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This paper aims to explore the role of records management in supporting the effective use of information visualisation and visual analytics (VA) to meet the challenges associated with the analysis of Big Data.

This exploratory research entailed conducting and analysing interviews with a convenience sample of visual analysts and VA tool developers, affiliated with a major VA institute, to gain a deeper understanding of data-related issues that constrain or prevent effective visual analysis of large data sets or the use of VA tools, and analysing key emergent themes related to data challenges to map them to records management controls that may be used to address them.

The authors identify key data-related issues that constrain or prevent effective visual analysis of large data sets or the use of VA tools, and identify records management controls that may be used to address these data-related issues.

This paper discusses a relatively new field, VA, which has emerged in response to meeting the challenge of analysing big, open data. It contributes a small exploratory research study aimed at helping records professionals understand the data challenges faced by visual analysts and, by extension, data scientists for the analysis of large and heterogeneous data sets. It further aims to help records professionals identify how records management controls may be used to address data issues in the context of VA.

Visual analytics is increasingly becoming a prominent technology for organizations seeking to gain knowledge and actionable insights from heterogeneous and big data to support decision-making. Whilst a broad range of visual analytics platforms exists, limited research has been conducted to explore the specific factors that influence their adoption in organizations. The purpose of this paper is to develop a framework for visual analytics adoption that synthesizes the factors related to the specific nature and characteristics of visual analytics technology.

This study applies a directed content analysis approach to online evaluation reviews of visual analytics platforms to identify the salient determinants of visual analytics adoption in organizations from the standpoint of practitioners. The online reviews were gathered from Gartner.com, and included a sample of 1,320 reviews for six widely adopted visual analytics platforms.

Based on the content analysis of online reviews, 34 factors emerged as key predictors of visual analytics adoption in organizations. These factors were synthesized into a conceptual framework of visual analytics adoption based on the diffusion of innovations theory and technology–organization–environment framework. The findings of this study demonstrated that the decision to adopt visual analytics technologies is not merely based on the technological factors. Various organizational and environmental factors have also significant influences on visual analytics adoption in organizations.

This study extends the previous work on technology adoption by developing an adoption framework that is aligned with the specific nature and characteristics of visual analytics technology and the factors involved to increase the utilization and business value of visual analytics in organizations.

This study highlights several factors that organizations should consider to facilitate the broad adoption of visual analytics technologies among IT and business professionals.

This study is among the first to use the online evaluation reviews to systematically explore the main factors involved in the acceptance and adoption of visual analytics technologies in organizations. Thus, it has potential to provide theoretical foundations for further research in this important and emerging field. The development of an integrative model synthesizing the salient determinants of visual analytics adoption in enterprises should ultimately allow both information systems researchers and practitioners to better understand how and why users form perceptions to accept and engage in the adoption of visual analytics tools and applications.

This paper aims to provide a better understanding of the challenges and potential solutions in Visual Simultaneous Localization and Mapping (SLAM), laying the foundation for its applications in autonomous navigation, intelligent driving and other related domains.

In analyzing the latest research, the review presents representative achievements, including methods to enhance efficiency, robustness and accuracy. Additionally, the review provides insights into the future development direction of Visual SLAM, emphasizing the importance of improving system robustness when dealing with dynamic environments. The research methodology of this review involves a literature review and data set analysis, enabling a comprehensive understanding of the current status and prospects in the field of Visual SLAM.

This review aims to comprehensively evaluate the latest advances and challenges in the field of Visual SLAM. By collecting and analyzing relevant research papers and classic data sets, it reveals the current issues faced by Visual SLAM in complex environments and proposes potential solutions. The review begins by introducing the fundamental principles and application areas of Visual SLAM, followed by an in-depth discussion of the challenges encountered when dealing with dynamic objects and complex environments. To enhance the performance of SLAM algorithms, researchers have made progress by integrating different sensor modalities, improving feature extraction and incorporating deep learning techniques, driving advancements in the field.

To the best of the authors’ knowledge, the originality of this review lies in its in-depth analysis of current research hotspots and predictions for future development, providing valuable references for researchers in this field.

The construction industry produces voluminous quantitative data. Much of this data is created during the controls phase of projects and relates to cost, schedule, and administrative information. Recent storage and processing advances in computers as well as display capabilities afforded by computer graphics increase the opportunity to monitor projects fundamentally different from existing project control systems. However, changes in project control methods have been slow to evolve. The lack of a fundamental model of project control data representation contributes to the inadequate application and implementation of visual tools in project control methods. Difficulties associated with the graphical representation of data can be traced to the diversity of skills required in creating visual information displays. Owing to the reality that not all engineers/constructors possess these attributes in great strength, streamlining the process of how to best visualize data is important. Visual representations of data hold great potential for reducing communication difficulties fostered by industry fragmentation. However, without information structure, organization, and visual explanations, the massive amount of data available to project managers results in information overload. Therefore, improved information displays are needed to overcome the possibility of information overload with the capability of human perception. This paper discusses research to create a framework for visual representation of construction project data. Underlying visualization theory, the visual framework, and a detailed implementation are provided.

