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This paper aims to describe the popular trends and methods and ICT tools used for mapping and visualization of scientific domains as a research methodology which is attracting more and more interest from scientific information and science studies professionals. Science mapping or bibliometric mapping is a spatial representation of how disciplines, fields, specialties and individual documents or authors. The researchers analysed Bibexel, Pajek, VOSViewer, programmes used for processing and visualization of bibliographic and bibliometric data, within the framework of the implementation of IRNet research network project and activities and presented several examples of visualisation.

Among the main indicators of research effectiveness – bibliometric indicators – is a powerful information tool to support the development of science. Practical research methods were prepared using the programs: Bibexel (metadata analyses), program Pajek: graph editing and visualization of the graph structure of co-authorship – using the method of Kamada-Kawai and VOSviewer: a computer program for creating maps based on network data and for visualizing and exploring these maps. The main features of VOSviewer can be summarized for creating maps based on network data, visualizing and exploring maps.

The cited references and keywords are used to analyse in particular the intellectual base used by the research field or to analyse documents that cite the same references.

The authors of the paper, who are researchers of the European IRNet project and international research network, described and analysed certain bibliographic results of these activities using methods of mapping and visualization of scientific domains.

This study aims to examine the use of data visualization as a tool to support practitioner-led organizational learning within the emergency services. The authors investigate how data visualization can support visual communication and the analysis of emergency response data to promote system improvement.

The authors investigate if communication data, presented as node-link diagrams, can be understood and evaluated by firefighters. Objective understanding of the communication network is measured quantitatively, while subject judgement of the emergency response system is measured qualitatively and compared to prior system evaluation outcomes. The authors compare different data visualization layouts and assess their value in supporting practitioner evaluation of emergency response systems.

The authors find that while firefighters are largely unfamiliar with their use, data visualizations function as a tool for visual communication and analysis. The authors identify the importance of visualization design and the difficulty in representing characteristics of a dynamic network within static diagrams. The authors also find some correlation between layout design and how respondents interpret visual data.

Results demonstrate the value of data visualization to support practitioner-led organizational learning and suggest future work to support the development of emergency response management.

The purpose of this paper is to demonstrate the technical feasibility of implementing multi-view visualization methods to assist auditors in reviewing the integrity of high-volume accounting transactions. Modern enterprise resource planning (ERP) systems record several thousands of transactions daily. This makes it difficult to find a few instances of anomalous activities among legitimate transactions. Although continuous auditing and continuous monitoring systems perform substantial analytics, they often produce lengthy reports that require painstaking post-analysis. Approaches that reduce the burden of excessive information are more likely to contribute to the overall effectiveness of the audit process. The authors address this issue by designing and testing the use of visualization methods to present information graphically, to assist auditors in detecting anomalous and potentially fraudulent accounts payable transactions. The strength of the authors ' approach is its capacity for discovery and recognition of new and unexpected insights.

Data were obtained from the SAP enterprise (ERP) system of a real-world organization. A framework for performing visual analytics was developed and applied to the data to determine its usefulness and effectiveness in identifying anomalous activities.

The paper provides valuable insights into understanding the use of different types of visualizations to effectively identify anomalous activities.

Because this study emphasizes asset misappropriation, generalizing these findings to other categories of fraud, such as accounts receivable, must be made with caution.

This paper provides a framework for developing an automated visualization solution which may have implications in practice.

This paper demonstrates the need to understand the effectiveness of visualizations in detecting accounting fraud. This is directly applicable to organizations investigating methods of improving fraud detection in their ERP systems.

When improving business processes, process analysts can use data-driven methods, such as process mining, to identify improvement opportunities. However, despite being supported by data, process analysts decide which changes to implement. Analysts often use process visualisations to assess and determine which changes to pursue. This paper helps explore how process mining visualisations can aid process analysts in their work to identify, prioritise and communicate business process improvement opportunities.

The study follows the design science methodology to create and evaluate an artefact for visualising identified improvement opportunities (IRVIN).

A set of principles to facilitate the visualisation of process mining outputs for analysts to work with improvement opportunities was suggested. Particularly, insights into identifying, prioritising and communicating process improvement opportunities from visual representation are outlined.

Prior work focuses on visualisation from the perspectives – among others – of process exploration, process comparison and performance analysis. This study, however, considers process mining visualisation that aids in analysing process improvement opportunities.

The purpose of this paper is to assess the efficacy of the Institute of Electrical and Electronics Engineers (IEEE) Xplore digital library search engine to return relevant materials on information visualization pedagogy literature and to recommend search strategies to assist the digital library academic readership improve the efficacy of their search tasks. Furthermore, the results are of interest to general readers using similar digital repositories.

An initial scoping review using EBSCO Discovery services returned the number and accessibility of sources and publications-based various Boolean searches. A revised search strategy focused the search to IEEE publications as the primary source of visualization research. A corpus of keywords were extracted from the 44 relevant articles and analyzed for relevance, keyword trends and contexts of use.

