2024
Conference article
Open Access
Operationalizing the fundamental rights impact assessment for AI systems: the FRIA project
Savella R., Pratesi F., Trasarti R., Gatt L., Gaeta M. C., Caggiano I. A., Aulino L., Troisi E., Izzo L.
This paper presents the FRIA Project, a multidisciplinary research study which connects the legal and ethical aspects related to the impact on fundamental rights of Artificial Intelligence systems and the technical issues that arise in the creation of an automated tool for the Fundamental Rights Impact Assessment, which is the ultimate objective of this work.Project(s): SoBigData-PlusPlus
Savella R., Pratesi F., Trasarti R., Gatt L., Gaeta M. C., Caggiano I. A., Aulino L., Troisi E., Izzo L.
This paper presents the FRIA Project, a multidisciplinary research study which connects the legal and ethical aspects related to the impact on fundamental rights of Artificial Intelligence systems and the technical issues that arise in the creation of an automated tool for the Fundamental Rights Impact Assessment, which is the ultimate objective of this work.Project(s): SoBigData-PlusPlus
See at:
CNR IRIS 
| ital-ia2024.it | CNR IRIS 
2024
Other
Open Access
FRIA Fundamental Rights Impact Assessment
Savella R., Pratesi F., Fadda D., Trasarti R.
Poster presented at ISTI Day 2023-2024 edition on June 14 2024.Project(s): SoBigData RI PPP, SoBigData-PlusPlus
Savella R., Pratesi F., Fadda D., Trasarti R.
Poster presented at ISTI Day 2023-2024 edition on June 14 2024.Project(s): SoBigData RI PPP
, SoBigData-PlusPlus
See at:
CNR IRIS | www.isti.cnr.it | CNR IRIS
2023
Journal article
Open Access
A research infrastructure where artificial intelligence meets persons and society
Trasarti R, Genovali K, Rapisarda B
In our complex society, the ethical use and storage of data are essential for the scientific community and institutions to build trust in citizens. SoBigData is a pan-European and cross-disciplinary Research Infrastructure on social mining and data analytics, which bases its research activities on ethics and fairness. SoBigData doesn't apply science only to the most challenging societal issues; in fact, it provides data and facilities to researchers and services to firms and public administrations to develop innovative tools and respond to societal needs. Above all, it works to create an ecosystem for data research that respects the founding principles of Europe for the benefit of the whole community.Source: ERCIM NEWS, vol. 133, pp. 30-31
Trasarti R, Genovali K, Rapisarda B
In our complex society, the ethical use and storage of data are essential for the scientific community and institutions to build trust in citizens. SoBigData is a pan-European and cross-disciplinary Research Infrastructure on social mining and data analytics, which bases its research activities on ethics and fairness. SoBigData doesn't apply science only to the most challenging societal issues; in fact, it provides data and facilities to researchers and services to firms and public administrations to develop innovative tools and respond to societal needs. Above all, it works to create an ecosystem for data research that respects the founding principles of Europe for the benefit of the whole community.Source: ERCIM NEWS, vol. 133, pp. 30-31
See at:
ercim-news.ercim.eu | CNR IRIS | ISTI Repository | CNR IRIS
2023
Conference article
Open Access
Dataspaces: concepts, architectures and initiatives
Atzori M, Ciaramella A, Diamantini C, Di Martino B, Distefano S, Facchinetti T, Montecchiani F, Nocera A, Ruffo G, Trasarti R
Despite not being a new concept, dataspaces have become a prominent topic due to the increasing availability of data and the need for efficient management and utilization of diverse data sources. In simple terms, a dataspace refers to an environment where data from various sources, formats, and domains can be integrated, shared, and analyzed. It aims to provide a unified view of heterogeneous data by bridging the gap between different data silos, enabling interoperability. The concept of dataspaces promotes the idea that data should be treated as a cohesive entity, rather than being fragmented across different systems and applications. Dataspaces often involve the integration of structured and unstructured data, including databases, documents, sensor data, social media feeds, and more. The goal is to enable organizations to harness the full potential of their data assets by facilitating data discovery, access, and analysis. By bringing together diverse data sources, dataspaces can offer new insights, support decision-making processes, and drive innovation. In the context of European Commission-funded research projects, dataspaces are often explored as part of initiatives focused on data management, data sharing, and the development of data-driven technologies. These projects aim to address challenges related to data integration, data privacy, data governance, and scalability. The goal is to advance the state of the art in data management and enable organizations to leverage data more effectively for societal, economic, and scientific advancements. It is important to notice that while dataspaces offer potential benefits, they also come with challenges. These challenges include data quality assurance, data privacy and security, semantic interoperability, scalability, and the need for appropriate data governance frameworks. Overall, dataspaces represent an approach to managing and utilizing data that emphasizes integration, interoperability, and accessibility. The concept is being explored and researched to develop innovative solutions that can unlock the value of data in various domains and sectors.Source: CEUR WORKSHOP PROCEEDINGS. Naples, Italy, 11-13/09/2023
Project(s): SoBigData
Atzori M, Ciaramella A, Diamantini C, Di Martino B, Distefano S, Facchinetti T, Montecchiani F, Nocera A, Ruffo G, Trasarti R
