Figure 2 - uploaded by Eliane Zambon Victorelli
Content may be subject to copyright.
Views of booking.com website with perceptive clues relating multiple views consistently. (https://www. booking.com/)
Contexts in source publication
Context 1
... users can learn systems with self-evident relationships more quickly [27]. Figure 2 shows an example of the Booking.com website 1 , presenting coordinated views with properties filtered on map and in the list with details. ...
Similar publications
With the rapid development of interactive technology, creating systems that allow users to define their interactive envelope freely and provide multi-interactive modalities is important to build up an intuitive interactive space. We present an indoor interactive system where a human can customize and interact through a projected screen utilizing th...
O presente trabalho tem como objetivo apresentar uma proposta de interface digital que permita avaliar a proficiência, no Português Europeu (PE), de crianças lusodescendentes na diáspora, e que forneça material informativo sobre a aquisição precoce de línguas de herança. No desenvolvimento deste trabalho foram expostos e discutidos os obstáculos à...
The interactive digital narrative (IDN) is an interdisciplinary field connected to narrative studies, design, human-computer interaction, and gaming. In this contribution, IDN is investigated from the designer’s perspective; it deals with the use of prolific artificial intelligence (AI) support systems in this area, highlighting the need to examine...
Citations
... In addition, overviews reduce the cognitive effort for interpretation and aid user navigation through the data space [Hornbaek and Hertzum, 2011]. The Human-Data Interaction Design Guidelines of Victorelli and Reis [2020] recommend reducing the information density and gradually revealing the data. ...
From a Human-Computer Interaction perspective, data visualizations are visual representations of data that improve users' cognitive capabilities during a task. In particular, UX data visibility can raise a team's engagement with the UX design and better inform product decisions. However, researchers and professionals lack a foundation to build new UX data visualizations. In this context, this paper describes a Systematic Mapping of the Literature that aims to consolidate the state of the art on UX data visualizations. To guide the open coding of the findings, we defined ten questions that span the Visual Information Seeking Mantra and the four levels of Munzner's analysis framework. We identified 28 well-known and seven custom chart formats, with the node-link diagram arising as the most popular. Most of the visualized data comes from software logs, and there is a lack of exploration of UX metrics, acoustic data, and demographic data as data sources. Regarding the Visual Information Seeking Mantra, visualizations had a zoom and filter function and a details on demand function for most chart formats. However, most chart formats lacked overview functions. Our findings provide a broad overview of the literature that can support the creation of new UX data visualizations.
... Interaction design The process of designing the interaction between the user and the product. [43,44] Eye tracking The visual attention and interaction behavior of users are studied by tracking their eye movements. [45][46][47][48][49][50][51][52][53][54][55] Multimodal interaction Interact using multiple senses and inputs. ...
This paper focuses on the application of human–computer interaction technology in construction project safety management. Through bibliometric methods, we carried out an in-depth analysis of 286 relevant papers from Web of Science and Google Scholar from 2000 to 2024. The research results indicate that human–computer interaction technology has achieved remarkable development in four aspects: intelligent monitoring systems, risk assessment and management, ergonomics and cognitive psychology, as well as computer simulation and virtual reality. Meanwhile, this research has given rise to a series of new research topics, such as the safety operation decision-making method for intelligent construction machinery, the application of human action behavior recognition technology, and the application of Internet of Things technology in the safety control of smart construction sites. Additionally, future research modules have been identified, including personalized safety training, digital twin technology, and multimodal data analysis. This study not only summarizes the existing research achievements but also puts forward targeted suggestions for future development trends in the field of construction safety management from a practical perspective, aiming to promote the in-depth application and development of human–computer interaction technology in construction safety management.
... The overarching question (RQ1) seeks to provide a holistic view of HDI research, which [2] is noted as a rapidly evolving field intersecting various disciplines. Its sub-questions delve into specific aspects: quantifying research output (RQ1.1) to gauge the field's growth and maturity [1]; compiling HDI definitions (RQ1.2) to understand how researchers conceptualize the field [3]; mapping knowledge areas (RQ1.3) to identify the field's scope and potential for cross-disciplinary collaboration [17]; identifying key topics (RQ1.4) to understand current focus areas and potential gaps [18]; analyzing publication channels (RQ1.5) to provide insight into the field's academic positioning [19]; examining research Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
... Building on this foundational concept, [18] expanded the definition to include "human manipulation, analysis and sensemaking of large, unstructured, and complex datasets." This broader perspective encompasses various aspects such as personal data, unstructured data sets, transparency, trust, privacy, and environments of embodied interaction. ...
