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2019
Report
Open Access
SoBigData - Sustainability Plan
Grossi V., Rapisarda B.
This report provides a detailed sustainability plan for the pan-European SoBigData Research Infrastructure. The purpose of this document is to describe sustainability activities, actions and events that have been undertaken in order to provide some guidelines about SoBigData's governance and long-term sustainability. Our description involves a detailed overview of the main steps in order to evaluate the impact of sustainability-related actions which have been undertaken in the SoBigData project.Source: Project report, SoBigData, Deliverable D3.7, 2019
Project(s): SoBigData
Grossi V., Rapisarda B.
This report provides a detailed sustainability plan for the pan-European SoBigData Research Infrastructure. The purpose of this document is to describe sustainability activities, actions and events that have been undertaken in order to provide some guidelines about SoBigData's governance and long-term sustainability. Our description involves a detailed overview of the main steps in order to evaluate the impact of sustainability-related actions which have been undertaken in the SoBigData project.Source: Project report, SoBigData, Deliverable D3.7, 2019
Project(s): SoBigData
See at:
ISTI Repository 
| CNR ExploRA
2020
Report
Open Access
SoBigData_training activities-planning material and reports 2
Wright J., Braghieri M., Rapisarda B.
Deliverable D4.3 provides an overview of training activities that have taken place from M36 to M52. It also includes a full overview of the training materials integration within the SoBigData Research Infrastructure. This final deliverable aims to provide a more detailed look into the various activities that have taken place since September 2018 and in particular to provide comprehensive reports on the events that have addressed gender and diversity issues. The whole project has sought to encourage more females into Computer Science and has therefore tailored some events to appeal to females in particular. SoBigData has also provided travel grants to assist female scientists in attending international events. With regards to the training materials, we have sought to make these a more effective resource by concentrating on four areas of improvement: Content Harmonization, Muti-Channel Content, Interactivity and Audience Targeting. These enhancements to the SoBigData Research Infrastructure will be covered in detail in Section 4. Discussions are also taking place with regards to the possibility of webinars, tutorials and video lectures to provide more options for the user as well as developing online assessment and exercises. The possibility of more targeted training paths is also being explored to provide a more tailored content for the user - whether they be a student, professional or the general public; the objective being to promote the dissemination of SoBigData training materials as widely as possible.Source: Project report, SoBigData, Deliverable D4.3, 2020
Project(s): SoBigData
Wright J., Braghieri M., Rapisarda B.
Deliverable D4.3 provides an overview of training activities that have taken place from M36 to M52. It also includes a full overview of the training materials integration within the SoBigData Research Infrastructure. This final deliverable aims to provide a more detailed look into the various activities that have taken place since September 2018 and in particular to provide comprehensive reports on the events that have addressed gender and diversity issues. The whole project has sought to encourage more females into Computer Science and has therefore tailored some events to appeal to females in particular. SoBigData has also provided travel grants to assist female scientists in attending international events. With regards to the training materials, we have sought to make these a more effective resource by concentrating on four areas of improvement: Content Harmonization, Muti-Channel Content, Interactivity and Audience Targeting. These enhancements to the SoBigData Research Infrastructure will be covered in detail in Section 4. Discussions are also taking place with regards to the possibility of webinars, tutorials and video lectures to provide more options for the user as well as developing online assessment and exercises. The possibility of more targeted training paths is also being explored to provide a more tailored content for the user - whether they be a student, professional or the general public; the objective being to promote the dissemination of SoBigData training materials as widely as possible.Source: Project report, SoBigData, Deliverable D4.3, 2020
Project(s): SoBigData
See at:
ISTI Repository | CNR ExploRA
2021
Contribution to journal
Open Access
Introduction to the special issue on social mining and big data ecosystem for open, responsible data science
Pappalardo L., Grossi V., Pedreschi D.
Source: International Journal of Data Science and Analytics (Online) (2021). doi:10.1007/s41060-021-00253-5
DOI: 10.1007/s41060-021-00253-5
Project(s): SoBigData, SoBigData-PlusPlus
Metrics:
Pappalardo L., Grossi V., Pedreschi D.
Source: International Journal of Data Science and Analytics (Online) (2021). doi:10.1007/s41060-021-00253-5
DOI: 10.1007/s41060-021-00253-5
Project(s): SoBigData
, SoBigData-PlusPlus Metrics:
See at:
link.springer.com | ISTI Repository | International Journal of Data Science and Analytics 
| International Journal of Data Science and Analytics | CNR ExploRA
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: SEBD 2022 - The 30th Italian Symposium on Advanced Database Systems, 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: SEBD 2022 - The 30th Italian Symposium on Advanced Database Systems, pp. 125–132, Tirrenia (PI), Italy, 19-22/06/2022
Project(s): SoBigData-PlusPlus
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2015
Report
Unknown
SoBigData - Fact sheets aimed at different stakeholders
Grossi V., Rapisarda B., Romano V.
