CNR Author
operator:
and / or
Typology
operator:
and / or
Language
operator:
and / or
Date
operator:
and / or
Rights
operator:
and / or
2020
Report
Open Access
SoBigData - Final dissemination and community building events
Rapisarda B., Romano V.
This report includes the description of dissemination activities and of the events organized during months 37-52 (from September 2018 (M37) to December 2019 (M52)) and assesses all the dissemination activity carried out during the project, providing overall statistics and comparisons. It also includes a complete list of publications. Furthermore, it describes the effort on communication and dissemination in order to reach as wide an audience as possible, and the multiple strategies employed by the consortium to grow and consolidate the community.Source: Project report, SoBigData, Deliverable D3.6, 2020
Project(s): SoBigData
Rapisarda B., Romano V.
This report includes the description of dissemination activities and of the events organized during months 37-52 (from September 2018 (M37) to December 2019 (M52)) and assesses all the dissemination activity carried out during the project, providing overall statistics and comparisons. It also includes a complete list of publications. Furthermore, it describes the effort on communication and dissemination in order to reach as wide an audience as possible, and the multiple strategies employed by the consortium to grow and consolidate the community.Source: Project report, SoBigData, Deliverable D3.6, 2020
Project(s): SoBigData
See at:
ISTI Repository 
| CNR ExploRA
2020
Report
Unknown
SoBigData_D3.6_654024_Final dissemination and community building events
B. Rapisarda, V. Romano
This report includes the description of dissemination activities and of the events organized during months 37-52 (from September 2018 (M37) to December 2019 (M52)) and assesses all the dissemination activity carried out during the project, providing overall statistics and comparisons. It also includes a complete list of publications. Furthermore, it describes the effort on communication and dissemination in order to reach as wide an audience as possible, and the multiple strategies employed by the consortium to grow and consolidate the community.
B. Rapisarda, V. Romano
This report includes the description of dissemination activities and of the events organized during months 37-52 (from September 2018 (M37) to December 2019 (M52)) and assesses all the dissemination activity carried out during the project, providing overall statistics and comparisons. It also includes a complete list of publications. Furthermore, it describes the effort on communication and dissemination in order to reach as wide an audience as possible, and the multiple strategies employed by the consortium to grow and consolidate the community.
See at: CNR ExploRA
2018
Report
Open Access
SoBigData - Periodic dissemination and impact report and plan for following year 2
Rapisarda B., Romano V.
This second periodic report includes the description of dissemination and impact activities undertaken and scientific papers published, as well as the planning for the next 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 March 2017 (M19) to August 2018 (M36).Source: Project report, SoBigData, Deliverable D3.5, 2018
Project(s): SoBigData
Rapisarda B., Romano V.
This second periodic report includes the description of dissemination and impact activities undertaken and scientific papers published, as well as the planning for the next 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 March 2017 (M19) to August 2018 (M36).Source: Project report, SoBigData, Deliverable D3.5, 2018
Project(s): SoBigData
See at:
ISTI Repository | CNR ExploRA
2016
Report
Unknown
PETRA - The framework for individual mobility pattern discovery and mobility diaries/activity model
Nanni M., Trasarti R., Gabrielli L., Romano V.
This document accompanies deliverable D3.4, which contains the software modules implementing the methods that form the core of the Mobility Pattern Mining module within the PETRA architecture, as presented in D2.2, devoted to deal with GPS and mobile phone (GSM) individual data. The rationale, motivations and some possible applications of such methods have been described in D3.3. The algorithms learn to identify the role or purpose of each trip or location within the history of a user, in terms of activity to be performed, whether it is a systematic trip or location, etc., and exploit such derived information for prediction purposes. The document briefly summarizes the interfaces and the functionalities provided.Source: Project report, PETRA, Deliverable D3.4, 2016
Project(s): PETRA
Nanni M., Trasarti R., Gabrielli L., Romano V.
This document accompanies deliverable D3.4, which contains the software modules implementing the methods that form the core of the Mobility Pattern Mining module within the PETRA architecture, as presented in D2.2, devoted to deal with GPS and mobile phone (GSM) individual data. The rationale, motivations and some possible applications of such methods have been described in D3.3. The algorithms learn to identify the role or purpose of each trip or location within the history of a user, in terms of activity to be performed, whether it is a systematic trip or location, etc., and exploit such derived information for prediction purposes. The document briefly summarizes the interfaces and the functionalities provided.Source: Project report, PETRA, Deliverable D3.4, 2016
Project(s): PETRA
See at: CNR ExploRA
2016
Report
Unknown
PETRA - An individual mobility pattern and diary model for smart cities
Nanni M., Trasarti R., Romano V.
