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 )     

基于深度学习的新型视频分析系统综述

摄像设备在生活中的普及,使得视频数据快速增长,这些数据中蕴含丰富的信息.早期,研究人员基于传统的计算机视觉技术开发视频分析系统,用于提取并分析视频数据.近年来,深度学习技术在人脸识别等领域取得了突破性进展,基于深度学习的新型视频分析系统不断涌现.从应用、技术、系统等角度,综述了新型视频分析系统的研究进展.首先,回顾了视频分析系统的发展历史,指出了新型视频分析系统与传统视频分析系统的区别;其次,分析了新型视频分析系统在计算和存储两方面所面临的挑战,从视频数据的组织分布和视频分析的应用需求两方面探讨了新型视频分析系统的影响因素;再次,将新型视频分析系统划分为针对计算优化的系统和针对存储优化的系统两大类,选取其中典型的代表并介绍其核心设计理念;最后,从多个维度对比和分析了新型视频分析系统,指出了这些系统当前存在的问题,并据此展望了新型视频分析系统未来的研究和发展方向.     

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.     

Towards a product design assessment of visual analytics in decision support applications: a systematic review

There is currently an increasing effort to develop visual analytics (VA) tools that can support human analytical reasoning and decision making. In the last decade, advances in this field has allowed the application of various kinds of VA systems in real-world settings. While this represents a promising start from a product design perspective, part of the challenge to the research community is that current VA tools have evolved without due consideration of standardized design criteria and processes. Accordingly, some questions remain to be addressed on what are the useful, underlying attributes of effective VA tools and how their impact can be measured in human-product interaction. These considerations indicate a need to identify a specific range of VA tools and assess their capabilities through state-of-the-art empirical analysis. To address these issues, we conducted a systematic review of 470 VA papers published between 2006 and 2012. We report on the bibliometric techniques, the evaluation attributes and the metrics that were used to sample and analyze the body of literature. The analysis focused mainly on 26 papers that implemented visual analytics decision support tools. The results are presented in the form of six inductively derived design recommendations that, when taken together, uniquely contribute to the fields of product design and visual analytics.     

支持深度学习的视觉数据库管理系统研究进展

计算机视觉因其强大的学习能力,在各种真实场景中得到了广泛应用.随着数据库的发展,利用数据库中成熟的数据管理技术来处理视觉分析应用,已成为一种日益增长的研究趋势.图像、视频和文本等多模态数据的相互融合处理,也促进了视觉分析应用的多样性和准确性.近年来,因深度学习的兴起,支持深度学习的视觉分析应用开始受到广泛关注.然而,传统的数据库管理技术在深度学习场景下面临着复杂视觉分析语义难以表达、应用执行效率低等问题.因此,支持深度学习的视觉数据库管理系统得到了广泛关注.综述了目前视觉数据库管理系统的研究进展:首先,总结了视觉数据库管理系统在不同层面上面临的挑战,包括编程接口、查询优化、执行调度和数据存储;其次,分别探讨了上述4个层面上的相关技术;最后,对视觉数据库管理系统未来的研究方向进行了展望.     

城市道路交通数据可视分析综述

目的 交通是困扰现代大都市的世界性难题.近年来,可视分析技术在分析和利用交通大数据中扮演了越来越重要的角色,成为一项重要的智能交通技术.本文将全面回顾自信息可视化和可视分析兴起以来城市交通数据可视分析领域的研究现状.方法 从道路交通流量分析和其他交通问题分析两个方面,按照数据的类型及问题的分类探讨交通领域的可视化技术和可视分析系统,简单回顾近年来出现的研究新趋势.结果 早期研究注重对道路流量的可视化展示方案,主要方法有箭头图、马赛克图和轨迹墙等.随着可视分析手段的丰富,对城市道路交通流量的分析层次上升到交通事件层面,但是交通事件的定义仅局限于交通拥堵.应用可视分析的其他交通问题领域包括公共交通、交通事故和人群出行行为等.近年出现了挖掘和利用交通轨迹或交通事件的社会属性或称环境上下文信息的研究新趋势.结论 从对交通流量的可视化到交通事件的可视分析,从面向道路交通状况到与交通相关的其他社会性问题,从单纯反映路况的交通数据到富含社会性语义的多源数据,从传统的PC端可视化和交互范式到新型的可视化展示介质,交通数据可视化领域的研究在深度和广度上都得到大大拓展,未来该领域的研究趋势也体现于其中.     

Spatiotemporal data mining: a survey on challenges and open problems

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

     

Social Media Visual Analytics

With the development of social media (e.g. Twitter, Flickr, Foursquare, Sina Weibo, etc.), a large number of people are now using them and post microblogs, messages and multi‐media information. The everyday usage of social media results in big open social media data. The data offer fruitful information and reflect social behaviors of people. There is much visualization and visual analytics research on such data. We collect state‐of‐the‐art research and put it into three main categories: social network, spatial temporal information and text analysis. We further summarize the visual analytics pipeline for the social media, combining the above categories and supporting complex tasks. With these techniques, social media analytics can apply to multiple disciplines. We summarize the applications and public tools to further investigate the challenges and trends.     

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.     