Despite not being a new concept, dataspaces have become a prominent topic due to the increasing availability of data and the need for efficient management and utilization of diverse data sources. In simple terms, a dataspace refers to an environment where data from various sources, formats, and domains can be integrated, shared, and analyzed. It aims to provide a unified view of heterogeneous data by bridging the gap between different data silos, enabling interoperability. The concept of dataspaces promotes the idea that data should be treated as a cohesive entity, rather than being fragmented across different systems and applications. Dataspaces often involve the integration of structured and unstructured data, including databases, documents, sensor data, social media feeds, and more. The goal is to enable organizations to harness the full potential of their data assets by facilitating data discovery, access, and analysis. By bringing together diverse data sources, dataspaces can offer new insights, support decision-making processes, and drive innovation. In the context of European Commission-funded research projects, dataspaces are often explored as part of initiatives focused on data management, data sharing, and the development of data-driven technologies. These projects aim to address challenges related to data integration, data privacy, data governance, and scalability. The goal is to advance the state of the art in data management and enable organizations to leverage data more effectively for societal, economic, and scientific advancements. It is important to notice that while dataspaces offer potential benefits, they also come with challenges. These challenges include data quality assurance, data privacy and security, semantic interoperability, scalability, and the need for appropriate data governance frameworks. Overall, dataspaces represent an approach to managing and utilizing data that emphasizes integration, interoperability, and accessibility. The concept is being explored and researched to develop innovative solutions that can unlock the value of data in various domains and sectors.Source: CEUR WORKSHOP PROCEEDINGS. Naples, Italy, 11-13/09/2023
Project(s): SoBigData
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Conference article
Open Access
SoBigData RI: european integrated infrastructure for social mining and big data analytics
Trasarti R, Grossi V, Natilli M, Rapisarda B
SoBigData RI has the ambition to support the rising demand for cross-disciplinary research and innovation on the multiple aspects of social complexity from combined data and model-driven perspectives and the increasing importance of ethics and data scientists' responsibility as pillars of trustworthy use of Big Data and analytical technology. Digital traces of human activities offer a considerable opportunity to scrutinize the ground truth of individual and collective behaviour at an unprecedented detail and on a global scale. This increasing wealth of data is a chance to understand social complexity, provided we can rely on social mining, i.e., adequate means for accessing big social data and models for extracting knowledge from them. SoBigData RI, with its tools and services, empowers researchers and innovators through a platform for the design and execution of large-scale social mining experiments, open to users with diverse backgrounds, accessible on the cloud (aligned with EOSC), and also exploiting supercomputing facilities. Pushing the FAIR (Findable, Accessible, Interoperable) and FACT (Fair, Accountable, Confidential, and Transparent) principles will render social mining experiments more efficiently designed, adjusted, and repeatable by domain experts that are not data scientists. SoBigData RI moves forward from the simple awareness of ethical and legal challenges in social mining to the development of concrete tools that operationalize ethics with value-sensitive design, incorporating values and norms for privacy protection, fairness, transparency, and pluralism. SoBigData RI is the result of two H2020 grants (g.a. n.654024 and 871042), and it is part of the ESFRI 2021 Roadmap.Source: CEUR WORKSHOP PROCEEDINGS, pp. 117-124. Tirrenia (PI), Italy, 19-22/06/2022
Project(s): SoBigData-PlusPlus
Trasarti R, Grossi V, Natilli M, Rapisarda B
SoBigData RI has the ambition to support the rising demand for cross-disciplinary research and innovation on the multiple aspects of social complexity from combined data and model-driven perspectives and the increasing importance of ethics and data scientists' responsibility as pillars of trustworthy use of Big Data and analytical technology. Digital traces of human activities offer a considerable opportunity to scrutinize the ground truth of individual and collective behaviour at an unprecedented detail and on a global scale. This increasing wealth of data is a chance to understand social complexity, provided we can rely on social mining, i.e., adequate means for accessing big social data and models for extracting knowledge from them. SoBigData RI, with its tools and services, empowers researchers and innovators through a platform for the design and execution of large-scale social mining experiments, open to users with diverse backgrounds, accessible on the cloud (aligned with EOSC), and also exploiting supercomputing facilities. Pushing the FAIR (Findable, Accessible, Interoperable) and FACT (Fair, Accountable, Confidential, and Transparent) principles will render social mining experiments more efficiently designed, adjusted, and repeatable by domain experts that are not data scientists. SoBigData RI moves forward from the simple awareness of ethical and legal challenges in social mining to the development of concrete tools that operationalize ethics with value-sensitive design, incorporating values and norms for privacy protection, fairness, transparency, and pluralism. SoBigData RI is the result of two H2020 grants (g.a. n.654024 and 871042), and it is part of the ESFRI 2021 Roadmap.Source: CEUR WORKSHOP PROCEEDINGS, pp. 117-124. Tirrenia (PI), Italy, 19-22/06/2022
Project(s): SoBigData-PlusPlus
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Conference article
Open Access
Workflows for bringing data science on the cloud/edge computing continuum
Dazzi P, Grossi V, Trasarti R