... 1. Data visualization: design and use of visual representations of data to communicate information effectively to humans [18]; 2. Data literacy: This topic covers the knowledge, skills, and attitudes needed for individuals to effectively interact with and make decisions based on data [19]; 3. Data ethics: ethical considerations related to the collection, storage, processing, and use of data, including issues related to privacy, consent, and fairness [2, 3, 20, 24, 26]; 4. Human factors in data systems: design of data systems and interfaces that are user-friendly, intuitive, and effective in communicating information to humans; 5. Data-driven decision-making: use of data to support decision-making processes, including how to effectively present data to decision-makers [2]; 6. Human-robot interaction: interaction between humans and robots that are designed to collect, process, or present data [23,35]; 7. Human-AI interaction: interaction between humans and artificial intelligence systems that are designed to collect, process, or present data [38]; 8. Data privacy and security: protection of sensitive data from unauthorized access or use, as well as the rights of individuals to control how their data is collected and used. ...
Living in a modern society driven by data underscores the significance of Human-data interaction (HDI). HDI is at the intersection of computer science, statistics, sociology, psychology, and behavioral studies, and is crucial in a landscape where seemingly ’free’ products often capitalize on users as commodities. It is important to ensure robust protection mechanisms for personal data, as the quality and ease of human interaction with the surrounding data shape our knowledge. The research explores HDI to understand how people perceive and interact with data, with the aim of improving decision-making and refining interaction within this context. The text has been improved to adhere to the following characteristics: objectivity, comprehensibility and logical structure, conventional structure, clear and objective language, format, formal register, structure, balance, precise word choice, and grammatical correctness. The primary goal is to generate insights that facilitate informed decision-making by understanding human engagement with data. In addition, our goal is to improve interaction within the context of HDI. To establish a knowledge foundation, we conducted a systematic review of HDI research over the past decade, consolidating essential knowledge. The article outlines the introduction of HDI, details the methodology of the systematic review, and presents the results obtained. The systematic study provides comprehensive insights into HDI, addressing key research questions. The findings illuminate human interaction with data, contributing to nuanced understandings of information dissemination and user engagement. The results offer a valuable resource for future studies, providing a well-rounded perspective on HDI. This article contributes an introductory exploration of HDI, outlines systematic study methodology, and presents outcomes to answer research questions. The importance of understanding human data interaction for informed decision-making is underscored by the findings. The synthesized knowledge can serve as a foundational stepping stone for future research in the dynamic realm of HDI.
... In that vein, the Electronic Performance Monitoring (EPM) (Lund, 1992;Schleifer and Shell, 1992;Alder, 2001) is a powerful element in evaluating on-site safety, health and productivity (Edirisinghe, 2019;Calvetti, Mêda, et al., 2020). Moreover, human-data interaction (HDI) also needs to be crucially considered to enable EPM deployment, which is characterised by legibility, agency, and negotiability for personal data collection and use (Mortier et al., 2014;Hornung et al., 2015;Victorelli et al., 2020). Therefore, raising awareness of HDI principles in the context of EPM's innovative initiatives in the construction industry is fundamental. ...
... Table 2 summarises the papers from the HDI plus EPM perspective due to their research contributions. (Sivarajah et al., 2013;Hutton and Henderson, 2017;Jones, Sailaja and Kerlin, 2017;Widjojo, Chinthammit and Engelke, 2017;Fu, Steichen and Zhang, 2019;Victorelli et al., 2019) UI (user interface) 3 (Vasuki et al., 2014;Gan et al., 2018;Victorelli and Reis, 2020) Artefact/systems-researchbased 7 (Mashhadi, Kawsar and Acer, 2014;Jones, Sailaja and Kerlin, 2017;Gan et al., 2018;Santos, Salgado and Viterbo, 2018;Fu, Steichen and Zhang, 2019;Victorelli et al., 2019) Empirical/ Experimental 4 (Sivarajah et al., 2013;Vasuki et al., 2014;Brombacher, Houben and Vos, 2022;Calvetti et al., 2022) Workshop proposal 2 (Wolff et al., 2018;Sailaja et al., 2021) Challenges mapping/ Review 1 Position/ Opinion 1 (Doan, 2018) Overall concepts 15 (Mashhadi, Kawsar and Acer, 2014;Hornung et al., 2015;Chowdhury and Dhawan, 2016;Crabtree, 2016;Hutton and Henderson, 2017;Jones, Sailaja and Kerlin, 2017;Widjojo, Chinthammit and Engelke, 2017;Doan, 2018;Santos, Salgado and Viterbo, 2018;Wolff et al., 2018;Fu, Steichen and Zhang, 2019;Victorelli and Reis, 2020;Victorelli et al., 2020;Sailaja et al., 2021) A few studies personated subjects that are often suitable for monitoring, such as people, materials, or machines. There were studies targetting specific construction workers (White/Blue-collar), Geophysicists or Geologists, Designers, Facility managers, and general Office workers. ...
