6 research outputs found
Capturing high-level requirements of information dashboards' components through meta-modeling
[EN]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.Tailoringcapabilities 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 high-level 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 structuresand 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
Tailored information dashboards: A systematic mapping of the literature
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
Towards a Technological Ecosystem to Provide Information Dashboards as a Service: A Dynamic Proposal for Supplying Dashboards Adapted to Specific Scenarios
[EN]Data are crucial to improve decision-making and obtain greater benefits in any type of
activity. However, the large amount of information generated by new technologies has made data
analysis and knowledge generation a complex task. Numerous tools have emerged to facilitate
this generation of knowledge, such as dashboards. Although dashboards are useful tools, their
effectiveness can be affected by poor design or by not taking into account the context in which
they are placed. Therefore, it is necessary to design and create custom dashboards according to
the audience and data domain. This paper presents an application of the software product line
paradigm and the integration of this approach into a web service to allow users to request source
code for customized information dashboards. The main goal is to introduce the idea of creating a
holistic ecosystem of different services to craft and integrate information visualizations in a variety of
contexts. One of the contexts that can be especially favored by this approach is the educational context,
where learning analytics, data analysis of student performance, and didactic tools are becoming very
relevant. Three different use cases of this approach are presented to illustrate the benefits of the
developed generative service
Information Dashboards and Tailoring Capabilities: A Systematic Literature Review
[EN]The design and development of information dashboards are not trivial. Several factors must be accounted; from the data to be displayed to the audience that will use the dashboard. However, the increase in popularity of these tools has extended their use in several and very different contexts among very different user pro les. This popularization has increased the necessity of building tailored displays focused on speci c requirements, goals, user roles, situations, domains, etc. Requirements are more sophisticated and varying; thus, dashboards need to match them to enhance knowledge generation and support more complex decision-making processes. This sophistication has led to the proposal of new approaches to address personal requirements and foster individualization regarding dashboards without involving high quantities of resources and long development processes. The goal of this work is to present a systematic review of the literature to analyze and classify the existing dashboard solutions that support
tailoring capabilities and the methodologies used to achieve them. The methodology follows the guidelines proposed by Kitchenham and other authors in the eld of software engineering. As results, 23 papers about tailored dashboards were retrieved. Three main approaches were identi ed regarding tailored solutions: customization, personalization, and adaptation. However, there is a wide variety of employed paradigms and features to develop tailored dashboards. The present systematic literature review analyzes challenges and issues regarding the existing solutions. It also identi es new research paths to enhance tailoring capabilities and thus, to improve user experience and insight delivery when it comes to visual analysis
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