Research infrastructures play a crucial role in the development of data science. In fact, the conjunction of data, infrastructures and analytical methods enable multidisciplinary scientists and innovators to extract knowledge and to make the knowledge and experiments reusable by the scientific community, innovators providing an impact on science and society. Resources such as data and methods, help domain and data scientists to transform research in an innovation question into a responsible datadriven analytical process. On the other hand, Edge computing is a new computing paradigm that is spreading and developing at an incredible pace. Edge computing is based on the assumption that for certain applications is beneficial to bring the computation as closer as possible to data or end-users. This paper discusses about this topic by describing an approach for writing data science workflows targeting research infrastructures that encompass resources located at the edge of the network.Source: CEUR WORKSHOP PROCEEDINGS, pp. 125-132. Tirrenia (PI), Italy, 19-22/06/2022
Project(s): SoBigData-PlusPlus
Dazzi P, Grossi V, Trasarti R
Research infrastructures play a crucial role in the development of data science. In fact, the conjunction of data, infrastructures and analytical methods enable multidisciplinary scientists and innovators to extract knowledge and to make the knowledge and experiments reusable by the scientific community, innovators providing an impact on science and society. Resources such as data and methods, help domain and data scientists to transform research in an innovation question into a responsible datadriven analytical process. On the other hand, Edge computing is a new computing paradigm that is spreading and developing at an incredible pace. Edge computing is based on the assumption that for certain applications is beneficial to bring the computation as closer as possible to data or end-users. This paper discusses about this topic by describing an approach for writing data science workflows targeting research infrastructures that encompass resources located at the edge of the network.Source: CEUR WORKSHOP PROCEEDINGS, pp. 125-132. Tirrenia (PI), Italy, 19-22/06/2022
Project(s): SoBigData-PlusPlus
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Contribution to book
Open Access
Ethics in smart information systems
Pratesi F, Trasarti R, Giannotti F
This chapter analyses some of the ethical implications of recent developments in artificial intelligence (AI), data mining, machine learning and robotics. In particular, we start summarising the more consolidated issues and solutions related to privacy in data management systems, moving towards the novel concept of explainability. The chapter reviews the development of the right to privacy and the right to explanation, culminated in the General Data Protection Regulation. However, the new kinds of big data (such as internet logs or GPS tracking) require a different approach to managing privacy requirements. Several solutions have been developed and will be reviewed here. Our view is that generally data protection must be considered from the beginning as novel AI solutions are developing using the Privacy-by-Design paradigm. This involves a shift in perspective away from remedying problems to trying to prevent them, instead. We conclude by covering the main requirements necessary to achieve a trustworthy scenario, as advised also by the European Commission. A step in the direction towards Trustworthy AI was achieved in the Ethics Guidelines for Trustworthy Artificial Intelligence produced by an expert group for the European Commission. The key elements in these guidelines will reviewed in this chapter. To ensure European independence and leadership, we must invest wisely by bundling, connecting and opening our AI resources while also having in mind ethical priorities, such as transparency and fairness.DOI: 10.51952/9781447363972.ch009
DOI: 10.56687/9781447363972-012
DOI: 10.2307/j.ctv2tbwqd5.14
Project(s): TAILOR, PRO-RES , SoBigData-PlusPlus
Metrics:
Pratesi F, Trasarti R, Giannotti F
This chapter analyses some of the ethical implications of recent developments in artificial intelligence (AI), data mining, machine learning and robotics. In particular, we start summarising the more consolidated issues and solutions related to privacy in data management systems, moving towards the novel concept of explainability. The chapter reviews the development of the right to privacy and the right to explanation, culminated in the General Data Protection Regulation. However, the new kinds of big data (such as internet logs or GPS tracking) require a different approach to managing privacy requirements. Several solutions have been developed and will be reviewed here. Our view is that generally data protection must be considered from the beginning as novel AI solutions are developing using the Privacy-by-Design paradigm. This involves a shift in perspective away from remedying problems to trying to prevent them, instead. We conclude by covering the main requirements necessary to achieve a trustworthy scenario, as advised also by the European Commission. A step in the direction towards Trustworthy AI was achieved in the Ethics Guidelines for Trustworthy Artificial Intelligence produced by an expert group for the European Commission. The key elements in these guidelines will reviewed in this chapter. To ensure European independence and leadership, we must invest wisely by bundling, connecting and opening our AI resources while also having in mind ethical priorities, such as transparency and fairness.DOI: 10.51952/9781447363972.ch009
DOI: 10.56687/9781447363972-012
DOI: 10.2307/j.ctv2tbwqd5.14
Project(s): TAILOR
, PRO-RES , SoBigData-PlusPlus Metrics:
See at:
bristoluniversitypressdigital.com | doi.org | doi.org | CNR IRIS | ISTI Repository | doi.org | CNR IRIS
2021
Journal article
Open Access
A workflow language for research e-infrastructures
Candela L, Grossi V, Manghi P, Trasarti R
