Creating User-specific Business Process Monitoring Dashboards with a Model-driven Approach

Kintz, Maximilien; Kochanowski, Monika; Koetter, Falko

doi:10.5220/0006135203530361

Cited by 22 publications

(36 citation statements)

References 9 publications

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“…Different approaches have been considered in the literature to automatize this implementation process and to decrease the development time of tailored dashboards [12]. From configuration wizards that allow users to choose the charts of their dashboards (e.g., Tableau, https://www.tableau.com/, or Grafana, https://grafana.com/) to model-driven approaches that render personalized dashboards based on formal descriptions of the domain [13][14][15][16], among other variety of solutions.…”

Section: Introductionmentioning

confidence: 99%

Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards

et al. 2020

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Information dashboards are everywhere. They support knowledge discovery in a huge variety of contexts and domains. Although powerful, these tools can be complex, not only for the end-users but also for developers and designers. Information dashboards encode complex datasets into different visual marks to ease knowledge discovery. Choosing a wrong design could compromise the entire dashboard’s effectiveness, selecting the appropriate encoding or configuration for each potential context, user, or data domain is a crucial task. For these reasons, there is a necessity to automatize the recommendation of visualizations and dashboard configurations to deliver tools adapted to their context. Recommendations can be based on different aspects, such as user characteristics, the data domain, or the goals and tasks that will be achieved or carried out through the visualizations. This work presents a dashboard meta-model that abstracts all these factors and the integration of a visualization task taxonomy to account for the different actions that can be performed with information dashboards. This meta-model has been used to design a domain specific language to specify dashboards requirements in a structured way. The ultimate goal is to obtain a dashboard generation pipeline to deliver dashboards adapted to any context, such as the educational context, in which a lot of data are generated, and there are several actors involved (students, teachers, managers, etc.) that would want to reach different insights regarding their learning performance or learning methodologies.

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Section: Introductionmentioning

confidence: 99%

Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards

et al. 2020

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“…Other solutions involve personalization; in [12], the methodology takes as input a model of the business process and goals to describe and generate a dashboard, so the authors use implicit data (goals) to build a concrete dashboard that would help to reach that input goals. The aforementioned work is extended in [21], where user-roles are taken into account to add more information to the dashboard personalization process. A similar solution is presented in [9], which also takes into account user-roles and business' KPIs to generate a dashboard that fits the business goals.…”

Section: Discussionmentioning

confidence: 99%

“…A few records were published in 2011 [10; 11], 2012 [12], 2013 [13], 2014 [14][15][16] and 2016 [17; 18]. However, the majority of records are distributed between 2017 [19][20][21][22][23][24] and 2018 [25][26][27][28][29][30][31], six and seven papers respectively.…”

Section: Mq1 How Many Studies Were Published Over the Years?mentioning

confidence: 99%

“…The majority of the included papers make use of the user preferences as input to modify the dashboard appearance and functionality (Table 4). Business process 3 [12] [9] [21] User role 2 [21] [9] Design guidelines 2 [25] [28] Usage profiles 1 [14] Data sources 2 [16] [18] Goals 2 [12] [21] User description 1 [24] Analysis scenario 1 [24] User abilities 1 [30]…”

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

confidence: 99%

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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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“…Among the methods used to configure these tools, the software product lines (SPL) paradigm [4,5] and model-driven development (MDD) [6,7] can be found [8][9][10][11][12].…”

Section: P O S Tmentioning

confidence: 99%

Capturing high-level requirements of information dashboards' components through meta-modeling

Vázquez-Ingelmo

García-Peñalvo

Therón

2019

Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality

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Information dashboards are increasing their sophistication to match new necessities and adapt to the high quantities of generated data nowadays. These tools support visual analysis, knowledge generation, and thus, are crucial systems to assist decision-making processes. However, the design and development processes are complex, because several perspectives and components can be involved. Tailoring capabilities are focused on providing individualized dashboards without affecting the time-to-market through the decrease of the development processes' time. Among the methods used to configure these tools, the software product lines paradigm and model-driven development can be found. These paradigms benefit from the study of the target domain and the abstraction of features, obtaining highlevel models that can be instantiated into concrete models. This paper presents a dashboard meta-model that aims to be applicable to any dashboard. Through domain engineering, different features of these tools are identified and arranged into abstract structures and relationships to gain a better understanding of the domain. The goal of the meta-model is to obtain a framework for instantiating any dashboard to adapt them to different contexts and user profiles. One of the contexts in which dashboards are gaining relevance is Learning Analytics, as learning dashboards are powerful tools for assisting teachers and students in their learning activities. To illustrate the instantiation process of the presented meta-model, a small example within this relevant context (Learning Analytics) is also provided.

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Creating User-specific Business Process Monitoring Dashboards with a Model-driven Approach

Cited by 22 publications

References 9 publications

Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards

Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards

Tailored information dashboards

Capturing high-level requirements of information dashboards' components through meta-modeling

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