This document reports a first stakeholder analysis conducted by the SoBigData consortium among its partners. During the first three months of the project, a set of shared documents has been defined and filled in order to provide an initial stakeholder analysis. On the one hand, the aim was to discover and take a census about the stakeholders already involved in the consortium. On the other hand, the goal was to identify a set of potential stakeholders starting from the activities provided by the partners. The aim is to understand who are the stakeholders already involved and how their needs are addressed and to identify a set of potential stakeholders starting from the activities provided by the partners. This analysis is required for writing a set of initial fact sheets. Currently, for this first deliverable, three fact sheet has been created: the first one is designed for the economy/business application field, the second one is conceived for computer science while the last one is targeted to social science application field. These fact sheets are published on the project web-site for downloading by users and others. Moreover, the fact sheets serve as hand-outs at events such as workshops and conferences. The fact sheets will be produced and updated continuously throughout the project.Source: Project report, SoBigData, Deliverable D3.2, 2015
Grossi V., Rapisarda B., Romano V.
This document reports a first stakeholder analysis conducted by the SoBigData consortium among its partners. During the first three months of the project, a set of shared documents has been defined and filled in order to provide an initial stakeholder analysis. On the one hand, the aim was to discover and take a census about the stakeholders already involved in the consortium. On the other hand, the goal was to identify a set of potential stakeholders starting from the activities provided by the partners. The aim is to understand who are the stakeholders already involved and how their needs are addressed and to identify a set of potential stakeholders starting from the activities provided by the partners. This analysis is required for writing a set of initial fact sheets. Currently, for this first deliverable, three fact sheet has been created: the first one is designed for the economy/business application field, the second one is conceived for computer science while the last one is targeted to social science application field. These fact sheets are published on the project web-site for downloading by users and others. Moreover, the fact sheets serve as hand-outs at events such as workshops and conferences. The fact sheets will be produced and updated continuously throughout the project.Source: Project report, SoBigData, Deliverable D3.2, 2015
See at: CNR ExploRA
2015
Report
Unknown
SoBigData - Project web site, project presentation, and social media presence
Grossi V., Rapisarda B., Romano V.
This report provides a full description of the main dissemination channels of the SoBigData project. On the one hand, we have classical media, such as web site while on the other hand we have a massive presence on social media, such as Twitter and Facebook. In this perspective, we can state that the project includes a wide range of dissemination channels from a website to a strong social media presence, including media and specialized journal. The project also has a wikitype environment for internal communication among the project partners. Furthermore, a project portal and management platform have been built to assist project planning and monitoring, and to allow discussion and exchange of draft documents between partners. The aim of this deliverable is to provide an overview of all dissemination channels. This document also describe the project logo and the project presentation. The project web site, presentation, and social media presence will be updated continuously throughout the project lifespan.Source: Project report, SoBigData, Deliverable D3.1, 2015
Grossi V., Rapisarda B., Romano V.
This report provides a full description of the main dissemination channels of the SoBigData project. On the one hand, we have classical media, such as web site while on the other hand we have a massive presence on social media, such as Twitter and Facebook. In this perspective, we can state that the project includes a wide range of dissemination channels from a website to a strong social media presence, including media and specialized journal. The project also has a wikitype environment for internal communication among the project partners. Furthermore, a project portal and management platform have been built to assist project planning and monitoring, and to allow discussion and exchange of draft documents between partners. The aim of this deliverable is to provide an overview of all dissemination channels. This document also describe the project logo and the project presentation. The project web site, presentation, and social media presence will be updated continuously throughout the project lifespan.Source: Project report, SoBigData, Deliverable D3.1, 2015
See at: CNR ExploRA
2015
Report
Unknown
SoBigData - Data management report
Grossi V., Romano V., Trasarti R.
This deliverable describes a web content that provides an ongoing and up to date wiki containing the description of the datasets available in the consortium. The description includes statistics, metadata, sharing policies and archiving technologies as well as the preservation provisions and lifespan. For doing that a set of relevant metadata has been defined in order to provide an homogeneous view of the datasets. The defined set of metadata will be useful also for making the datasets available into the RI. In this perspective, this deliverable represents a first definition of the metadata for describing a dataset that will be available into the RI. Furthermore, this document presents the web form to insert a description of a new dataset, and the wiki page containing the list of the datasets available among the partners,. The proposed wiki page shows a set of relevant information, such as the name of dataset, the accessibility policy, the reference partner. Finally, this document provides a first census of the datasets available in the consortium.Source: Project report, SoBigData, Deliverable D8.1, 2015
Grossi V., Romano V., Trasarti R.