The purpose of this study is to conduct a comprehensive investigation into the utilization of visual impression management techniques within sustainability reporting. Specifically, the study aims to determine whether Italian companies employ impression management tactics in the presentation of graphs within their sustainability reports and, thus, problematize visual data communication in corporate social responsibility (CSR).

The research adopts a multimodal content analysis of the 58 sustainability reports from Italian listed companies that are GRI-compliant. The analysis focused on three types of graphs: pie charts, line graphs and bar graphs. In total, 860 graphs have been examined.

The study found evidence of graphical distortion techniques being employed by companies in their sustainability reports to create a favorable impression. Specifically, graph distortions are found in column graphs and not in line or pie charts. In particular, selectivity, presentation enhancement and measurement distortion techniques seem to be extensively used when adopting column graphs in sustainability communication. Moreover, social sustainability–related topics tend to be more represented of other area of CSR reporting. This suggests that companies, whether consciously or unconsciously, engage in impression management techniques when using graphs in their sustainability reports.

The study findings suggest that more consciousness is needed for companies when engaging in the construction and selection of graphs in their sustainability reports and that decision-makers should develop a clear guide for ethical visual communication.

The paper systematically analyzes visual impression management techniques in communicating sustainability data and, in particular, advances literature on graphical distortion. The value lies in empirical evidence of distortion adoption in GRI-compliant reports as well as problematizing visual data communication as a fundamental challenge for sustainability communication management.

Digital tools have been used to document cultural heritage with high-quality imaging and metadata. However, some of the historical assets are totally or partially unlabeled and some are physically damaged, which decreases their attractiveness and induces loss of value. This paper introduces a new framework that aims at tackling the cultural data enrichment challenge using machine learning.

This framework focuses on the automatic annotation and metadata completion through new deep learning classification and annotation methods. It also addresses issues related to physically damaged heritage objects through a new image reconstruction approach based on supervised and unsupervised learning.

The authors evaluate approaches on a data set of cultural objects collected from various cultural institutions around the world. For annotation and classification part of this study, the authors proposed and implemented a hierarchical multimodal classifier that improves the quality of annotation and increases the accuracy of the model, thanks to the introduction of multitask multimodal learning. Regarding cultural data visual reconstruction, the proposed clustering-based method, which combines supervised and unsupervised learning is found to yield better quality completion than existing inpainting frameworks.

This research work is original in sense that it proposes new approaches for the cultural data enrichment, and to the authors’ knowledge, none of the existing enrichment approaches focus on providing an integrated framework based on machine learning to solve current challenges in cultural heritage. These challenges, which are identified by the authors are related to metadata annotation and visual reconstruction.

This paper aims to review and critically assess the role that data visualizations played as communication media tools to help society during a worldwide crisis. This paper re-creates and analyzes several visualizations, critically and ethically assesses their strengths and limitations and provides a set of best practices that are informative, accurate, ethical and engaging at each stage in a reader’s interest.

The paper bases its methodology on the construct of “The Network Society” (Van Dijk, 2006; Castells, 2000, 2006) by creating a series of social networked visualizations, identifying the challenges and pitfalls associated with this communication approach and suggesting best practices in information communication technology. The case study is COVID-19.

The research in this study found that visual data dashboards and interactive Web-based charts did play a significant role in helping society understand COVID-19’s impact to make better informed decisions about society’s health and safety.

Visual expositions of data do have strengths and weaknesses depending on how they are designed, how they communicate the story and how they are ethically deployed. Best practices are provided to help mitigate these limitations.

Visualizations are certainly not new, but the technology for rapidly developing and sharing them is new. Visual expositions provide an effective media for communicating complex information to a networked society.

Visual expositions provide an effective media for communicating complex information to a networked society.

This paper highlights the significance of the need to understand complex data in a crisis in a visual format and to communicate the information quickly, persuasively, effectively and ethically to a networked audience.

Simultaneous localization and map building (SLAM), as a state estimation problem, is a prerequisite for solving the problem of autonomous vehicle motion in unknown environments. Existing algorithms are based on laser or visual odometry; however, the lidar sensing range is small, the amount of data features is small, the camera is vulnerable to external conditions and the localization and map building cannot be performed stably and accurately using a single sensor. This paper aims to propose a laser three dimensions tightly coupled map building method that incorporates visual information, and uses laser point cloud information and image information to complement each other to improve the overall performance of the algorithm.

The visual feature points are first matched at the front end of the method, and the mismatched point pairs are removed using the bidirectional random sample consensus (RANSAC) algorithm. The laser point cloud is then used to obtain its depth information, while the two types of feature points are fed into the pose estimation module for a tightly coupled local bundle adjustment solution using a heuristic simulated annealing algorithm. Finally, the visual bag-of-words model is fused in the laser point cloud information to establish a threshold to construct a loopback framework to further reduce the cumulative drift error of the system over time.