Research e-infrastructures are "systems of systems," patchworks of resources such as tools and services, which change over time to address the evolving needs of the scientific process. In such environments, researchers carry out their scientific process in terms of sequences of actions that mainly include invocation of web services, user interaction with web applications, user download and use of shared software libraries/tools. The resulting workflows are intended to generate new research products (articles, datasets, methods, etc.) out of existing ones. Sharing a digital and executable representation of such workflows with other scientists would enforce Open Science publishing principles of "reproducibility of science" and "transparent assessment of science." This work presents HyWare, a language and execution platform capable of representing scientific processes in highly heterogeneous research e-infrastructures in terms of so-called hybrid workflows. Hybrid workflows can express sequences of "manually executable actions," i.e., formal descriptions guiding users to repeat a reasoning, protocol or manual procedure, and "machine-executable actions," i.e., encoding of the automated execution of one (or more) web services. An HyWare execution platform enables scientists to (i) create and share workflows out of a given action set (as defined by the users to match e-infrastructure needs) and (ii) execute hybrid workflows making sure input/output of the actions flow properly across manual and automated actions. The HyWare language and platform can be implemented as an extension of well-known workflow languages and platforms.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
DOI: 10.1007/s41060-020-00237-x
Project(s): SoBigData
Metrics:
Candela L, Grossi V, Manghi P, Trasarti R
Research e-infrastructures are "systems of systems," patchworks of resources such as tools and services, which change over time to address the evolving needs of the scientific process. In such environments, researchers carry out their scientific process in terms of sequences of actions that mainly include invocation of web services, user interaction with web applications, user download and use of shared software libraries/tools. The resulting workflows are intended to generate new research products (articles, datasets, methods, etc.) out of existing ones. Sharing a digital and executable representation of such workflows with other scientists would enforce Open Science publishing principles of "reproducibility of science" and "transparent assessment of science." This work presents HyWare, a language and execution platform capable of representing scientific processes in highly heterogeneous research e-infrastructures in terms of so-called hybrid workflows. Hybrid workflows can express sequences of "manually executable actions," i.e., formal descriptions guiding users to repeat a reasoning, protocol or manual procedure, and "machine-executable actions," i.e., encoding of the automated execution of one (or more) web services. An HyWare execution platform enables scientists to (i) create and share workflows out of a given action set (as defined by the users to match e-infrastructure needs) and (ii) execute hybrid workflows making sure input/output of the actions flow properly across manual and automated actions. The HyWare language and platform can be implemented as an extension of well-known workflow languages and platforms.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
DOI: 10.1007/s41060-020-00237-x
Project(s): SoBigData
Metrics:
See at:
CNR IRIS | link.springer.com | International Journal of Data Science and Analytics | ISTI Repository | CNR IRIS | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics
2021
Conference article
Open Access
Data Science Workflows for the Cloud/Edge Computing Continuum
Grossi V, Trasarti R, Dazzi P
Research infrastructures play a crucial role in the development of data science. In fact, the conjunction of data, infrastructures and analytical methods enable multidisciplinary scientists and innovators to extract knowledge and to make the knowledge and experiments reusable by the scientific community, innovators providing an im- pact on science and society. Resources such as data and methods, help domain and data scientists to transform research in an innovation question into a responsible data-driven analytical process. On the other hand, Edge computing is a new computing paradigm that is spreading and developing at an incredible pace. Edge computing is based on the assumption that for certain applications is beneficial to bring the computation as closer as possible to data or end-users. This paper introduces an approach for writing data science workflows targeting research infrastructures that encompass resources located at the edge of the network.DOI: 10.1145/3452369.3463820
Project(s): SoBigData-PlusPlus
Metrics:
Grossi V, Trasarti R, Dazzi P
Research infrastructures play a crucial role in the development of data science. In fact, the conjunction of data, infrastructures and analytical methods enable multidisciplinary scientists and innovators to extract knowledge and to make the knowledge and experiments reusable by the scientific community, innovators providing an im- pact on science and society. Resources such as data and methods, help domain and data scientists to transform research in an innovation question into a responsible data-driven analytical process. On the other hand, Edge computing is a new computing paradigm that is spreading and developing at an incredible pace. Edge computing is based on the assumption that for certain applications is beneficial to bring the computation as closer as possible to data or end-users. This paper introduces an approach for writing data science workflows targeting research infrastructures that encompass resources located at the edge of the network.DOI: 10.1145/3452369.3463820
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2020
Journal article
Open Access
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
Nanni M, Andrienko G, Barabasi Al, Boldrini C, Bonchi F, Cattuto C, Chiaromonte F, Comandé G, Conti M, Coté M, Dignum F, Dignum V, Domingoferrer J, Ferragina P, Giannotti F, Guidotti R, Helbing D, Kaski K, Kertesz J, Lehmann S, Lepri B, Lukowicz P, Matwin S, Jimenez D, Monreale A, Morik K, Oliver N, Passarella A, Passerini A, Pedreschi D, Pentland A, Pianesi F, Pratesi F, Rinzivillo S, Ruggieri S, Siebes A, Torra V, Trasarti R, Van Den Hoven J, Vespignani A
