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Ana Lavalle
Lucentia Research, Department of Software and Computing Systems, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, San Vicente del Raspeig, Alicante, Spain

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Journal article
Published: 05 April 2021 in Information and Software Technology
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Information visualization is paramount for the analysis of Big Data. The volume of data requiring interpretation is continuously growing. However, users are usually not experts in information visualization. Thus, defining the visualization that best suits a determined context is a very challenging task for them. Moreover, it is often the case that users do not have a clear idea of what objectives they are building the visualizations for. Consequently, it is possible that graphics are misinterpreted, making wrong decisions that lead to missed opportunities. One of the underlying problems in this process is the lack of methodologies and tools that non-expert users in visualizations can use to define their objectives and visualizations. The main objectives of this paper are to (i) enable non-expert users in data visualization to communicate their analytical needs with little effort, (ii) generate the visualizations that best fit their requirements, and (iii) evaluate the impact of our proposal with reference to a case study, describing an experiment with 97 non-expert users in data visualization. We propose a methodology that collects user requirements and semi-automatically creates suitable visualizations. Our proposal covers the whole process, from the definition of requirements to the implementation of visualizations. The methodology has been tested with several groups to measure its effectiveness and perceived usefulness. The experiments increase our confidence about the utility of our methodology. It significantly improves over the case when users face the same problem manually. Specifically: (i) users are allowed to cover more analytical questions, (ii) the visualizations produced are more effective, and (iii) the overall satisfaction of the users is larger. By following our proposal, non-expert users will be able to more effectively express their analytical needs and obtain the set of visualizations that best suits their goals.

ACS Style

Ana Lavalle; Alejandro Maté; Juan Trujillo; Miguel A. Teruel; Stefano Rizzi. A methodology to automatically translate user requirements into visualizations: Experimental validation. Information and Software Technology 2021, 136, 106592 .

AMA Style

Ana Lavalle, Alejandro Maté, Juan Trujillo, Miguel A. Teruel, Stefano Rizzi. A methodology to automatically translate user requirements into visualizations: Experimental validation. Information and Software Technology. 2021; 136 ():106592.

Chicago/Turabian Style

Ana Lavalle; Alejandro Maté; Juan Trujillo; Miguel A. Teruel; Stefano Rizzi. 2021. "A methodology to automatically translate user requirements into visualizations: Experimental validation." Information and Software Technology 136, no. : 106592.

Journal article
Published: 14 August 2020 in Sensors
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Improving sustainability is a key concern for industrial development. Industry has recently been benefiting from the rise of IoT technologies, leading to improvements in the monitoring and breakdown prevention of industrial equipment. In order to properly achieve this monitoring and prevention, visualization techniques are of paramount importance. However, the visualization of real-time IoT sensor data has always been challenging, especially when such data are originated by sensors of different natures. In order to tackle this issue, we propose a methodology that aims to help users to visually locate and understand the failures that could arise in a production process.This methodology collects, in a guided manner, user goals and the requirements of the production process, analyzes the incoming data from IoT sensors and automatically derives the most suitable visualization type for each context. This approach will help users to identify if the production process is running as well as expected; thus, it will enable them to make the most sustainable decision in each situation. Finally, in order to assess the suitability of our proposal, a case study based on gas turbines for electricity generation is presented.

ACS Style

Ana LaValle; Miguel A. Teruel; Alejandro Maté; Juan Trujillo. Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production. Sensors 2020, 20, 4556 .

AMA Style

Ana LaValle, Miguel A. Teruel, Alejandro Maté, Juan Trujillo. Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production. Sensors. 2020; 20 (16):4556.

Chicago/Turabian Style

Ana LaValle; Miguel A. Teruel; Alejandro Maté; Juan Trujillo. 2020. "Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production." Sensors 20, no. 16: 4556.

Journal article
Published: 11 July 2020 in Sustainability
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Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.

ACS Style

Ana Lavalle; Miguel Teruel; Alejandro Maté; Juan Trujillo. Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices. Sustainability 2020, 12, 5595 .

AMA Style

Ana Lavalle, Miguel Teruel, Alejandro Maté, Juan Trujillo. Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices. Sustainability. 2020; 12 (14):5595.

Chicago/Turabian Style

Ana Lavalle; Miguel Teruel; Alejandro Maté; Juan Trujillo. 2020. "Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices." Sustainability 12, no. 14: 5595.

Conference paper
Published: 15 October 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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Choosing the right Visualization techniques is critical in Big Data Analytics. However, decision makers are not experts on visualization and they face up with enormous difficulties in doing so. There are currently many different (i) Big Data sources and also (ii) many different visual analytics to be chosen. Every visualization technique is not valid for every Big Data source and is not adequate for every context. In order to tackle this problem, we propose an approach, based on the Model Driven Architecture (MDA) to facilitate the selection of the right visual analytics to non-expert users. The approach is based on three different models: (i) a requirements model based on goal-oriented modeling for representing information requirements, (ii) a data representation model for representing data which will be connected to visualizations and, (iii) a visualization model for representing visualization details regardless of their implementation technology. Together with these models, a set of transformations allow us to semi-automatically obtain the corresponding implementation avoiding the intervention of the non-expert users. In this way, the great advantage of our proposal is that users no longer need to focus on the characteristics of the visualization, but rather, they focus on their information requirements and obtain the visualization that is better suited for their needs. We show the applicability of our proposal through a case study focused on a tax collection organization from a real project developed by the Spin-off company Lucentia Lab.

ACS Style

Ana LaValle; Alejandro Maté; Juan Trujillo. Requirements-Driven Visualizations for Big Data Analytics: A Model-Driven Approach. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 78 -92.

AMA Style

Ana LaValle, Alejandro Maté, Juan Trujillo. Requirements-Driven Visualizations for Big Data Analytics: A Model-Driven Approach. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():78-92.

Chicago/Turabian Style

Ana LaValle; Alejandro Maté; Juan Trujillo. 2019. "Requirements-Driven Visualizations for Big Data Analytics: A Model-Driven Approach." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 78-92.

Conference paper
Published: 01 September 2019 in 2019 IEEE 27th International Requirements Engineering Conference (RE)
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ACS Style

Ana Lavalle; Alejandro Mate; Juan Trujillo; Stefano Rizzi. Visualization Requirements for Business Intelligence Analytics: A Goal-Based, Iterative Framework. 2019 IEEE 27th International Requirements Engineering Conference (RE) 2019, 1 .

AMA Style

Ana Lavalle, Alejandro Mate, Juan Trujillo, Stefano Rizzi. Visualization Requirements for Business Intelligence Analytics: A Goal-Based, Iterative Framework. 2019 IEEE 27th International Requirements Engineering Conference (RE). 2019; ():1.

Chicago/Turabian Style

Ana Lavalle; Alejandro Mate; Juan Trujillo; Stefano Rizzi. 2019. "Visualization Requirements for Business Intelligence Analytics: A Goal-Based, Iterative Framework." 2019 IEEE 27th International Requirements Engineering Conference (RE) , no. : 1.