2,100 research outputs found
Specifying information dashboards’ interactive features through meta-model instantiation
[EN]Information dashboards1 can be leveraged to make informed decisions with the goal of improving policies, processes, and results in different contexts. However, the design process of these tools can be convoluted, given the variety
of profiles that can be involved in decision-making processes. The educative context
is one of the contexts that can benefit from the use of information dashboards,
but given the diversity of actors within this area (teachers, managers, students,
researchers, etc.), it is necessary to take into account different factors to deliver
useful and effective tools. This work describes an approach to generate information
dashboards with interactivity capabilities in different contexts through
meta-modeling. Having the possibility of specifying interaction patterns within
the generative workflow makes the personalization process more fine-grained,
allowing to match very specific requirements from the user. An example of application
within the context of Learning Analytics is presented to demonstrate the
viability of this approach
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
Taking advantage of the software product line paradigm to generate customized user interfaces for decision-making processes: a case study on university employability
[EN]University employment and, specifically, employability has gained relevance since research in these fields can lead to improvement in the quality of life of individual citizens. However, empirical research is still insufficient to make significant decisions, and relying on powerful tools to explore data and reach insights on these fields is paramount. Information dashboards play a key role in analyzing and visually exploring data about a specific topic or domain, but end users can present several necessities that differ from each other, regarding the displayed information itself, design features and even functionalities. By applying a domain engineering approach (within the software product line paradigm), it is possible to produce customized dashboards to fit into particular requirements, by the identification of commonalities and singularities of every product that could be part of the product line. Software product lines increase productivity, maintainability and traceability regarding the evolution of the requirements, among other benefits. To validate this approach, a case study of its application in the context of the Spanish Observatory for University Employability and Employment system has been developed, where users (Spanish universities and administrators) can control their own dashboards to reach insights about the employability of their graduates. These dashboards have been automatically generated through a domain specific language, which provides the syntax to specify the requirements of each user. The domain language fuels a template-based code generator, allowing the generation of the dashboards' source code. Applying domain engineering to the dashboards' domain improves the development and maintainability of these complex software products given the variety of requirements that users might have regarding their graphical interfaces
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
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
Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering and machine learning
Information dashboards are sophisticated tools. Although they
enable users to reach useful insights and support their decisionmaking
challenges, a good design process is essential to obtain
powerful tools. Users need to be part of these design processes,
as they will be the consumers of the information displayed. But
users are very diverse and can have different goals, beliefs,
preferences, etc., and creating a new dashboard for each
potential user is not viable. There exist several tools that allow
users to configure their displays without requiring programming
skills. However, users might not exactly know what they want to
visualize or explore, also becoming the configuration process a
tedious task. This research project aims to explore the automatic
generation of user interfaces for supporting these decisionmaking
processes. To tackle these challenges, a domain
engineering, and machine learning approach is taken. The main
goal is to automatize the design process of dashboards by
learning from the context, including the end-users and the target
data to be displayed
Addressing Fine-Grained Variability in User-Centered Software Product Lines: A Case Study on Dashboards
Software product lines provide a theoretical framework to generate
and customize products by studying the target domain and by capturing the
commonalities among the potential products of the family. This domain
knowledge is subsequently used to implement a series of configurable core
assets that will be systematically reused to obtain products with different features
to match particular user requirements. Some kind of interactive systems,
like dashboards, require special attention as their features are very fine-grained.
Having the capacity of configuring a dashboard product to match particular user
requirements can improve the utility of these products by providing the support
to users to reach useful insights, in addition to a decrease in the development
time and an increase in maintainability. Several techniques for implementing
features and variability points in the context of SPLs are available, and it is
important to choose the right one to exploit the SPL paradigm benefits to the
maximum. This work addresses the materialization of fine-grained variability in
SPL through code templates and macros, framed in the particular domain of
dashboards
Generating Dashboards Using Fine-Grained Components: A Case Study for a PhD Programme
Developing dashboards is a complex domain, especially when several
stakeholders are involved; while some users could demand certain indicators,
other users could demand specific visualizations or design features.
Creating individual dashboards for each potential need would consume several
resources and time, being an unfeasible approach. Also, user requirements must
be thoroughly analyzed to understand their goals regarding the data to be
explored, and other characteristics that could affect their user experience. All
these necessities ask for a paradigm to foster reusability not only at development
level but also at knowledge level. Some methodologies, like the Software
Product Line paradigm, leverage domain knowledge and apply it to create a
series of assets that can be composed, parameterized, or combined to obtain
fully functional systems. This work presents an application of the SPL paradigm
to the domain of information dashboards, with the goal of reducing their
development time and increasing their effectiveness and user experience. Different
dashboard configurations have been suggested to test the proposed
approach in the context of the Education in the Knowledge Society PhD programme
of the University of Salamanca
- …