... Table 2 summarises the papers from the HDI plus EPM perspective due to their research contributions. (Sivarajah et al., 2013;Hutton and Henderson, 2017;Jones, Sailaja and Kerlin, 2017;Widjojo, Chinthammit and Engelke, 2017;Fu, Steichen and Zhang, 2019;Victorelli et al., 2019) UI (user interface) 3 (Vasuki et al., 2014;Gan et al., 2018;Victorelli and Reis, 2020) Artefact/systems-researchbased 7 (Mashhadi, Kawsar and Acer, 2014;Jones, Sailaja and Kerlin, 2017;Gan et al., 2018;Santos, Salgado and Viterbo, 2018;Fu, Steichen and Zhang, 2019;Victorelli et al., 2019) Empirical/ Experimental 4 (Sivarajah et al., 2013;Vasuki et al., 2014;Brombacher, Houben and Vos, 2022;Calvetti et al., 2022) Workshop proposal 2 (Wolff et al., 2018;Sailaja et al., 2021) Challenges mapping/ Review 1 Position/ Opinion 1 (Doan, 2018) Overall concepts 15 (Mashhadi, Kawsar and Acer, 2014;Hornung et al., 2015;Chowdhury and Dhawan, 2016;Crabtree, 2016;Hutton and Henderson, 2017;Jones, Sailaja and Kerlin, 2017;Widjojo, Chinthammit and Engelke, 2017;Doan, 2018;Santos, Salgado and Viterbo, 2018;Wolff et al., 2018;Fu, Steichen and Zhang, 2019;Victorelli and Reis, 2020;Victorelli et al., 2020;Sailaja et al., 2021) A few studies personated subjects that are often suitable for monitoring, such as people, materials, or machines. There were studies targetting specific construction workers (White/Blue-collar), Geophysicists or Geologists, Designers, Facility managers, and general Office workers. ...
Human-Data Interaction (HDI) revolves around how humans generate, process, and utilise data. HDI plays a crucial role in evaluating data collection and use in the context of the construction industry, considering the impact on stakeholders such as site managers and labourers. One significant application of HDI is in on-site Electronic Performance Monitoring (EPM), which aims to leverage workplace innovations to enhance productivity, safety, and health. However, the integration and implications of HDI and EPM lack comprehensive understanding. This research seeks to bridge this knowledge gap by presenting a human-data perspective on sensored construction sites, emphasising the challenges and opportunities for driving innovative EPM initiatives. Through a combination of literature review, surveys with HDI experts, and the authors' perspectives and abduction, conceptual frameworks are developed that cluster HDI and EPM. The study's implications are multifaceted, impacting both theoretical understanding and practical applications. The findings highlight the key actors and the data they generate and manipulate across different platforms during EPM deployment. Through the lens of explanatory theories, sociomateriality, and work sociology, the research contributes to understanding the fragmented nature of HDI and EPM as a managerial issue embedded in the work environment. It sheds light on the interactions of actors using digital EPM devices and relevant data streams influenced by the limited agency of specific stakeholders, such as labourers, and the potential neglect of factors related to their well-being. This research distinguishes itself by focusing on the less explored intersection of HDI and EPM in the construction industry. It offers a novel perspective by considering the sensored environment of construction sites as a venue for analysing human-data interactions.
... [14] The core purpose of HDI is to design, investigate, and assess systems that enhance data interactions, rendering them more intuitive and impactful. The key aspects of HDI can be enumerated as follows: [15] • Understanding Data Interaction: Examining how people perceive, interpret, and utilise data in various contexts, including the cognitive and behavioural aspects of data engagement [4,16]. • Effective Human-Data Interfaces: Developing innovative interfaces, visualizations, and interaction modalities that improve data accessibility, comprehension, and usability [17]. ...