生物医疗健康大数据应用支撑平台与关键技术

随着生物信息学的不断发展,生物医学领域积累了大量的数据,大数据已经贯穿基础研究、临床诊断、医药开发、健康管理等生物医学领域的各个环节。如何有效存储、管理、分析这些海量数据面临严峻的而挑战。基于超级计算机的计算分析和存储能力,在生物医学大数据处理的异构融合架构,面向生物医学大数据的层次式存储系统,生物医学大数据处理的异构并行计算和多源数据的汇聚机制与分析方法,突破生物医学大数据的汇聚、存储、分析等方面的关键技术,构建一个计算、分析处理和存储融合平台,以满足多种类型生物医学大数据应用的不同需求。     

Predicting aspect-based sentiment using deep learning and information visualization: The impact of COVID-19 on the airline industry

This study investigates customer satisfaction through aspect-level sentiment analysis and visual analytics. We collected and examined the flight reviews on TripAdvisor from January 2016 to August 2020 to gauge the impact of COVID-19 on passenger travel sentiment in several aspects. Till now, information systems, management, and tourism research have paid little attention to the use of deep learning and word embedding techniques, such as bidirectional encoder representations from transformers, especially for aspect-level sentiment analysis. This paper aims to identify perceived aspect-based sentiments and predict unrated sentiments for various categories to address this research gap. Ultimately, this study complements existing sentiment analysis methods and extends the use of data-driven and visual analytics approaches to better understand customer satisfaction in the airline industry and within the context of the COVID-19. Our proposed method outperforms baseline comparisons and therefore contributes to the theoretical and managerial literature.     

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。     

vSLAM在无人机平台上的发展研究综述

vSLAM（visual Simultaneous Localization and Mapping）是一种基于视觉传感器实现同时定位与建图的技术，不仅可为地面机器人提供服务，同时在无人机的定位导航中也有着非常重要的应用。对基于无人机的vSLAM发展概况进行整理研究，就其中几大关键方向的研究现状予以介绍，主要包括结合IMU、结合光流传感器的vSLAM，同时总结目前研究中仍存在的一些问题和不足之处。结合经典理论与最新研究动态，对基于无人机的vSLAM重点研究内容和未来发展方向提出了新的展望。     

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.     

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。     

Mapping the study of learning analytics in higher education

ABSTRACT

In recent years, the application of technological innovation in higher education has become more and more widely spread, and technological innovation has been improving the level of education. In the research of higher education with innovation technology, one of the main focuses is on the dynamic data which can lay a foundation for the analysis of educational activities by learning analytics. The dynamic data created by technological innovation will become the key basis for analytical research and development in higher education. The methods and analysis results of learning analytics will directly affect decision-making and strategy about higher education. In this paper, we use bibliometric and visualisation methods to review the literature, in order to highlight the development of learning analytics in higher education. Using bibliometric analysis, our study depicts the development process of the main methods used in learning analytics, and summarises the current situation in this field, which increases the level of understanding provided by those studies. Finally, we summarise the research hotspots and study trends, which will be useful for future study in this field.     

A bibliometric perspective of learning analytics research landscape

ABSTRACT

Learning analytics is an emerging field of research, motivated by the wide spectrum of the available educational information that can be analysed to provide a data-driven decision about various learning problems. This study intends to examine the research landscape of learning analytics to deliver a comprehensive understanding of the research activities in this multidisciplinary field, using scientific literature from the Scopus database. An array of state-of-the-art bibliometric indices is deployed on 2811 procured publication datasets: publication counts, citation counts, co-authorship patterns, citation networks and term co-occurrence. The results indicate that the field of learning analytics appears to have been instantiated around 2011; thus, before this time period no significant research activity can be observed. The temporal evolution indicates that the terms ‘students’, ‘teachers’, ‘higher education institutions’ and ‘learning process’ appear to be the major components of the field. More recent trends in the field are the tools that tap into Big Data analytics and data mining techniques for more rational data-driven decision-making services. A future direction research depicts a need to integrate learning analytics research with multidisciplinary smart education and smart library services. The vision towards smart city research requires a meta-level of smart learning analytics value integration and policy-making.     

A survey on information visualization: recent advances and challenges

Information visualization (InfoVis), the study of transforming data, information, and knowledge into interactive visual representations, is very important to users because it provides mental models of information. The boom in big data analytics has triggered broad use of InfoVis in a variety of domains, ranging from finance to sports to politics. In this paper, we present a comprehensive survey and key insights into this fast-rising area. The research on InfoVis is organized into a taxonomy that contains four main categories, namely empirical methodologies, user interactions, visualization frameworks, and applications, which are each described in terms of their major goals, fundamental principles, recent trends, and state-of-the-art approaches. At the conclusion of this survey, we identify existing technical challenges and propose directions for future research.     

Integrating Visual Analytics Support for Grounded Theory Practice in Qualitative Text Analysis

We present an argument for using visual analytics to aid Grounded Theory methodologies in qualitative data analysis. Grounded theory methods involve the inductive analysis of data to generate novel insights and theoretical constructs. Making sense of unstructured text data is uniquely suited for visual analytics. Using natural language processing techniques such as parts‐of‐speech tagging, retrieving information content, and topic modeling, different parts of the data can be structured and semantically associated, and interactively explored, thereby providing conceptual depth to the guided discovery process. We review grounded theory methods and identify processes that can be enhanced through visual analytic techniques. Next, we develop an interface for qualitative text analysis, and evaluate our design with qualitative research practitioners who analyze texts with and without visual analytics support. The results of our study suggest how visual analytics can be incorporated into qualitative data analysis tools, and the analytic and interpretive benefits that can result.