. In this paper, we propose and empirically validate a Learning Analytics Acceptance Model (LAAM) of factors influencing the beliefs of educators concerning the adoption a learning analytics tool. In particular, our model explains how the usage beliefs (i.e., ease-of-use and usefulness perceptions) about the learning analytics of a tool are associated with the intention to adopt the tool. In our study, we considered several factors that could potentially affect the adoption beliefs: i) pedagogical knowledge and information design skills of educators; ii) educators' perceived utility of a learning analytics tool; and iii) educators' perceived ease-of-use of a learning analytics tool. By following the principles of Technology Acceptance Model, the study is done with a sample of educators who experimented with a LOCO-Analyst tool. Our study also determined specific analytics types that are primary antecedence of perceived usefulness (concept comprehension and social interaction) and ease-of-use (interactive visualization).