This deliverable describes a web content that provides an ongoing and up to date wiki containing the description of the datasets available in the consortium. The description includes statistics, metadata, sharing policies and archiving technologies as well as the preservation provisions and lifespan. For doing that a set of relevant metadata has been defined in order to provide an homogeneous view of the datasets. The defined set of metadata will be useful also for making the datasets available into the RI. In this perspective, this deliverable represents a first definition of the metadata for describing a dataset that will be available into the RI. Furthermore, this document presents the web form to insert a description of a new dataset, and the wiki page containing the list of the datasets available among the partners,. The proposed wiki page shows a set of relevant information, such as the name of dataset, the accessibility policy, the reference partner. Finally, this document provides a first census of the datasets available in the consortium.Source: Project report, SoBigData, Deliverable D8.1, 2015
See at: CNR ExploRA
2008
Conference article
Restricted
Ontological Support For Association Rule Mining
Bellandi A., Furletti B., Grossi V., Romei A.
This paper describes some improvements of our previous work that realizes an integrated framework for extracting constraint based multilevel association rules with an ontology support. The ontology is not the repository of the data, but it models the application domain describing the metadata. Furthermore, it permits to focus the analysis only on a subset of data and to express multi level constraints on them. In this context, we report some theoretical notion already introduced and a detailed description of the recent improvements: the introduction of the object properties in the framework, and the implementation of an user interface.Source: 26th IASTED International Conference on Artificial Intelligence and Applications, 2008, pp. 110–115, Innsbruck, Austria, 11-13 February 2008
Bellandi A., Furletti B., Grossi V., Romei A.
This paper describes some improvements of our previous work that realizes an integrated framework for extracting constraint based multilevel association rules with an ontology support. The ontology is not the repository of the data, but it models the application domain describing the metadata. Furthermore, it permits to focus the analysis only on a subset of data and to express multi level constraints on them. In this context, we report some theoretical notion already introduced and a detailed description of the recent improvements: the introduction of the object properties in the framework, and the implementation of an user interface.Source: 26th IASTED International Conference on Artificial Intelligence and Applications, 2008, pp. 110–115, Innsbruck, Austria, 11-13 February 2008
See at:
dl.acm.org | CNR ExploRA
2007
Conference article
Unknown
Ontology-Driven Association Rule Extraction: A Case Study
Bellandi A., Furletti B., Grossi V., Romei A.
This paper proposes an integrated framework for extracting Constraint-based Multi-level Association Rules with an ontology support. The system permits the definition of a set of domain-specific constraints on a specific domain ontology, and to query the ontology for filtering the instances used in the association rule mining process. This method can improve the quality of the extracted associations rules in terms of relevance and understandability.Source: C&O:RR-2007 - International Workshop on Contexts and Ontologies: Representation and Reasoning, pp. 10–19, Roskilde University, Denmark, 21 August 2007
Project(s): MUSING
Bellandi A., Furletti B., Grossi V., Romei A.
This paper proposes an integrated framework for extracting Constraint-based Multi-level Association Rules with an ontology support. The system permits the definition of a set of domain-specific constraints on a specific domain ontology, and to query the ontology for filtering the instances used in the association rule mining process. This method can improve the quality of the extracted associations rules in terms of relevance and understandability.Source: C&O:RR-2007 - International Workshop on Contexts and Ontologies: Representation and Reasoning, pp. 10–19, Roskilde University, Denmark, 21 August 2007
Project(s): MUSING
See at: CNR ExploRA
2017
Report
Unknown
SoBigData - Periodic dissemination and impact report and plan for following year 1
Grossi V., Rapisarda B., Falchi C.
This first periodic report includes the description of dissemination and impact activities undertaken and scientific papers published, as well as the planning for the second period. Furthermore, It describes the efforts to be made to reach as wide an audience as possible, and the multiple strategies employed by the consortium. This deliverable covers a time period starting from December 2015 (M4) to February 2017 (M18).Source: Project report, SoBigData, Deliverable D3.4, 2017
Project(s): SoBigData
Grossi V., Rapisarda B., Falchi C.