Experiments on publicly available data sets show that the proposed method in this paper can match its real trajectory well. For various scenes, the map can be constructed by using the complementary laser and vision sensors, with high accuracy and robustness. At the same time, the method is verified in a real environment using an autonomous walking acquisition platform, and the system loaded with the method can run well for a long time and take into account the environmental adaptability of multiple scenes.

A multi-sensor data tight coupling method is proposed to fuse laser and vision information for optimal solution of the positional attitude. A bidirectional RANSAC algorithm is used for the removal of visual mismatched point pairs. Further, oriented fast and rotated brief feature points are used to build a bag-of-words model and construct a real-time loopback framework to reduce error accumulation. According to the experimental validation results, the accuracy and robustness of the single-sensor SLAM algorithm can be improved.

The purpose of this paper is to improve decision quality, and therefore project and portfolio success, by testing the influence of different visual representations of interdependency data in a simulated decision experiment. A network mapping approach to visualize project interdependencies is introduced and compared with matrix and tabular displays.

A simulated decision task in a controlled classroom setting tested five hypotheses though a sample of 480 experiments.

The type of data representation used is associated with differing levels of decision quality, and the use of network mapping displays is aligned with the best results.

The findings are limited as this experiment-based study presented a simplified decision scenario and involved students rather than practicing managers. The findings are best interpreted in combination with organization-based research.

The findings of this study suggest that visual data displays, particularly network mapping displays, can provide benefits and improve project portfolio decision quality. Managers may draw upon this study to design ways to include visual data representations in their project portfolio management decision processes.

This study uses experimentation to complement organization-based studies to better understand the influence of different methods of visualizing data and managing interdependencies between projects. This research provides an important contribution to meet the acknowledged need for better tools to understand and manage project interdependencies.

This study aims to explore the visual social media (SM) (Instagram) communication and the visual characteristics of smart tourism destination (STD) communication from destination marketing/management organizations (DMOs) and user-generated content (UGC) perspectives, which refer to projected image and perceived image, respectively.

Three DMO official accounts of STDs (Helsinki, Gothenburg and Lyon) and corresponding official hashtags were selected for the sample and total 6,000 post data (1,000 × 6) were retrieved from Instagram. Visual communication content was examined with a netnographic design over a proposed four-level visual content framework using corresponding methodological approaches (thematic analysis, visual analysis, object detection and text mining) for each level.

Among the eight emerging themes dominating the images, communication of smart elements conveys far less than expected textual and visual signals from DMOs despite their smart status, and in turn, from UGC as well. UGC revealed three extra image themes regardless of smartness perception. DMOs tend to project and give voice to their standard metropolitan areas and neighborhoods while UGCs focus on food-related and emotional elements. The findings show a partial overlap between DMOs and UGCs, revealing discrepancies in objects contained in visuals, hashtags and emojis. Additionally, as a rare attempt, the proposed framework for visual content analysis showed the importance of integrated methods to investigate visual content effectively.

The number of attributes in visual analysis and focusing on the observed elements in text content (text, hashtags and emojis) are the limitations of the study in terms of methodology.

Apart from the multiple integrated methods used over a netnographic design, this study differs from existing SM and smart destinations intersection literature by attempting to fill a gap in focusing on and exploring visual SM communication, which is scarce in tourism context, for the contents generated by DMOs and users.

本研究旨在探讨从目的地营销管理组织（DMO）和用户生成的内容（UGC）获取的视觉社交网站传播（instagram）以及智慧旅游目的地的视觉特征。这两个角度在本文中分别命名为投射形象和感知形象。

本文收集了三个智慧旅游目的地（赫尔辛基、哥德堡、里昂）的DMO官方账号及其对应的官方标签数据, 其中从Instagram获取了一共6000个帖子（1000 x 6）。视觉传播内容是通过网络民族志设计进行分析的, 该设计采用四层视觉内容框架, 每一层使用相应的方法, 包括主题分析、视觉分析、目标检测、文本挖掘。

在8个新兴的主导形象中, 智慧元素, 尽管是智能的, 但其传达的信息远不如预期的来自DMO的文本和视觉信号, 同样, 也不如来自UGC的信息。无论智慧感知程度如何, UGC显示了3个额外的形象。DMO倾向于展现和表达目的地标准的大都会地区及其临近社区, 而UGC专注于与食物相关的和情感的元素。我们的研究显示了DMO和UGC内容的部分重叠, 但揭示了两者在图像、标签和表情符号中包含对象的差异。此外, 作为一个少有的尝试, 我们提出的视觉内容分析框架展示了集成方法对有效研究视觉内容的重要性。

从方法上来说, 视觉分析中属性的数量和在文本语境中对观察元素的关注是本研究的局限性

除了在网络民族志设计中使用多个集成方法,本文和以往社交媒体和智慧目的地交互文献的区别还体现在关注和探索DMO和UGC生成内容的视觉社交媒体沟通, 这类研究旅游研究情境下是少见的。