Keyword analysis results show visualization education research is confounded by several information retrieval issues: relevancy, incomplete taxonomy, non-standard lexicon, diverse disciplines and under-representation. Recommendations include: search strategies, alternative digital collections, a potential opportunity for research in information visualization pedagogy to address this gap in an emerging field and the need for more effective interactive tools to assist with keyword selection.

The study focused on the IEEE publications as the primary source of visualization research.

A repository of visualization education research that is easily findable and relevant benefits both faculty using information visualization in their teaching and academics whose work must be disseminated to the broadest audience. Strategic keyword selection, interactive keyword tools or more robust thesaurus will enable IEEE Xplore digital library users to optimize their interaction with the system. Furthermore, results suggest a need for more research in information visualization pedagogy.

This is the only study to uniquely assess the efficacy of the IEEE Xplore digital library database system to retrieve relevant visualization education literature based on keyword search.

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

This paper aims to examine major cognitive biases in auditors’ analyses involving visualization, as well as proposes practical approaches to address such biases in data visualization.

Using the professional judgment framework of KPMG (2011), this study performs an analysis of whether and how five major types of cognitive biases (framing, availability, overconfidence, anchoring and confirmation) may occur in an auditor’s data visualization and how such biases potentially compromise audit quality.

The analysis suggests that data visualization can trigger and/or aggravate the common cognitive biases in audit. If not properly addressed, such biases may adversely affect auditors' judgment and decision-making.

To ensure that data visualization improves audit efficiency and effectiveness, it is essential that auditors are aware of and successfully address cognitive biases in data visualization. Six practical approaches to debias cognitive biases in auditors’ visualization are proposed: using data visualization to complement rather than supplement traditional audit evidence; positioning data visualization to support rather than replace sophisticated analytics tools; using a dashboard with multiple dimensions; using both visualized and tabular data in analyses; assigning experienced audit staff; and providing pre-audit tutorials on cognitive bias and visualization.

The study raises awareness of psychological issues in an audit setting.

The purpose of this paper is to propose a novel approach that automates the visualisation of both quantitative data (the network) and qualitative data (the content) within emails to aid the triage of evidence during a forensics investigation. Email remains a key source of evidence during a digital investigation, and a forensics examiner may be required to triage and analyse large email data sets for evidence. Current practice utilises tools and techniques that require a manual trawl through such data, which is a time-consuming process.

This paper applies the methodology to the Enron email corpus, and in particular one key suspect, to demonstrate the applicability of the approach. Resulting visualisations of network narratives are discussed to show how network narratives may be used to triage large evidence data sets.

Using the network narrative approach enables a forensics examiner to quickly identify relevant evidence within large email data sets. Within the case study presented in this paper, the results identify key witnesses, other actors of interest to the investigation and potential sources of further evidence.

The implications are for digital forensics examiners or for security investigations that involve email data. The approach posited in this paper demonstrates the triage and visualisation of email network narratives to aid an investigation and identify potential sources of electronic evidence.

There are a number of network visualisation applications in use. However, none of these enable the combined visualisation of quantitative and qualitative data to provide a view of what the actors are discussing and how this shapes the network in email data sets.

New computer-assisted techniques for visualizing data are evolving in a number of areas in transportation. For example, in engineering, 3D visualization and microsimulation techniques are being applied for the identification and evaluation of geometric and operational solutions for improving visually impaired pedestrian access to roundabouts and channelized turn lanes. For planning, visualization is being used for corridor analysis. Data visualization is being used as a tool for improving decision-making within transit agencies, as well as a tool for understanding truck trip generation on highways. Many of these new techniques take advantage of archived intelligent transportation systems (ITS) data. Examples of other innovative data sources include global positioning systems (GPS), geographic information systems (GIS), computer-aided design (CAD), and a variety of visualization tools available for use with travel survey data. As these various techniques and software applications move forward, consideration needs to be given to how the “lessons learned” from these applications can facilitate the use of data visualization techniques for travel survey data analysis and decision-making.

Industries’ projects are associated with large amounts of data throughout the project lifecycle. As the size of the projects grows, so does the data associate with the projects. To generate useful information from Big Data, appropriate predictive analysis and data visualisation tools are required. Therefore, the use of visualisation techniques will target this expected evolution of big data. Thus, this study aims to give an overview of the migrations of an existing computer-based application, used to analyse multidata sources of enterprises, to a web-based application. Essential development aspects were concerned with the process of application development, platform development, software utilisation, and efficient display interface according to the enterprise’s requirement. Two solutions such as Microsoft Power Business Intelligence (BI) and the Dynamic Web System have been presented that proven to be widespread because these solutions have met the enterprises’ requirements in terms of expenses, policy, security, maintenance, and interactive interface. Also, an assessment of these solutions and challenges to adopting has been presented. Eventually, the system developed on the web overcomes computer-based systems in solving their problems such as deploying and maintaining applications and providing efficient and richer user interfaces for users. The web-based visualisation application has wide beneficial features; it provides high-performance real-time data analysis, data multimodal visualisation, and friendly to use. Web-based applications will increase confidence in decision-making based on data visualisation, which will positively reflect on comprehensive data analysis processes for project success.