This first periodic report includes the description of dissemination and impact activities undertaken and scientific papers published, as well as the planning for the second period. Furthermore, It describes the efforts to be made to reach as wide an audience as possible, and the multiple strategies employed by the consortium. This deliverable covers a time period starting from December 2015 (M4) to February 2017 (M18).Source: Project report, SoBigData, Deliverable D3.4, 2017
Project(s): SoBigData
See at: CNR ExploRA
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.Source: 27th International World Wide Web, WWW 2018, pp. 437–438, Lyon, France, 23-27/04/2018
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.Source: 27th International World Wide Web, WWW 2018, pp. 437–438, Lyon, France, 23-27/04/2018
DOI: 10.1145/3184558.3186205
Project(s): SoBigData
Metrics:
See at:
dl.acm.org | dl.acm.org | ISTI Repository | doi.org | CNR ExploRA
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 (Online) (2021). doi:10.1007/s41060-020-00237-x
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 (Online) (2021). doi:10.1007/s41060-020-00237-x
DOI: 10.1007/s41060-020-00237-x
Project(s): SoBigData
Metrics:
See at:
link.springer.com | International Journal of Data Science and Analytics | ISTI Repository | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics | CNR ExploRA
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.Source: FRAME'21 - 1st Workshop on Flexible Resource and Application Management on the Edge, Virtual Event, Sweden, 25/06/2021
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.Source: FRAME'21 - 1st Workshop on Flexible Resource and Application Management on the Edge, Virtual Event, Sweden, 25/06/2021
DOI: 10.1145/3452369.3463820
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | CNR ExploRA
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: SEBD 2022 - The 30th Italian Symposium on Advanced Database Systems, 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: SEBD 2022 - The 30th Italian Symposium on Advanced Database Systems, pp. 117–124, Tirrenia (PI), Italy, 19-22/06/2022
Project(s): SoBigData-PlusPlus
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2016
Report
Open Access
SoBigData - Data processing workflow specification language
Candela L., Giannotti F., Grossi V., Manghi P., Trasarti R.
This document contains a general overview of the workflow language definition status.Source: Project report, SoBigData, Deliverable D10.11, 2016
Project(s): SoBigData
Candela L., Giannotti F., Grossi V., Manghi P., Trasarti R.
This document contains a general overview of the workflow language definition status.Source: Project report, SoBigData, Deliverable D10.11, 2016
Project(s): SoBigData
See at:
ISTI Repository | CNR ExploRA
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 23 (2017): 8–11. doi:10.1045/january2017-candela
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 23 (2017): 8–11. doi:10.1045/january2017-candela
DOI: 10.1045/january2017-candela
Project(s): SoBigData
Metrics:
See at:
D-Lib Magazine | ISTI Repository | OpenAIRE | CNR ExploRA
2017
Report
Unknown
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).Source: Project report, SoBigData, Deliverable D7.1, 2017
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).Source: Project report, SoBigData, Deliverable D7.1, 2017
Project(s): SoBigData
See at: CNR ExploRA
2021
Journal article
Open Access
Data Science: a game changer for science and innovation
Grossi V., Giannotti F., Pedreschi D., Manghi P., Pagano P., Assante M.
This paper shows data science's potential for disruptive innovation in science, industry, policy, and people's lives. We present how data science impacts science and society at large in the coming years, including ethical problems in managing human behavior data and considering the quantitative expectations of data science economic impact. We introduce concepts such as open science and e-infrastructure as useful tools for supporting ethical data science and training new generations of data scientists. Finally, this work outlines SoBigData Research Infrastructure as an easy-to-access platform for executing complex data science processes. The services proposed by SoBigData are aimed at using data science to understand the complexity of our contemporary, globally interconnected society.Source: International Journal of Data Science and Analytics (Print) 11 (2021): 263–278. doi:10.1007/s41060-020-00240-2
DOI: 10.1007/s41060-020-00240-2
Metrics:
Grossi V., Giannotti F., Pedreschi D., Manghi P., Pagano P., Assante M.
This paper shows data science's potential for disruptive innovation in science, industry, policy, and people's lives. We present how data science impacts science and society at large in the coming years, including ethical problems in managing human behavior data and considering the quantitative expectations of data science economic impact. We introduce concepts such as open science and e-infrastructure as useful tools for supporting ethical data science and training new generations of data scientists. Finally, this work outlines SoBigData Research Infrastructure as an easy-to-access platform for executing complex data science processes. The services proposed by SoBigData are aimed at using data science to understand the complexity of our contemporary, globally interconnected society.Source: International Journal of Data Science and Analytics (Print) 11 (2021): 263–278. doi:10.1007/s41060-020-00240-2
DOI: 10.1007/s41060-020-00240-2
Metrics:
See at:
link.springer.com | ISTI Repository | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics | CNR ExploRA