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: TRANSACTIONS ON DATA PRIVACY, vol. 13 (issue 1), pp. 61-66
Nanni M, Andrienko G, Barabasi Al, Boldrini C, Bonchi F, Cattuto C, Chiaromonte F, Comandé G, Conti M, Coté M, Dignum F, Dignum V, Domingoferrer J, Ferragina P, Giannotti F, Guidotti R, Helbing D, Kaski K, Kertesz J, Lehmann S, Lepri B, Lukowicz P, Matwin S, Jimenez D, Monreale A, Morik K, Oliver N, Passarella A, Passerini A, Pedreschi D, Pentland A, Pianesi F, Pratesi F, Rinzivillo S, Ruggieri S, Siebes A, Torra V, Trasarti R, Van Den Hoven J, Vespignani A
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: TRANSACTIONS ON DATA PRIVACY, vol. 13 (issue 1), pp. 61-66
See at:
CNR IRIS | ISTI Repository | www.tdp.cat | CNR IRIS
2020
Journal article
Open Access
(So) Big Data and the transformation of the city
Andrienko G, Andrienko N, Boldrini C, Caldarelli G, Cintia P, Cresci S, Facchini A, Giannotti F, Gionis A, Guidotti R, Mathioudakis M, Muntean Ci, Pappalardo L, Pedreschi D, Pournaras E, Pratesi F, Tesconi M, Trasarti R
The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the "City of Citizens" thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, vol. 1
DOI: 10.1007/s41060-020-00207-3
Project(s): SoBigData
Metrics:
Andrienko G, Andrienko N, Boldrini C, Caldarelli G, Cintia P, Cresci S, Facchini A, Giannotti F, Gionis A, Guidotti R, Mathioudakis M, Muntean Ci, Pappalardo L, Pedreschi D, Pournaras E, Pratesi F, Tesconi M, Trasarti R
The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the "City of Citizens" thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, vol. 1
DOI: 10.1007/s41060-020-00207-3
Project(s): SoBigData
Metrics:
See at:
Aaltodoc Publication Archive | International Journal of Data Science and Analytics | White Rose Research Online | HELDA - Digital Repository of the University of Helsinki | Archivio istituzionale della ricerca - Università degli Studi di Venezia Ca' Foscari | CNR IRIS | link.springer.com | International Journal of Data Science and Analytics | City Research Online | ISTI Repository | CNR IRIS | Fraunhofer-ePrints
2020
Conference article
Open Access
Towards in-memory sub-trajectory similarity search
Alamdari I, Nanni M, Trasarti R, Pedreschi D
Spatial-temporal trajectory data contains rich information aboutmoving objects and has been widely used for a large numberof real-world applications. However, the complexity of spatial-temporal trajectory data, on the one hand, and the fast collectionof datasets, on the other hand, has made it challenging to ef-ficiently store, process, and query such data. In this paper, wepropose a scalable method to analyze the sub-trajectory simi-larity search in an in-memory cluster computing environment.Notably, we have extended Apache Spark with efficient trajectoryindexing, partitioning, and querying functionalities to supportthe sub-trajectory similarity query. Our experiments on a realtrajectory dataset have shown the efficiency and effectiveness ofthe proposed method.Source: CEUR WORKSHOP PROCEEDINGS. Copenhagen, Denmark, 30th March - 2nd April, 2020
Project(s): Track and Know
Alamdari I, Nanni M, Trasarti R, Pedreschi D
Spatial-temporal trajectory data contains rich information aboutmoving objects and has been widely used for a large numberof real-world applications. However, the complexity of spatial-temporal trajectory data, on the one hand, and the fast collectionof datasets, on the other hand, has made it challenging to ef-ficiently store, process, and query such data. In this paper, wepropose a scalable method to analyze the sub-trajectory simi-larity search in an in-memory cluster computing environment.Notably, we have extended Apache Spark with efficient trajectoryindexing, partitioning, and querying functionalities to supportthe sub-trajectory similarity query. Our experiments on a realtrajectory dataset have shown the efficiency and effectiveness ofthe proposed method.Source: CEUR WORKSHOP PROCEEDINGS. Copenhagen, Denmark, 30th March - 2nd April, 2020
Project(s): Track and Know
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2019
Journal article
Open Access
Computational modelling and data-driven techniques for systems analysis
Matwin S, Tesei L, Trasarti R
This JIIS Special Issue aimed at bringing together contributions from academia, industry and research institutions interested in the combined application of computational modelling methods with data-driven techniques from the areas of knowledge management, data mining and machine learning. Modelling methodologies of interest included automata, agents, Petri nets, process algebras and rewriting systems. Application domains included social systems, ecology, biology, medicine, smart cities, governance, education, software engineering, and any other field that deals with complex systems and large amounts of data.Source: JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, vol. 52 (issue 3), pp. 473-475
DOI: 10.1007/s10844-019-00554-z
Project(s): SoBigData
Metrics:
Matwin S, Tesei L, Trasarti R
This JIIS Special Issue aimed at bringing together contributions from academia, industry and research institutions interested in the combined application of computational modelling methods with data-driven techniques from the areas of knowledge management, data mining and machine learning. Modelling methodologies of interest included automata, agents, Petri nets, process algebras and rewriting systems. Application domains included social systems, ecology, biology, medicine, smart cities, governance, education, software engineering, and any other field that deals with complex systems and large amounts of data.Source: JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, vol. 52 (issue 3), pp. 473-475
DOI: 10.1007/s10844-019-00554-z
Project(s): SoBigData
Metrics:
See at:
Journal of Intelligent Information Systems | CNR IRIS | Journal of Intelligent Information Systems | ISTI Repository | CNR IRIS
2019
Journal article
Open Access
Finding roles of players in football using automatic particle swarm optimization-clustering algorithm
Behravan I, Zahiri Sh, Razavi Sm, Trasarti R
