Subjective information visualizations

Information Visualizations (InfoViz) are systems that require high levels of cognitive processing. They revolve around the notion of decoding and interpreting visual patterns in order to achieve certain goals. We argue that purely designing for the visual will not allow for optimum experiences since there is more to InfoViz than just the visual. Interaction is a key to achieving higher levels of knowledge. In this position paper we present a different perspective on the underlying meaning of interaction, where we describe it as incorporating both the visual and the physical activities. By physical activities we mean the physical actions upon the physical input device/s. We argue that interaction is the key element for supporting users’ subjective experiences hence these experiences should first be understood. All the discussions in this paper are based upon on going work in the field of visualizing the literature knowledge domain (LKDViz)

InfoVis experience enhancement through mediated interaction

Information visualization is an experience in which both the aesthetic representations and interaction are part. Such an experience can be augmented through close consideration of its major components. Interaction is crucial to the experience, yet it has seldom been adequately explored in the field. We claim that direct mediated interaction can augment such an experience. This paper discusses the reasons behind such a claim and proposes a mediated interactive manipulation scheme based on the notion of directness. It also describes the ways in which such a claim will be validated. The Literature Knowledge Domain (LKD) is used as the concrete domain around which the discussions will be held

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

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

Human-Centered Approaches in Geovisualization Design: Investigating Multiple Methods Through a Long-Term Case Study

Working with three domain specialists we investigate human-centered approaches to geovisualization following an ISO13407 taxonomy covering context of use, requirements and early stages of design. Our case study, undertaken over three years, draws attention to repeating trends: that generic approaches fail to elicit adequate requirements for geovis application design; that the use of real data is key to understanding needs and possibilities; that trust and knowledge must be built and developed with collaborators. These processes take time but modified human-centred approaches can be effective. A scenario developed through contextual inquiry but supplemented with domain data and graphics is useful to geovis designers. Wireframe, paper and digital prototypes enable successful communication between specialist and geovis domains when incorporating real and interesting data, prompting exploratory behaviour and eliciting previously unconsidered requirements. Paper prototypes are particularly successful at eliciting suggestions, especially for novel visualization. Enabling specialists to explore their data freely with a digital prototype is as effective as using a structured task protocol and is easier to administer. Autoethnography has potential for framing the design process. We conclude that a common understanding of context of use, domain data and visualization possibilities are essential to successful geovis design and develop as this progresses. HC approaches can make a significant contribution here. However, modified approaches, applied with flexibility, are most promising. We advise early, collaborative engagement with data – through simple, transient visual artefacts supported by data sketches and existing designs – before moving to successively more sophisticated data wireframes and data prototypes

Creative User-Centered Visualization Design for Energy Analysts and Modelers

We enhance a user-centered design process with techniques that deliberately promote creativity to identify opportunities for the visualization of data generated by a major energy supplier. Visualization prototypes developed in this way prove effective in a situation whereby data sets are largely unknown and requirements open – enabling successful exploration of possibilities for visualization in Smart Home data analysis. The process gives rise to novel designs and design metaphors including data sculpting. It suggests: that the deliberate use of creativity techniques with data stakeholders is likely to contribute to successful, novel and effective solutions; that being explicit about creativity may contribute to designers developing creative solutions; that using creativity techniques early in the design process may result in a creative approach persisting throughout the process. The work constitutes the first systematic visualization design for a data rich source that will be increasingly important to energy suppliers and consumers as Smart Meter technology is widely deployed. It is novel in explicitly employing creativity techniques at the requirements stage of visualization design and development, paving the way for further use and study of creativity methods in visualization design

Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning

[EN] Data analysis is a key process to foster knowledge generation in particular domains or fields of study. With a strong informative foundation derived from the analysis of collected data, decision-makers can make strategic choices with the aim of obtaining valuable benefits in their specific areas of action. However, given the steady growth of data volumes, data analysis needs to rely on powerful tools to enable knowledge extraction. Information dashboards offer a software solution to analyze large volumes of data visually to identify patterns and relations and make decisions according to the presented information. But decision-makers may have different goals and, consequently, different necessities regarding their dashboards. Moreover, the variety of data sources, structures, and domains can hamper the design and implementation of these tools. This Ph.D. Thesis tackles the challenge of improving the development process of information dashboards and data visualizations while enhancing their quality and features in terms of personalization, usability, and flexibility, among others. Several research activities have been carried out to support this thesis. First, a systematic literature mapping and review was performed to analyze different methodologies and solutions related to the automatic generation of tailored information dashboards. The outcomes of the review led to the selection of a modeldriven approach in combination with the software product line paradigm to deal with the automatic generation of information dashboards. In this context, a meta-model was developed following a domain engineering approach. This meta-model represents the skeleton of information dashboards and data visualizations through the abstraction of their components and features and has been the backbone of the subsequent generative pipeline of these tools. The meta-model and generative pipeline have been tested through their integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully integrated with other meta-model to support knowledge generation in learning ecosystems, and as a framework to conceptualize and instantiate information dashboards in different domains. In terms of the practical applications, the focus has been put on how to transform the meta-model into an instance adapted to a specific context, and how to finally transform this later model into code, i.e., the final, functional product. These practical scenarios involved the automatic generation of dashboards in the context of a Ph.D. Programme, the application of Artificial Intelligence algorithms in the process, and the development of a graphical instantiation platform that combines the meta-model and the generative pipeline into a visual generation system. Finally, different case studies have been conducted in the employment and employability, health, and education domains. The number of applications of the meta-model in theoretical and practical dimensions and domains is also a result itself. Every outcome associated to this thesis is driven by the dashboard meta-model, which also proves its versatility and flexibility when it comes to conceptualize, generate, and capture knowledge related to dashboards and data visualizations

Designing Interactive Explainable AI Systems for Lay Users

Explainability considered a critical component of trustworthy artificial intelligence (AI) systems, has been proposed to address AI systems’ lack of transparency by revealing the reasons behind their decisions to lay users. However, most explainability methods developed so far provide static explanations that limit the information conveyed to lay users resulting in an insufficient understanding of how AI systems make decisions. To address this challenge and support the efforts to improve the transparency of AI systems, we conducted a design science research project to design an interactive explainable artificial intelligence (XAI) system to help lay users understand AI systems’ decisions. We relied on existing knowledge in the XAI literature to propose design principles and instantiate them in an initial prototype. We then conducted an evaluation of the prototype and interviews with lay users. Our research contributes design knowledge for interactive XAI systems and provides practical guidelines for practitioners