6,224 research outputs found

    You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems

    Full text link
    Visual query systems (VQSs) empower users to interactively search for line charts with desired visual patterns, typically specified using intuitive sketch-based interfaces. Despite decades of past work on VQSs, these efforts have not translated to adoption in practice, possibly because VQSs are largely evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we collaborated with experts from three diverse domains---astronomy, genetics, and material science---via a year-long user-centered design process to develop a VQS that supports their workflow and analytical needs, and evaluate how VQSs can be used in practice. Our study results reveal that ad-hoc sketch-only querying is not as commonly used as prior work suggests, since analysts are often unable to precisely express their patterns of interest. In addition, we characterize three essential sensemaking processes supported by our enhanced VQS. We discover that participants employ all three processes, but in different proportions, depending on the analytical needs in each domain. Our findings suggest that all three sensemaking processes must be integrated in order to make future VQSs useful for a wide range of analytical inquiries.Comment: Accepted for presentation at IEEE VAST 2019, to be held October 20-25 in Vancouver, Canada. Paper will also be published in a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG) IEEE VIS (InfoVis/VAST/SciVis) 2019 ACM 2012 CCS - Human-centered computing, Visualization, Visualization design and evaluation method

    Group vs Individual: Impact of TOUCH and TILT Cross-Device Interactions on Mixed-Focus Collaboration

    Get PDF
    Cross-device environments (XDEs) have been devel-oped to support a multitude of collaborative activities. Yet, little is known about how different cross-device in-teraction techniques impact group collaboration; in-cluding their impact on independent and joint work that often occur during group work. In this work, we explore the impact of two XDE data browsing tech-niques: TOUCH and TILT. Through a mixed-methods study of a collaborative sensemaking task, we show that TOUCH and TILT have distinct impacts on how groups accomplish, and shift between, independent and joint work. Finally, we reflect on these findings and how they can more generally inform the design of XDEs.NSER

    Revisiting leadership development:the participant perspective

    Get PDF
    Purpose – The purpose of this paper is to address limitations of prevailing approaches to leadership development programmes and make suggestions as to how these might be overcome. These limitations are an outcome of the dominant rational functional approach to leadership development programmes. Based on empirical research, and underpinned by organisational theory, the paper suggests a shift towards a socio-constructivist perspective on design and implementation of leadership development programmes. The explorative study proposes that context and participant differences need to be recognised as factors impacting on the effectiveness of leadership development initiatives. Design/methodology/approach – The paper is based on a review of relevant literature and qualitative data collected using the case study method. The study presented is explorative. Findings – The paper finds that participant interaction with leadership development programmes varies depending on individual and/or contextual factors. Current design logic neither recognises nor utilises such situatedness as programmes develop their linear and unidirectional logic. Designers of programmes underestimate the extent to which programme participants create a context-specific understanding of leadership learning as they interact with the programme. Their personal and organisational context shapes this interaction. A socio-constructivist perspective can provide theoretical foundation for the argument that leadership development programmes can become more effective if context-specific dimensions are recognised as shaping and constraining factors impacting on programme participants. Originality/value – The paper argues that it is time to move away from offering leadership development programmes which emphasise input over interaction. The paper encourages commissioners and designers of leadership programmes to recognise that an overly didactic approach may limit the effectiveness of such programmes

    Evaluation methodology for visual analytics software

    Get PDF
    O desafio do Visual Analytics (VA) é produzir visualizaçÔes que ajudem os utilizadores a concentrarem-se no aspecto mais relevante ou mais interessante dos dados apresentados. A sociedade actual enfrenta uma quantidade de dados que aumenta rapidamente. Assim, os utilizadores de informação em todos os domínios acabam por ter mais informação do que aquela com que podem lidar. O software VA deve suportar interacçÔes intuitivas para que os analistas possam concentrar-se na informação que estão a manipular, e não na técnica de manipulação em si. Os ambientes de VA devem procurar minimizar a carga de trabalho cognitivo global dos seus utilizadores, porque se tivermos de pensar menos nas interacçÔes em si, teremos mais tempo para pensar na anålise propriamente dita. Tendo em conta os benefícios que as aplicaçÔes VA podem trazer e a confusão que ainda existe ao identificar tais aplicaçÔes no mercado, propomos neste trabalho uma nova metodologia de avaliação baseada em heurísticas. A nossa metodologia destina-se a avaliar aplicaçÔes através de testes de usabilidade considerando as funcionalidades e características desejåveis em sistemas de VA. No entanto, devido à sua natureza quatitativa, pode ser naturalmente utilizada para outros fins, tais como comparação para decisão entre aplicaçÔes de VA do mesmo contexto. Além disso, seus critérios poderão servir como fonte de informação para designers e programadores fazerem escolhas apropriadas durante a concepção e desenvolvimento de sistemas de VA

    Collaborative Human-Computer Interaction with Big Wall Displays - BigWallHCI 2013 3rd JRC ECML Crisis Management Technology Workshop