Recently, professional team sport organizations have invested their resources to analyze their own and opponents' performance. So, developing methods and algorithms for analyzing team sports has become one of the most popular topics among data scientists. Analyzing football is hard because of its complexity, number of events in each match, and constant flow of circulation of the ball. Finding roles of players with the purpose of analyzing the performance of a team or making a meaningful comparison between players is crucial. In this article, an automatic big data clustering method, based on a swarm intelligence algorithm, is proposed to automatically cluster the data set of players' performance centers in different matches and extract different kinds of roles in football. The proposed method created using particle swarm optimization algorithm has two phases. In the first phase, the algorithm searches the solution space to find the number of clusters and, in the second phase, it finds the positions of the centroids. To show the effectiveness of the algorithm, it is tested on six synthetic data sets and its performance is compared with two other conventional clustering methods. After that, the algorithm is used to find clusters of a data set containing 93,000 objects, which are the centers of players' performance in about 4900 matches in different European leagues.Source: BIG DATA, vol. 7 (issue 1), pp. 35-56
DOI: 10.1089/big.2018.0069
Metrics:
Behravan I, Zahiri Sh, Razavi Sm, Trasarti R
Recently, professional team sport organizations have invested their resources to analyze their own and opponents' performance. So, developing methods and algorithms for analyzing team sports has become one of the most popular topics among data scientists. Analyzing football is hard because of its complexity, number of events in each match, and constant flow of circulation of the ball. Finding roles of players with the purpose of analyzing the performance of a team or making a meaningful comparison between players is crucial. In this article, an automatic big data clustering method, based on a swarm intelligence algorithm, is proposed to automatically cluster the data set of players' performance centers in different matches and extract different kinds of roles in football. The proposed method created using particle swarm optimization algorithm has two phases. In the first phase, the algorithm searches the solution space to find the number of clusters and, in the second phase, it finds the positions of the centroids. To show the effectiveness of the algorithm, it is tested on six synthetic data sets and its performance is compared with two other conventional clustering methods. After that, the algorithm is used to find clusters of a data set containing 93,000 objects, which are the centers of players' performance in about 4900 matches in different European leagues.Source: BIG DATA, vol. 7 (issue 1), pp. 35-56
DOI: 10.1089/big.2018.0069
Metrics:
See at:
CNR IRIS | ISTI Repository | www.liebertpub.com | Big Data | CNR IRIS | CNR IRIS
2018
Journal article
Open Access
Guest editorial special issue on knowledge discovery from mobility data for intelligent transportation systems
Moreiramatias L, Gama J, Olaverri Monreal C, Nair R, Trasarti R
Source: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (PRINT), vol. 19 (issue 11), pp. 3626-3629
DOI: 10.1109/tits.2018.2877063
Metrics:
Moreiramatias L, Gama J, Olaverri Monreal C, Nair R, Trasarti R
Source: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (PRINT), vol. 19 (issue 11), pp. 3626-3629
DOI: 10.1109/tits.2018.2877063
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | IEEE Transactions on Intelligent Transportation Systems | ISTI Repository | IEEE Transactions on Intelligent Transportation Systems | CNR IRIS
2018
Journal article
Open Access
PRUDEnce: A system for assessing privacy risk vs utility in data sharing ecosystems
Pratesi F, Monreale A, Trasarti R, Giannotti F, Pedreschi D, Yanagihara T
Data describing human activities are an important source of knowledge useful for understanding individual and collective behavior and for developing a wide range of user services. Unfortunately, this kind of data is sensitive, because people's whereabouts may allow re-identification of individuals in a de-identified database. Therefore, Data Providers, before sharing those data, must apply any sort of anonymization to lower the privacy risks, but they must be aware and capable of controlling also the data quality, since these two factors are often a trade-off. In this paper we propose PRUDEnce (Privacy Risk versus Utility in Data sharing Ecosystems), a system enabling a privacy-aware ecosystem for sharing personal data. It is based on a methodology for assessing both the empirical (not theoretical) privacy risk associated to users represented in the data, and the data quality guaranteed only with users not at risk. Our proposal is able to support the Data Provider in the exploration of a repertoire of possible data transformations with the aim of selecting one specific transformation that yields an adequate trade-off between data quality and privacy risk. We study the practical effectiveness of our proposal over three data formats underlying many services, defined on real mobility data, i.e., presence data, trajectory data and road segment data.Source: TRANSACTIONS ON DATA PRIVACY, vol. 11 (issue 2), pp. 139-167
Project(s): SoBigData
Pratesi F, Monreale A, Trasarti R, Giannotti F, Pedreschi D, Yanagihara T