This document presents the key algorithms that form the core of the Mobility Pattern Mining module within the PETRA architecture, as presented in D2.2, devoted to deal with GPS and mobile phone (GSM) individual data. The algorithms learn to identify the role or purpose of each trip or location within the history of a user, in terms of activity to be performed, whether it is a systematic trip or location, etc., and exploit such derived information for prediction purposes. This document provides some preliminaries, the rationale of the methods, highlighting the improvement over the state-of-art, and a brief summary of performances.Source: Project report, PETRA, Deliverable D3.3, 2016
Project(s): PETRA
Nanni M., Trasarti R., Romano V.
This document presents the key algorithms that form the core of the Mobility Pattern Mining module within the PETRA architecture, as presented in D2.2, devoted to deal with GPS and mobile phone (GSM) individual data. The algorithms learn to identify the role or purpose of each trip or location within the history of a user, in terms of activity to be performed, whether it is a systematic trip or location, etc., and exploit such derived information for prediction purposes. This document provides some preliminaries, the rationale of the methods, highlighting the improvement over the state-of-art, and a brief summary of performances.Source: Project report, PETRA, Deliverable D3.3, 2016
Project(s): PETRA
See at: CNR ExploRA
2016
Report
Unknown
PETRA - The simulation framework for crowd mobility behaviour
Nanni M., Trasarti R., Romano V.
This document presents the tools and framework developed within the PETRA project for simulating the mobility behaviour of crowds. The tools are mainly based on the modeling of individual users - possibly derived from real data - and allow to realize various kinds of simulations, from simple predictions over current traffic/crowd status to more involved what-if analyses. This document provides some preliminaries and the rationale of the methods, highlighting their usability over the PETRA showcases.Source: Project report, PETRA, Deliverable D3.5, 2016
Project(s): PETRA
Nanni M., Trasarti R., Romano V.
This document presents the tools and framework developed within the PETRA project for simulating the mobility behaviour of crowds. The tools are mainly based on the modeling of individual users - possibly derived from real data - and allow to realize various kinds of simulations, from simple predictions over current traffic/crowd status to more involved what-if analyses. This document provides some preliminaries and the rationale of the methods, highlighting their usability over the PETRA showcases.Source: Project report, PETRA, Deliverable D3.5, 2016
Project(s): PETRA
See at: CNR ExploRA
2016
Report
Unknown
PETRA - Methods for computing collective mobility indicator from individual patterns
Nanni M., Trasarti R., Romano V.
This document presents a set of methods for exploiting individual patterns and measures developed within WP3, and described in D3.3, to produce collective indicators. Such indicators will be used for various applications purposes, some of which are described as representative examples. This document provides preliminaries and the rationale of the methods, highlighting how they are used (or can be used) for the showcases of PETRA.Source: Project report, PETRA, Deliverable D3.6, 2016
Project(s): PETRA
Nanni M., Trasarti R., Romano V.
This document presents a set of methods for exploiting individual patterns and measures developed within WP3, and described in D3.3, to produce collective indicators. Such indicators will be used for various applications purposes, some of which are described as representative examples. This document provides preliminaries and the rationale of the methods, highlighting how they are used (or can be used) for the showcases of PETRA.Source: Project report, PETRA, Deliverable D3.6, 2016
Project(s): PETRA
See at: CNR ExploRA
2016
Report
Unknown
PETRA - Transit monitor for detecting changes in predictions
Nanni M., Trasarti R., Romano V.
This document presents the algorithms developed within PETRA to monitor the performances of the main predictive models built in the project, in order to provide feedbacks and possibly trigger updates or re-computation of the models. The algorithms are based on the comparison of the models against GPS mobility data. The document briefly summarizes the interfaces and the functionalities provided.Source: Project report, PETRA, Deliverable D5.4, 2016
Project(s): PETRA
Nanni M., Trasarti R., Romano V.
