Exploration Views: Understanding Dashboard Creation and Customization for Visualization Novices

Elias, Micheline; Bezerianos, Anastasia

doi:10.1007/978-3-642-23768-3_23

Cited by 43 publications

(34 citation statements)

References 14 publications

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“…Similar efforts have been made in the field of Visual Analytics and Information Visualisation to empower laymen with specialist skills through simplification and modularity: Ren et al present a toolkit for the rapid prototyping of Information Visualisation applications by allowing users to select matching visualisations for their data from a library of widgets [8], as do Elias and Bezerianos [9]. Similarly, Howe et al [10] provide a library of visual elements to "assist scientists and researchers in creating interactive visual dashboard applications in seconds with no programming necessary" [10].…”

Section: Visual Languages and Visualisation Toolkitsmentioning

confidence: 99%

“…Other good examples of how existing applications and frameworks for data visualisation can benefit from our visual guide are VizDeck [10], Exploration Views [9], and Voyager [11]. All three allow novices to transform their data into powerful visualisations for presentation and extended analysis, but lack uncertainty depiction for algorithms.…”

Section: Application In Framework and Toolkits For Information Visuamentioning

confidence: 99%

“…While these users are experts in their own domain, they may be laymen in the field of data science. To help researchers who lack such skills, a variety of frameworks and toolkits exists [8][9][10][11]. These either automatically choose the right presentation for a certain type of data, or allow users to build their own visualisations using a library of widgets.…”

Section: Introductionmentioning

confidence: 99%

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Towards a visual guide for communicating uncertainty in Visual Analytics

Seipp

Gutiérrez

Ochôa

et al. 2019

Journal of Computer Languages

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This article presents a first step towards the definition of a visual guide for communicating uncertainty which is to fit into existing visualisation frameworks and toolkits. The first entry in our guide is made by a set of visual variables appropriate for representing areal uncertainty in algorithm mechanics. Such visualisations show users how data points are distributed in the classification space and allow them to understand the "goodness-of-fit" of their data to the algorithm. This is important for Visual Analytics applications, which combine Information Visualisation with information mining techniques in an interactive decision-making process. Model uncertainties stemming from widely spread data points need to be visualised so that the user can make adjustments and improve the analysis. To capitalise on established knowledge and meaning, we explore whether popular visual variables for representing areal uncertainty in the domain of geospatial visualisation may also be effective for representing uncertainty in the visualisation of the mechanics of K-means clustering and Linear Regression algorithms, as both use a spatial distribution of data points. In a study with 500 participants we find that overall the visual means opacity performs best, followed by texture, but that grid and blur may be unsuitable for quantifying uncertainty. The performance of contour lines appears to depend on the algorithm visualisation. Using this study, we extend the validity of a set of domain-specific findings from geospatial visualisation to the visualisation of algorithm mechanics and use these to form the first building blocks of a cross-disciplinary visual guide for representing uncertainty, laying promising foundations for future work.

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Section: Visual Languages and Visualisation Toolkitsmentioning

confidence: 99%

Section: Application In Framework and Toolkits For Information Visuamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards a visual guide for communicating uncertainty in Visual Analytics

Seipp

Gutiérrez

Ochôa

et al. 2019

Journal of Computer Languages

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“…Regarding customizable and/or personalized dashboards, it can be seen that Business Intelligence (BI) is the most common application domain (Figure 3), followed by the Internet of Things (IoT), Learning Analytics (LA), services monitoring and social science domains. IoT 2 [16] [18] Learning Analytics 2 [22] [24] Services monitoring 2 [20] [26] Disaster situations 1 [30] Economics 1 [31] Emergency management 1 [17] Energy monitoring 1 [13] Generic 1 [29] Interface evaluation 1 [28] Microservices monitoring 1 [19] Physics 1 [11] Sensor monitoring 1 [15] Social sciences 1 [27] 4.5 MQ5. Which are the factors that condition the dashboards' variability process?…”

