Knowledge at First Glance: A Model for a Data Visualization Recommender System Suited for Non-expert Users

Kubernátová, Petra; Friedjungová, Magda; Duijn, Max J. van

doi:10.5220/0006851302080219

Cited by 1 publication

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“…However, the answer to the specified question could also be answered with a scatter plot as suggested by Voyager. This is because networks can also be represented as adjacency matrices and the scatter plot generated by Voyager is Table 1: Results of the competitiveness test, our model is labeled NEViM [2] essentially an adjacency matrix. Data set 7 and its question were aimed at visualizing distributions.…”

Section: Resultsmentioning

confidence: 99%

“…Thankfully, data visualization recommender systems exist to help us with this complicated task. This paper is an extended version of [2] which was presented at the DATA 2018 conference. The main contribution of this extended version lies in including a step-by-step guide for building a data visualization recommender system.…”

Section: Introductionmentioning

confidence: 99%

“…Example of a Sankey diagram showing the distribution of energy in a filament lamp[30] Two possible paths to reach a Sankey diagram (left: task-based, right: data-based)[2] …”

mentioning

confidence: 99%

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Constructing a Data Visualization Recommender System

Kubernátová

Friedjungová

Duijn

2019

Communications in Computer and Information Science

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Choosing a suitable visualization for data is a difficult task. Current data visualization recommender systems exist to aid in choosing a visualization, yet suffer from issues such as low accessibility and indecisiveness. In this study, we first define a step-by-step guide on how to build a data visualization recommender system. We then use this guide to create a model for a data visualization recommender system for non-experts that aims to resolve the issues of current solutions. The result is a questionbased model that uses a decision tree and a data visualization classification hierarchy in order to recommend a visualization. Furthermore, it incorporates both task-driven and data characteristicsdriven perspectives, whereas existing solutions seem to either convolute these or focus on one of the two exclusively. Based on testing against existing solutions, it is shown that the new model reaches similar results while being simpler, clearer, more versatile, extendable and transparent. The presented guide can be used as a manual for anyone building a data visualization recommender system. The resulting model can be applied in the development of new data visualization software or as part of a learning tool. Figure 1: The data science process [3]and also elaborate on ways of evaluating and implementing it. Section 2 places data visualization recommender systems in the context of data science. Section 3 introduces our step-by-step guide to building a data visualization recommender system. In Sections 4-10 we go through the individual steps and build our very own data visualization recommender system while taking measures to make it well suited for non-expert users. We define a 'non-expert user' as someone without professional or specialized knowledge of data visualization. We thus include both complete beginners and users who have general knowledge of data visualization types (e.g. bar charts, pie charts, scatter plots) but have no professional experience in the fields of data science and data communication. We want to see if we can make adjustments that make a system more suitable for non-expert users while maintaining effectiveness (still clearly distinguishing the data visualizations from each other) and performance (recommending the most suitable visualization type). We draw conclusions in Section 11 and set an agenda for future work in Section 12. Context Data scienceData science plays an important role in scientific research, as it aids us in collecting, organizing, and interpreting data, so that it can be transformed into valuable knowledge. Figure 1 shows a simplified diagram of the data science process as described by O‚Neil and Schutt [3]. This diagram is helpful in demarcating the research objectives of this paper. According to O‚Neil and Schutt, first, real world raw data is collected, processed and cleaned through a process called data munging. Then exploratory data analysis (EDA) follows, during which we might find that we need to collect more data or dedicate more time to cleaning and organizing the cu...

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%