This document presents the algorithms developed within PETRA to monitor the performances of the main predictive models built in the project, in order to provide feedbacks and possibly trigger updates or re-computation of the models. The algorithms are based on the comparison of the models against GPS mobility data. The document briefly summarizes the interfaces and the functionalities provided.Source: Project report, PETRA, Deliverable D5.4, 2016
Project(s): PETRA
See at: CNR ExploRA
2016
Report
Unknown
M-Atlas 2.0: the DMQL. The new data mining query language
Romano V., Trasarti R., Ghelli G.
In the last years the researchers at KDD Lab have used the M-Atlas system for their mobility data analyses and contributed to expand the M-Atlas functions and features. After so much time, the original design of M-Atlas needed to be reviewed in order to understand how it could be adapted to improve the performances and ease the work of the researchers performing analyses. In the last months the M-Atlas Core has been almost completely redesigned in the way it connects to the PostgreSQL database and how datasets and software methods are managed. The new design of the software methods as Plug-ins makes it very simple to invoke a method. Now the old DMQL module has to be modified in order to interact with the new Core and to take advantage from the new design. Moreover, as the DMQL language can be used by data scientists to directly write DMQL code or to interact through the GUI or the Web Service which will generate DMQL code, we built a new intuitive language that was easy to learn and similar to other known languages.Source: ISTI Technical reports, 2016
Romano V., Trasarti R., Ghelli G.
In the last years the researchers at KDD Lab have used the M-Atlas system for their mobility data analyses and contributed to expand the M-Atlas functions and features. After so much time, the original design of M-Atlas needed to be reviewed in order to understand how it could be adapted to improve the performances and ease the work of the researchers performing analyses. In the last months the M-Atlas Core has been almost completely redesigned in the way it connects to the PostgreSQL database and how datasets and software methods are managed. The new design of the software methods as Plug-ins makes it very simple to invoke a method. Now the old DMQL module has to be modified in order to interact with the new Core and to take advantage from the new design. Moreover, as the DMQL language can be used by data scientists to directly write DMQL code or to interact through the GUI or the Web Service which will generate DMQL code, we built a new intuitive language that was easy to learn and similar to other known languages.Source: ISTI Technical reports, 2016
See at: CNR ExploRA
2016
Report
Restricted
Database of female scientists at the 6 targeted Departments of UNIPI
Romano V., Natilli M., Fadda D., Rossetti G., Giannotti F.
The experience gained with the several funded European projects allows us to collect data on female careers but also to identify the context (at institutional level) as a crucial factor in defining the phenomenon of gender equality. The usual approach is to perform a survey or to ask the administration in order to understand how many women are employed at the different levels of the institution at a certain time. The institution obtains a snapshot of the gender equality or, if the study is repeated regularly, a sequence of snapshots that allows gender researchers to perform comparisons and better understand the trends. The aim of the Women Scientific Career Database is to integrate the study of gender equality in the structure of the administration of the institution, in order to build a permanent gender monitor that is automatically updated by the administration. This new approach allows a real time analysis of the gender equality within the institution and, as data are continuously updated, makes it easier to verify how different strategies, laws or regulations can modify the status of gender equality. In order to better understand how the career of a researcher evolves within the institution through years, a lot of different events have to be monitored, like the type of contract and its evolution, and scientific production. The analysis provides statistics aggregated at university level, at department level and personal level in order to give a global picture of the university status and to show how different departments present different behaviors with respect to the gender inequalities. The personal level aggregation wants instead to show how real women scientist can have a successful career. This report describes the realization of the Women Scientific Career Database, the data model, the acquisition procedures and the implementation of first family of indicators and their rendering through a navigable web interface.Source: Project report, TRIGGER, Deliverable D1.8, 2016
Project(s): TRIGGER
Romano V., Natilli M., Fadda D., Rossetti G., Giannotti F.
The experience gained with the several funded European projects allows us to collect data on female careers but also to identify the context (at institutional level) as a crucial factor in defining the phenomenon of gender equality. The usual approach is to perform a survey or to ask the administration in order to understand how many women are employed at the different levels of the institution at a certain time. The institution obtains a snapshot of the gender equality or, if the study is repeated regularly, a sequence of snapshots that allows gender researchers to perform comparisons and better understand the trends. The aim of the Women Scientific Career Database is to integrate the study of gender equality in the structure of the administration of the institution, in order to build a permanent gender monitor that is automatically updated by the administration. This new approach allows a real time analysis of the gender equality within the institution and, as data are continuously updated, makes it easier to verify how different strategies, laws or regulations can modify the status of gender equality. In order to better understand how the career of a researcher evolves within the institution through years, a lot of different events have to be monitored, like the type of contract and its evolution, and scientific production. The analysis provides statistics aggregated at university level, at department level and personal level in order to give a global picture of the university status and to show how different departments present different behaviors with respect to the gender inequalities. The personal level aggregation wants instead to show how real women scientist can have a successful career. This report describes the realization of the Women Scientific Career Database, the data model, the acquisition procedures and the implementation of first family of indicators and their rendering through a navigable web interface.Source: Project report, TRIGGER, Deliverable D1.8, 2016
Project(s): TRIGGER
See at:
triggerproject.eu 
| CNR ExploRA
2015
Report
Unknown
KDD Lab: datasets metadata census
Romano V.