... • Effective Human-Data Interfaces: Developing innovative interfaces, visualizations, and interaction modalities that improve data accessibility, comprehension, and usability [17]. • Designing for User Empowerment: Creating systems that empower users to actively participate in data-driven decision-making and governance, promoting transparency and user agency [16,18] . • Ethical Considerations: Addressing the ethical implications of data use, including issues of privacy, bias, and the societal impact of data-driven technologies [19]. ...
... The existing literature highlights the need for comprehensive HDI frameworks that can guide the design and evaluation of data-intensive systems, addressing crucial considerations such as data agency, legibility, and negotiability. [16,73] The findings from our investigations into DV use cases across multiple domains provide valuable insights that can inform the development of a holistic HDI framework. ...
The rapid technological progress has ushered in a new era of human-computer interaction, where the distinction between the physical and virtual realms is becoming increasingly blurred. This research paper explores the profound and multifaceted intersection of Human-Data Interaction (HDI) and Data Virtualization (DV), examining how emerging technologies can significantly enhance the exploration, comprehension, and utilization of complex, multidimensional data sets. Informed by the insights gleaned from prior research in this domain , the present study delves into the potential of DV techniques to improve HDI, with a particular focus on three experimental investigations conducted within the realms of education, healthcare, and retail. The findings reveal the benefits and potential challenges associated with the implementation of DV in these diverse contexts, offering valuable guidance for the design and development of future HDI systems. Drawing upon a diverse array of authoritative sources, this paper presents a holistic, forward-looking perspective on the future of HDI, underscoring the critical role that DV will play in shaping the next generation of human-computer interfaces and facilitating a deeper, more intuitive understanding of the digital world. Furthermore, the paper presents a preliminary framework for integrating HDI principles into standard design practices. This framework outlines key considerations and guidelines to help designers and developers incorporate HDI techniques more effectively into the development of data-driven applications and interfaces.The proposed framework outlines key considerations for enhancing data accessibility and comprehension, empowering users to exercise greater control over their data, and cultivating transparent dialogues between data providers and end-users. By establishing this conceptual foundation, the paper aims to facilitate the seamless integration of HDI principles into standard design practices, ultimately leading to more intuitive, user-centric, and ethically-grounded approaches to data interaction and utilization.
... This study relies on the existing knowledge from previous investigations represented by design guidelines. We use a set of design guidelines for HDI in information visualization [Victorelli and Reis, 2020]. We explored the feasibility of adopting a set of previously selected guidelines, and a set of categories of interaction [Yi et al., 2007] to facilitate the work of inexperienced designers. ...
... We started this study by proposing evolutions for the HDI design process. Then, we prepared materials to facilitate the understanding of the HDI design guidelines set [Victorelli and Reis, 2020] and data interaction categories [Yi et al., 2007]. We presented the guidelines to the study participants (undergraduate students), who explored the recommendations on existing websites to consolidate learning. ...
... Several sources provide recommendations that can represent guidelines to help designers to conceive the interaction with IV applications. A set of design guidelines for HDI brings gathers recommendations scattered in the literature [Victorelli and Reis, 2020]. The set gathers classical guidelines for HCI usability [Nielsen, 1994a]; principles for User-Centered Design [Norman and Draper, 1986]; specific design recommendations for interaction with visualizations [Baldonado et al., 2000;Elmqvist et al., 2011;Endert, 2014]; and requirements for HDI design [Victorelli et al., 2020a]. ...
Nowadays, voluminous data support may influence decision-making. People with varied profiles need to interact with data to gain valuable insights. There is a need for software tools to support the understanding and management of information to favor Human-Data Interaction (HDI) with a richer user experience. This study explores the combination of HDI design guidelines and participatory approaches to improve user experience in data interaction. We defined a design process to support the activities and adapted participatory practices to facilitate HDI design. We conducted workshops with inexperienced designers developing information visualization applications for common-sense domains. They generated and analyzed several application prototypes. Results suggest that design guidelines help generate HDI-based prototypes with a good user experience.
... In addition, overviews reduce the cognitive effort for interpretation and aid user navigation through the data space [Hornbaek and Hertzum, 2011]. The Human-Data Interaction Design Guidelines of Victorelli and Reis [2020] recommend reducing the information density and gradually revealing the data. ...