Section: Mq4 To Which Contexts Have Been the Variability Processes Amentioning

confidence: 99%

Tailored information dashboards

Vázquez-Ingelmo

García-Peñalvo

Therón

2019

Proceedings of the XX International Conference on Human Computer Interaction

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Information dashboards are extremely useful tools to exploit knowledge. Dashboards enable users to reach insights and to identify patterns within data at-a-glance. However, dashboards present a series of characteristics and configurations that could not be optimal for every user, thus requiring the modification or variation of its features to fulfill specific user requirements. This variation process is usually referred to as customization, personalization or adaptation, depending on how this variation process is achieved. Given the great number of users and the exponential growth of data sources, tailoring an information dashboard is not a trivial task, as several solutions and configurations could arise. To analyze and understand the current state-of-the-art regarding tailored information dashboards, a systematic mapping has been performed. This mapping focus on answering questions regarding how existing dashboard solutions in the literature manage the customization, personalization and/or adaptation of its elements to produce tailored displays. P O S TGiven the large amount of content customization and/or personalization possibilities, and the potential misconception of those terms, this paper aims to investigate the existing literature regarding the customization, adaptation and/or personalization of information dashboards, focusing on mapping [4] the collected studies to understand the existing solutions and research lines of this area.The remainder of this paper is organized as follows. Section 2 outlines the research method followed to perform the systematic mapping. Section 3 describes the data extraction process for analyzing the collected works. Section 4 presents the results of the systematic mapping, finishing with Section 5 where the results are discussed and Section 6, where the work's conclusions are shared. RESEARCH METHODThis study is based on the guidelines suggested by Kitchenham and Charters [5] for systematic literature studies and the guidelines suggested by Petersen [6] for mapping studies. The mapping process is organized in a series of phases; first, the planning phase, where the main goals and research questions to be answered are defined. Second, the conducting phase, where the search strategy is generated and the selection, assessment and data extraction of the studies are performed. The final stage is the reporting phase, where the results are disseminated. P O S T

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“…El diseño de un dashboard involucra múltiples actores, incluidos los usuarios finales y los analistas del negocio [4]. Si en la etapa de requisitos no se tienen en cuenta los diferentes actores que intervienen, las metas y tareas de cada uno, sus relaciones y los recursos que se necesitan para cumplir una determinada meta o tarea, se corre el riesgo de que el dashboard no cumpla con los propósitos deseados.…”

Section: Introductionunclassified

Modelos de Requisitos Basados en I* para Detectar Proactividad en Dashboards

Acosta

Espino

2014

Lámpsakos

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Resumen. El objetivo del trabajo es presentar modelos para la captura de los requisitos de un dashboard para detectar un comportamiento proactivo. Estos modelos siguen un enfoque orientado hacia metas y fueron creados con el marco de trabajo i*, que toma como base las premisas del modelado social. Para detectar el comportamiento proactivo se usaron patrones basados en modelos de i* para detectar proactividad en la etapa de requisitos de un sistema de software. Los modelos que se obtienen como resultado del trabajo tienen representados los actores, metas, intenciones, tareas y recursos que se necesitan para modelar los requisitos de un dashboard con un comportamiento proactivo y pueden, además, ser utilizados en distintos contextos de negocio. Palabras clave: Dashboards; i*; Modelos de requisitos.Abstract. This paper aims to present models for the capture of the requirements of a dashboard that allow detecting a proactive behavior. These models follow a goals oriented approach and were created using the framework i *, which is based on the premises of the social modeling. In order to detect the proactive behavior patterns based on models of i * that allow detecting proactivity in the stage of requirements of a system of software were used. In the models obtained as a result of the paper were represented the actors, goals, intentions, tasks and resources necessary to model the requirements of a dashboard with a proactive behavior, and these models can be used in addition in different contexts of business.

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Exploration Views: Understanding Dashboard Creation and Customization for Visualization Novices

Cited by 43 publications

References 14 publications

Towards a visual guide for communicating uncertainty in Visual Analytics

Towards a visual guide for communicating uncertainty in Visual Analytics

Tailored information dashboards

Modelos de Requisitos Basados en I* para Detectar Proactividad en Dashboards

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