As many other research laboratories around the world, one of the most precious resources is information. Methods and algorithms often need datasets in order to be tested or executed. Publicly available real life datasets are rare because companies and institutions want to protect their dataset's value or the privacy of their users. In order to better manage and publish the KDD Lab's datasets we decided to define a collection of metadata fields to describe the datasets build a dataset census in order to collect the metadata about KDD Lab's datasets part of the information collected can be published on the website.Source: ISTI Technical reports, 2015
Romano V.
As many other research laboratories around the world, one of the most precious resources is information. Methods and algorithms often need datasets in order to be tested or executed. Publicly available real life datasets are rare because companies and institutions want to protect their dataset's value or the privacy of their users. In order to better manage and publish the KDD Lab's datasets we decided to define a collection of metadata fields to describe the datasets build a dataset census in order to collect the metadata about KDD Lab's datasets part of the information collected can be published on the website.Source: ISTI Technical reports, 2015
See at: CNR ExploRA
2015
Report
Unknown
KDD Lab: Methods metadata census
Romano V.
The KDD Lab's best achievements often produce software and methodologies that are useful to solve problems of to perform analysis. These methods are one of the most precious resources of the laboratory that often can lead future collaborations. In order to better manage and publish the KDD Lab's methods we decided to: define a collection of metadata fields; to describe the methods; build a methods census in order to collect the metadata about KDD Lab's methods; part of the information collected can be published on the website.Source: ISTI Technical reports, 2015
Romano V.
The KDD Lab's best achievements often produce software and methodologies that are useful to solve problems of to perform analysis. These methods are one of the most precious resources of the laboratory that often can lead future collaborations. In order to better manage and publish the KDD Lab's methods we decided to: define a collection of metadata fields; to describe the methods; build a methods census in order to collect the metadata about KDD Lab's methods; part of the information collected can be published on the website.Source: ISTI Technical reports, 2015
See at: CNR ExploRA
2015
Report
Unknown
KDD Lab: Social media communication
Romano V.
One of the most important activities in research is dissemination: the best achievement will be useless if nobody knows about it. Nowadays Social Media became a very popular communication mean that allows people to share information very quickly with people that has similar interests. Consequently a research lab like KDD Lab needs to strengthen its web communication with a powerful social media strategy. There are lots of social media platforms that can be used, but using social media implies an effort in order to give the idea of an active community. We decided to select a few very popular Social Media platforms and to design the strategy in order to reduce the effort for the editors.Source: ISTI Technical reports, 2015
Romano V.
One of the most important activities in research is dissemination: the best achievement will be useless if nobody knows about it. Nowadays Social Media became a very popular communication mean that allows people to share information very quickly with people that has similar interests. Consequently a research lab like KDD Lab needs to strengthen its web communication with a powerful social media strategy. There are lots of social media platforms that can be used, but using social media implies an effort in order to give the idea of an active community. We decided to select a few very popular Social Media platforms and to design the strategy in order to reduce the effort for the editors.Source: ISTI Technical reports, 2015
See at: CNR ExploRA
2015
Report
Unknown
KDD Lab: the new Drupal 7 website
Romano V.
Technical advancement in web applications and web formats force institutions and companies to quickly and frequently update their communication strategies. Websites, Social Media profiles, document templates should be frequently updated in order to give a positive, fresh, modern and dynamic idea of their activities. The old KDD Lab website was built using old web standards and technologies and the look was consequently a little aged. Moreover, the website was difficult to navigate on small devices like modern tablets and smartphones. The lack of a heavy structure of the information published on the website caused sporadic (unfriendly) updates and difficulties in the site searches. Only a small part of the KDD Lab activities was actually published on the website and easily searchable. A new website has been designed in order to: adopt a strong content structure; easily add and edit contents; automatically link related contents; improve site navigation; improve user interface; improve visualization on small devices.Source: ISTI Technical reports, 2015
Romano V.