Este trabalho tem como objetivo apresentar resultados parciais de um mapeamento sistemático da literatura de visualizações de dados sobre UX. A partir dos 57 artigos selecionados em nosso estudo, a análise preliminar revelou 32 tipos de gráficos, 10 fontes de dados usadas para construir as visualizações e 5 propósitos principais de uso das visualizações.
... We defined a novel module for assessing the experience in data analysis and proposed statements for UX evaluation. The module involves aspects related to principles of HDI [27], categories for user data interaction [42], and guidelines for data interaction design [40]. ...
... The HDI module encompasses aspects concerned with the interaction with data. The core principles of HDI [27] combined with the categories of interaction [42] and design guidelines for HDI [40] are elements that can help to understand users' needs during the design of data-driven applications. In our understanding, memorability, engagement, and enjoyment are essential to interacting with data. ...
... We draw upon the literature related to the new dimensions as sources of information and motivations for the first version of statements. The core principles of HDI [27] combined with the categories of interaction in IV systems [42] and design guidelines for HDI [40] inspired the generation of a draft set of statements. 2. The draft statements were classified to help identify redundancies and gaps in the sets. ...
Understanding how people interact with data and why users prefer one system over another is necessary to develop successful solutions for information visualization. Evaluating the users’ experience with applications based on interaction with data is challenging due to the several parameters involved. It requires evaluating the experience offered by the system, affected data, and the analysis process supported. Existing measurement tools do not take all of these aspects into account. This study investigated the gaps in an existing user experience evaluation tool regarding the interaction with data. A thematic analysis identified the missing themes and grounded our definition of evaluation dimensions. A combination of literature review and designer collaboration resulted in the evaluation statements. Our contribution consists of a questionnaire to support evaluating specific aspects of experience and relevant dimensions for measuring the interaction with data in information visualization systems.KeywordsUser experienceEvaluationInformation visualizationHuman-data interaction
... Porém, no âmbito da representação gráfica não há propostas de automação para a Linguagem, sendo essa a principal contribuição desta pesquisa. Já na área da visualização em si, há trabalhos que apoiam partes do processo de construção de uma visualização fornecendo diretrizes para o desenvolvimento de sistemas (Victorelli;Reis, 2020). Contudo, esta pesquisa promove uma ferramenta que além de fornecer funções para criação simples e intuitiva, realiza uma análise e recomendações que permitam criar visualizações mais compreensíveis. ...
... Porém, no âmbito da representação gráfica não há propostas de automação para a Linguagem, sendo essa a principal contribuição desta pesquisa. Já na área da visualização em si, há trabalhos que apoiam partes do processo de construção de uma visualização fornecendo diretrizes para o desenvolvimento de sistemas (Victorelli;Reis, 2020). Contudo, esta pesquisa promove uma ferramenta que além de fornecer funções para criação simples e intuitiva, realiza uma análise e recomendações que permitam criar visualizações mais compreensíveis. ...
Neste trabalho, apresentamos a ferramenta Chart Lab para apoiar a criação de gráficos mais fáceis de serem entendidos principalmente por pessoas com baixo letramento. O sistema se baseia em regras da literatura da área de visualizações de dados e de práticas da técnica de Linguagem Simples. A partir de recomendações de alterações de componentes do gráfico são promovidas mudanças para melhoria na compreensão do leitor.
... According to the findings obtained from the focus group, one of the most valuable features of EDVL is being able to create charts and graphs directly from queries to databases and being able to interact with them. Literature corroborates that HDI must follow some guidelines, 17 such as maximizing direct manipulation of data and minimizing information overload. The fact that EDVL enables users to create and interact with charts and graphs, along with changing parameters in code chunks, has been perceived by the focus group as promising for training and learning. ...
Educational Data Virtual Lab is an open-source platform for data exploration and analysis that combines the power of a coding environment, the convenience of an interactive visualization engine and the infrastructure needed to handle the complete data lifecycle. Based on the building blocks of the FIWARE European platform and Apache Zeppelin, this tool allows domain experts to become acquainted with data science methods using the data available within their own organization, ensuring that the skills they acquire are relevant to their field and driven by their own professional goals. We used EDVL in a pilot study in which we carried out a focus group within a multinational company to gain insight into potential users' perceptions of EDVL, both from the educational and operational point of view. The results of our evaluation suggest that EDVL holds a great potential to train the workforce in data science skills and to enable collaboration among professionals with different levels of expertise.