Data describing human activities are an important source of knowledge useful for understanding individual and collective behavior and for developing a wide range of user services. Unfortunately, this kind of data is sensitive, because people's whereabouts may allow re-identification of individuals in a de-identified database. Therefore, Data Providers, before sharing those data, must apply any sort of anonymization to lower the privacy risks, but they must be aware and capable of controlling also the data quality, since these two factors are often a trade-off. In this paper we propose PRUDEnce (Privacy Risk versus Utility in Data sharing Ecosystems), a system enabling a privacy-aware ecosystem for sharing personal data. It is based on a methodology for assessing both the empirical (not theoretical) privacy risk associated to users represented in the data, and the data quality guaranteed only with users not at risk. Our proposal is able to support the Data Provider in the exploration of a repertoire of possible data transformations with the aim of selecting one specific transformation that yields an adequate trade-off between data quality and privacy risk. We study the practical effectiveness of our proposal over three data formats underlying many services, defined on real mobility data, i.e., presence data, trajectory data and road segment data.Source: TRANSACTIONS ON DATA PRIVACY, vol. 11 (issue 2), pp. 139-167
Project(s): SoBigData
See at:
CNR IRIS | ISTI Repository | www.tdp.cat | CNR IRIS
2018
Conference article
Open Access
SoBigData: social mining big data ecosystem
Giannotti F, Trasarti R, Bontcheva K, Grossi V
One of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena. Such challenge requires high-level analytics, modeling and reasoning across all the social dimensions above. There is a need to harness these opportunities for scientific advancement and for the social good, compared to the currently prevalent exploitation of big data for commercial purposes or, worse, social control and surveillance. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open ecosystem where big data and social mining research can be carried out. The SoBigData Research Infrastructure (RI) provides an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life as recorded by "big data". The research community uses the SoBigData facilities as a "secure digital wind-tunnel" for large-scale social data analysis and simulation experiments. SoBigData promotes repeatable and open science and supports data science research projects by providing: (i) an ever-growing, distributed data ecosystem for procurement, access and curation and management of big social data, to underpin social data mining research within an ethic-sensitive context; (ii) an ever-growing, distributed platform of interoperable, social data mining methods and associated skills: tools, methodologies and services for mining, analysing, and visualising complex and massive datasets, harnessing the techno-legal barriers to the ethically safe deployment of big data for social mining; (iii) an ecosystem where protection of personal information and the respect for fundamental human rights can coexist with a safe use of the same information for scientific purposes of broad and central societal interest. SoBigData has a dedicated ethical and legal board, which is implementing a legal and ethical framework.DOI: 10.1145/3184558.3186205
Project(s): SoBigData
Metrics:
Giannotti F, Trasarti R, Bontcheva K, Grossi V
One of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena. Such challenge requires high-level analytics, modeling and reasoning across all the social dimensions above. There is a need to harness these opportunities for scientific advancement and for the social good, compared to the currently prevalent exploitation of big data for commercial purposes or, worse, social control and surveillance. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open ecosystem where big data and social mining research can be carried out. The SoBigData Research Infrastructure (RI) provides an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life as recorded by "big data". The research community uses the SoBigData facilities as a "secure digital wind-tunnel" for large-scale social data analysis and simulation experiments. SoBigData promotes repeatable and open science and supports data science research projects by providing: (i) an ever-growing, distributed data ecosystem for procurement, access and curation and management of big social data, to underpin social data mining research within an ethic-sensitive context; (ii) an ever-growing, distributed platform of interoperable, social data mining methods and associated skills: tools, methodologies and services for mining, analysing, and visualising complex and massive datasets, harnessing the techno-legal barriers to the ethically safe deployment of big data for social mining; (iii) an ecosystem where protection of personal information and the respect for fundamental human rights can coexist with a safe use of the same information for scientific purposes of broad and central societal interest. SoBigData has a dedicated ethical and legal board, which is implementing a legal and ethical framework.DOI: 10.1145/3184558.3186205
Project(s): SoBigData
Metrics:
See at:
dl.acm.org | dl.acm.org | CNR IRIS | ISTI Repository | doi.org | CNR IRIS
2017
Journal article
Open Access
MyWay: location prediction via mobility profiling
Trasarti R, Guidotti R, Monreale A, Giannotti F
Forecasting the future positions of mobile users is a valuable task allowing us to operate efficiently a myriad of different applications which need this type of information. We propose MyWay, a prediction system which exploits the individual systematic behaviors modeled by mobility profiles to predict human movements. MyWay provides three strategies: the individual strategy uses only the user individual mobility profile, the collective strategy takes advantage of all users individual systematic behaviors, and the hybrid strategy that is a combination of the previous two. A key point is that MyWay only requires the sharing of individual mobility profiles, a concise representation of the user's movements, instead of raw trajectory data revealing the detailed movement of the users. We evaluate the prediction performances of our proposal by a deep experimentation on large real-world data. The results highlight that the synergy between the individual and collective knowledge is the key for a better prediction and allow the system to outperform the state-of-art methods.Source: INFORMATION SYSTEMS, vol. 64, pp. 350-367
DOI: 10.1016/j.is.2015.11.002
Project(s): SoBigData
Metrics:
Trasarti R, Guidotti R, Monreale A, Giannotti F
