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FEDERICO DESIMONI

Assegnista di ricerca presso: Dipartimento di Ingegneria "Enzo Ferrari"

DOCENTE A CONTRATTO presso: Dipartimento di Scienze e Metodi dell'Ingegneria


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Pubblicazioni

2020 - A comparative study of state-of-the-art linked data visualization tools [Relazione in Atti di Convegno]
Desimoni, F.; Bikakis, N.; Po, L.; Papastefanatos, G.
abstract

Data visualization tools are of great importance for the exploration and the analysis of Linked Data (LD) datasets. Such tools allow users to get an overview, understand content, and discover interesting insights of a dataset. Visualization approaches vary according to the domain, the type of data, the task that the user is trying to perform, as well as the skills of the user. Thus, the study of the capabilities that each approach offers is crucial in supporting users to select the proper tool/technique based on their need. In this paper we present a comparative study of the state-of-the-art LD visualization tools over a list of fundamental use cases. First, we define 16 use cases that are representative in the setting of LD visual exploration, examining several tool's aspects; e.g., functionality capabilities, feature richness. Then, we evaluate these use cases over 10 LD visualization tools, examining: (1) if the tools have the required functionality for the tasks; and (2) if they allow the successful completion of the tasks over the DBpedia dataset. Finally, we discuss the insights derived from the evaluation, and we point out possible future directions.


2020 - Empirical Evaluation of Linked Data Visualization Tools [Articolo su rivista]
Desimoni, Federico; Po, Laura
abstract

The economic impact of open data in Europe has an estimated value of €140 billions a year between direct and indirect effects. The social impact is also known to be high, as the use of more transparent open data have been enhancing public services and creating new opportunities for citizens and organizations. We are assisting at a staggering growth in the production and consumption of Linked Data (LD). Exploring, visualizing and analyzing LD is a core task for a variety of users in numerous scenarios. This paper deeply analyzes the state of the art of tools for LD visualization. Linked Data visualization aims to provide graphical representations of datasets or of some information of interest selected by a user, with the aim to facilitate their analysis. A complete list of 77 LD visualization tools has been created starting from tools listed in previous surveys or research papers and integrating newer tools recently published online. The visualization tools have been described and compared based on their usability, and their features. A set of goals that LD tools should implement in order to provide clear and convincing visualizations has been defined and 14 tools have been tested on a big LD dataset. The results of this comparison and test led us to define some suggestions for LD consumers in order for them to be able to select the most appropriate tools based on the type of analysis they wish to perform.


2020 - Linked Data Visualization: Techniques, Tools, and Big Data [Monografia/Trattato scientifico]
Po, Laura; Bikakis, Nikos; Desimoni, Federico; Papastefanatos, George
abstract

Linked Data (LD) is nowadays a well established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from very different scientific and commercial domains. The development of Linked Data Visualization techniques and tools has been followed as the primary means for the analysis of this vast amount of information by data scientists, domain experts, business users and citizens. This book aims at providing an overview of the recent advances in this area, focusing on techniques, tools and use cases of visualization and visual analysis of LD. It presents all necessary preliminary concepts related to the LD technology, the main techniques employed for data visualization based on the characteristics of the underlying data, use cases and tools for LD visualization and finally a thorough assessment of the usability of these tools, under different business scenarios. The goal of this book is to offer interested readers a complete guide on the evolution of LD visualization and empower them to get started with the visual analysis of such data.


2020 - Providing effective visualizations over big linked data [Relazione in Atti di Convegno]
DESIMONI, FEDERICO; PO, Laura
abstract

The number and the size of Linked Data sources are constantly increasing. In some lucky case, the data source is equipped with a tool that guides and helps the user during the exploration of the data, but in most cases, the data are published as an RDF dump through a SPARQL endpoint that can be accessed only through SPARQL queries. Although the RDF format was designed to be processed by machines, there is a strong need for visualization and exploration tools. Data visualizations make big and small linked data easier for the human brain to understand, and visualization also makes it easier to detect patterns, trends, and outliers in groups of data. For this reason, we developed a tool called H-BOLD (Highlevel Visualization over Big Linked Open Data). H-BOLD aims to help the user exploring the content of a Linked Data by providing a high-level view of the structure of the dataset and an interactive exploration that allows users to focus on the connections and attributes of one or more classes. Moreover, it provides a visual interface for querying the endpoint that automatically generates SPARQL queries.


2020 - Semantic Traffic Sensor Data: The TRAFAIR Experience [Articolo su rivista]
Desimoni, Federico; Ilarri, Sergio; Po, Laura; Rollo, Federica; Trillo Lado, Raquel
abstract

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.


2020 - Using real sensors data to calibrate a traffic model for the city of Modena [Relazione in Atti di Convegno]
BACHECHI, CHIARA; ROLLO, FEDERICA; DESIMONI, FEDERICO; PO, Laura
abstract

In Italy, road vehicles are the preferred mean of transport. Over the last years, in almost all the EU Member States, the passenger car fleet increased. The high number of vehicles complicates urban planning and often results in traffic congestion and areas of increased air pollution. Overall, efficient traffic control is profitable in individual, societal, financial, and environmental terms. Traffic management solutions typically require the use of simulators able to capture in detail all the characteristics and dependencies associated with real-life traffic. Therefore, the realization of a traffic model can help to discover and control traffic bottlenecks in the urban context. In this paper, we analyze how to better simulate vehicle flows measured by traffic sensors in the streets. A dynamic traffic model was set up starting from traffic sensors data collected every minute in about 300 locations in the city of Modena. The reliability of the model is discussed and proved with a comparison between simulated values and real values from traffic sensors. This analysis pointed out some critical issues. Therefore, to better understand the origin of fake jams and incoherence with real data, we approached different configurations of the model as possible solutions.


2020 - Visual analytics for spatio-temporal air quality data [Relazione in Atti di Convegno]
Bachechi, Chiara; Desimoni, Federico; Po, Laura
abstract

Air pollution is the second biggest environmental concern for Europeans after climate change and the major risk to public health. It is imperative to monitor the spatio-temporal patterns of urban air pollution. The TRAFAIR air quality dashboard is an effective web application to empower decision-makers to be aware of the urban air quality conditions, define new policies, and keep monitoring their effects. The architecture copes with the multidimensionality of data and the real-time visualization challenge of big data streams coming from a network of low-cost sensors. Moreover, it handles the visualization and management of predictive air quality maps series that is produced by an air pollution dispersion model. Air quality data are not only visualized at a limited set of locations at different times but in the continuous space-time domain, thanks to interpolated maps that estimate the pollution at un-sampled locations.


2018 - H-BOLD (High level visualizations on Big Open Linked Data) [Software]
PO, Laura; DESIMONI, FEDERICO; MALVEZZI, DAVIDE
abstract