    Get PDF
    The 3rd JRC ECML Crisis Management Technology Workshop on Human-Computer Interaction with Big Wall Displays in Situation Rooms and Monitoring Centres was co-organised by the European Commission Joint Research Centre and the University of Applied Sciences St. Pölten, Austria. It took place in the European Crisis Management Laboratory (ECML) of the JRC in Ispra, Italy, from 18 to 19 April 2013. 40 participants from stakeholders in the EC, civil protection bodies, academia, and industry attended the workshop. The hardware of large display areas is on the one hand mature since many years and on the other hand changing rapidly and improving constantly. This high pace developments promise amazing new setups with respect to e.g., pixel density or touch interaction. On the software side there are two components with room for improvement: 1. the software provided by the display manufacturers to operate their video walls (source selection, windowing system, layout control) and 2. dedicated ICT systems developed to the very needs of crisis management practitioners and monitoring centre operators. While industry starts to focus more on the collaborative aspects of their operating software already, the customized and tailored ICT applications needed are still missing, unsatisfactory, or very expensive since they have to be developed from scratch many times. Main challenges identified to enhance big wall display systems in crisis management and situation monitoring contexts include: 1. Interaction: Overcome static layouts and/or passive information consumption. 2. Participatory Design & Development: Software needs to meet users’ needs. 3. Development and/or application of Information Visualisation & Visual Analytics principle to support the transition from data to information to knowledge. 4. Information Overload: Proper methods for attention management, automatic interpretation, incident detection, and alarm triggering are needed to deal with the ever growing amount of data to be analysed.JRC.G.2-Global security and crisis managemen

    Supporting the sensemaking process in visual analytics

    Get PDF
    Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. It involves interactive exploration of data using visualizations and automated data analysis to gain insight, and to ultimately make better decisions. It aims to support the sensemaking process in which information is collected, organized and analyzed to form new knowledge and inform further action. Interactive visual exploration of the data can lead to many discoveries in terms of relations, patterns, outliers and so on. It is difficult for the human working memory to keep track of all findings during a visual analysis. Also, synthesis of many different findings and relations between those findings increase the information overload and thereby hinders the sensemaking process further. The central theme of this dissertation is How to support users in their sensemaking process during interactive exploration of data? To support the sensemaking process in visual analytics, we mainly focus on how to support users to capture, reuse, review, share, and present the key aspects of interest concerning the analysis process and the findings during interactive exploration of data. For this, we have developed generic models and tools that enable users to capture findings with provenance, and construct arguments; and to review, revise and share their visual analysis. First, we present a sensemaking framework for visual analytics that contains three linked views: a data view, a navigation view and a knowledge view for supporting the sense-making process. The data view offers interactive data visualization tools. The navigation view automatically captures the interaction history using a semantically rich action model and provides an overview of the analysis structure. The knowledge view is a basic graphics editor that helps users to record findings with provenance and to organize findings into claims using diagramming techniques. Users can exploit automatically captured interaction history and manually recorded findings to review and revise their visual analysis. Thus, the analysis process can be archived and shared with others for collaborative visual analysis. Secondly, we enable analysts to capture data selections as semantic zones during an analysis, and to reuse these zones on different subsets of data. We present a Select & Slice table that helps analysts to capture, manipulate, and reuse these zones more explicitly during exploratory data analysis. Users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data slices. Finally, exploration overviews and searching techniques based on keywords, content similarity, and context helped analysts to develop awareness over the key aspects of the exploration concerning the analysis process and findings. On one hand, they can proactively search analysis processes and findings for reviewing purposes. On the other hand, they can use the system to discover implicit connections between findings and the current line of inquiry, and recommend these related findings during an interactive data exploration. We implemented the models and tools described in this dissertation in Aruvi and HARVEST. Using Aruvi and HARVEST, we studied the implications of these models on a user’s sensemaking process. We adopted the short-term and long-term case studies approach to study support offered by these tools for the sensemaking process. The observations of the case studies were used to evaluate the models

    A student-facing dashboard for supporting sensemaking about the brainstorm process at a multi-surface space

    Full text link
    © 2017 Association for Computing Machinery. All rights reserved. We developed a student-facing dashboard tuned to support posthoc sensemaking in terms of participation and group effects in the context of collocated brainstorming. Grounding on foundations of small-group collaboration, open learner modelling and brainstorming at large interactive displays, we designed a set of models from behavioural data that can be visually presented to students. We validated the effectiveness of our dashboard in provoking group reflection by addressing two questions: (1) What do group members gain from studying measures of egalitarian contribution? and (2) What do group members gain from modelling how they sparked ideas off each other? We report on outcomes from a study with higher education students performing brainstorming. We present evidence from i) descriptive quantitative usage patterns; and ii) qualitative experiential descriptions reported by the students. We conclude the paper with a discussion that can be useful for the community in the design of collective reflection systems

    Large displays and tablets:Data exploration and its effects on data collection

    Get PDF
    Data is pivotal to open government initiatives, where citizens are often expected to be informed and actively participate. Yet, it can be difficult for people to understand the meaning of data. Presenting data to the public in an appropriate way may also increase citizen's willingness to participate in data collection. Here we present a study which explores how large screens can support socially relevant data exploration. In a between subject laboratory experiment, we analysed how pairs of participants explored data visualisations on a high-resolution display (LHRD) and a tablet. Our results indicate that LHRDs are less cognitively demanding, while tablets offer more shared control of the interface. Data exploration had limited effect on increasing comfort with sharing personal data but helped increase perceptions of trustworthiness within the data collection process. We observed that appropriately visualised data on either platform has significant potential to increase the public's understanding of large data sets
    • 

    corecore