Forecasting the future positions of mobile users is a valuable task allowing us to operate efficiently a myriad of different applications which need this type of information. We propose MyWay, a prediction system which exploits the individual systematic behaviors modeled by mobility profiles to predict human movements. MyWay provides three strategies: the individual strategy uses only the user individual mobility profile, the collective strategy takes advantage of all users individual systematic behaviors, and the hybrid strategy that is a combination of the previous two. A key point is that MyWay only requires the sharing of individual mobility profiles, a concise representation of the user's movements, instead of raw trajectory data revealing the detailed movement of the users. We evaluate the prediction performances of our proposal by a deep experimentation on large real-world data. The results highlight that the synergy between the individual and collective knowledge is the key for a better prediction and allow the system to outperform the state-of-art methods.Source: INFORMATION SYSTEMS, vol. 64, pp. 350-367
DOI: 10.1016/j.is.2015.11.002
Project(s): SoBigData
Metrics:
See at:
Information Systems | CNR IRIS | ISTI Repository | www.sciencedirect.com | Information Systems | CNR IRIS | CNR IRIS
2017
Journal article
Open Access
HyWare: a HYbrid Workflow lAnguage for Research E-infrastructures
Candela L, Giannotti F, Grossi V, Manghi P, Trasarti R
Research e-infrastructures are "systems of systems", patchworks of tools, services and data sources, evolving over time to address the needs of the scientific process. Accordingly, in such environments, researchers implement their scientific processes by means of workflows made of a variety of actions, including for example usage of web services, download and execution of shared software libraries or tools, or local and manual manipulation of data. Although scientists may benefit from sharing their scientific process, the heterogeneity underpinning e-infrastructures hinders their ability to represent, share and eventually reproduce such workflows. This work presents HyWare, a language for representing scientific process in highly-heterogeneous e-infrastructures in terms of so-called hybrid workflows. HyWare lays in between "business process modeling languages", which offer a formal and high-level description of a reasoning, protocol, or procedure, and "workflow execution languages", which enable the fully automated execution of a sequence of computational steps via dedicated engines.Source: D-LIB MAGAZINE, vol. 23, pp. 8-11
DOI: 10.1045/january2017-candela
Project(s): SoBigData
Metrics:
Candela L, Giannotti F, Grossi V, Manghi P, Trasarti R
Research e-infrastructures are "systems of systems", patchworks of tools, services and data sources, evolving over time to address the needs of the scientific process. Accordingly, in such environments, researchers implement their scientific processes by means of workflows made of a variety of actions, including for example usage of web services, download and execution of shared software libraries or tools, or local and manual manipulation of data. Although scientists may benefit from sharing their scientific process, the heterogeneity underpinning e-infrastructures hinders their ability to represent, share and eventually reproduce such workflows. This work presents HyWare, a language for representing scientific process in highly-heterogeneous e-infrastructures in terms of so-called hybrid workflows. HyWare lays in between "business process modeling languages", which offer a formal and high-level description of a reasoning, protocol, or procedure, and "workflow execution languages", which enable the fully automated execution of a sequence of computational steps via dedicated engines.Source: D-LIB MAGAZINE, vol. 23, pp. 8-11
DOI: 10.1045/january2017-candela
Project(s): SoBigData
Metrics:
See at:
D-Lib Magazine | CNR IRIS | ISTI Repository | OpenAIRE | CNR IRIS
2017
Other
Restricted
SoBigData - VA e-Infrastructure service provision and operation report 1
Trasarti R, Pagano P, Falchi C, Grossi V, Rapisarda B
The deliverable present the status of the SoBigData platform as an evolving e-infrastructure where the partners are continuosly adding new contents and improving the presentation of them. The virtual research enviroments (VREs) already integrated will be described and monitored with a set of KPIs describing the number of access, the experiments done and the social network activities related to them. Moreover a description of the VREs which are not yet public but are under an internal review phase will be described in order to understand how the consortium is prooceding in integrating resources to the e-infrastructure. An important note is the fact that this deliverable does not contain the assessment from the Advisory Board as described in the DOW, this due the fact that the board is not yet formed. Anyway the SoBigData Platform is begin used by the partner for inserting resources only in the last 6 months and therefore it is in stage where the content vary greatly (in the last month the resources in the catalogue doubled).Project(s): SoBigData
Trasarti R, Pagano P, Falchi C, Grossi V, Rapisarda B
The deliverable present the status of the SoBigData platform as an evolving e-infrastructure where the partners are continuosly adding new contents and improving the presentation of them. The virtual research enviroments (VREs) already integrated will be described and monitored with a set of KPIs describing the number of access, the experiments done and the social network activities related to them. Moreover a description of the VREs which are not yet public but are under an internal review phase will be described in order to understand how the consortium is prooceding in integrating resources to the e-infrastructure. An important note is the fact that this deliverable does not contain the assessment from the Advisory Board as described in the DOW, this due the fact that the board is not yet formed. Anyway the SoBigData Platform is begin used by the partner for inserting resources only in the last 6 months and therefore it is in stage where the content vary greatly (in the last month the resources in the catalogue doubled).Project(s): SoBigData