Technical advancement in web applications and web formats force institutions and companies to quickly and frequently update their communication strategies. Websites, Social Media profiles, document templates should be frequently updated in order to give a positive, fresh, modern and dynamic idea of their activities. The old KDD Lab website was built using old web standards and technologies and the look was consequently a little aged. Moreover, the website was difficult to navigate on small devices like modern tablets and smartphones. The lack of a heavy structure of the information published on the website caused sporadic (unfriendly) updates and difficulties in the site searches. Only a small part of the KDD Lab activities was actually published on the website and easily searchable. A new website has been designed in order to: adopt a strong content structure; easily add and edit contents; automatically link related contents; improve site navigation; improve user interface; improve visualization on small devices.Source: ISTI Technical reports, 2015
See at: CNR ExploRA
2015
Report
Unknown
Master big data: the website
Romano V.
The Computer Science Department of Pisa University in 2015 organized the first master in Big Data in collaboration with Istituto di Scienza e Tecnologie and Istituto di Informatica e Telematica of CNR, which are members of SoBigData.it Lab. The organizers of the master in order to attract students and sponsors required the design and realization of a website as communication channel. To this end, we added a section to the SoBigData.it website [1] dedicated to the Master in Big Data. The goal of this website is to communicate to visitors: information about the Master objectives; information about the data scientist career opportunities; information about the Teaching Activities of the Master; information about the application rules to be followed to become a master's student.Source: ISTI Technical reports, 2015
Romano V.
The Computer Science Department of Pisa University in 2015 organized the first master in Big Data in collaboration with Istituto di Scienza e Tecnologie and Istituto di Informatica e Telematica of CNR, which are members of SoBigData.it Lab. The organizers of the master in order to attract students and sponsors required the design and realization of a website as communication channel. To this end, we added a section to the SoBigData.it website [1] dedicated to the Master in Big Data. The goal of this website is to communicate to visitors: information about the Master objectives; information about the data scientist career opportunities; information about the Teaching Activities of the Master; information about the application rules to be followed to become a master's student.Source: ISTI Technical reports, 2015
See at: CNR ExploRA
2015
Report
Unknown
SoBigData.it: datasets metadata census
Romano V.
SoBigData.it groups some of the Italian laboratories that are working on big data. The partners want to build a European Research Infrastructure for big data and social analytics through the European Project SoBigData.eu. One of the main bricks of a research infrastructure is a data catalog where the users of the infrastructure can search for available datasets. In order to anticipate the activities of the European project, we decided to start a census of the datasets available among the partners. We decided to define a collection of metadata fields to: describe the datasets; build a dataset census in order to collect the metadata about datasets; part of the information collected can be published on the website.Source: ISTI Technical reports, 2015
Romano V.
SoBigData.it groups some of the Italian laboratories that are working on big data. The partners want to build a European Research Infrastructure for big data and social analytics through the European Project SoBigData.eu. One of the main bricks of a research infrastructure is a data catalog where the users of the infrastructure can search for available datasets. In order to anticipate the activities of the European project, we decided to start a census of the datasets available among the partners. We decided to define a collection of metadata fields to: describe the datasets; build a dataset census in order to collect the metadata about datasets; part of the information collected can be published on the website.Source: ISTI Technical reports, 2015
See at: CNR ExploRA
2015
Report
Unknown
SoBigData.it: methods metadata census
Romano V.
SoBigData.it groups some of the Italian laboratories that are working on big data. The partners want to build a European Research Infrastructure for big data and social analytics through the European Project SoBigData.eu. One of the main bricks of a research infrastructure is a method's catalog where the users of the infrastructure can search for available methods. In order to anticipate the activities of the European project, we decided to start a census of the methods available among the partners. We decided to: define a collection of metadata fields; to describe the methods; build a method's census in order to collect the metadata about methods; part of the information collected can be published on the website.Source: ISTI Technical reports, 2015
Romano V.
SoBigData.it groups some of the Italian laboratories that are working on big data. The partners want to build a European Research Infrastructure for big data and social analytics through the European Project SoBigData.eu. One of the main bricks of a research infrastructure is a method's catalog where the users of the infrastructure can search for available methods. In order to anticipate the activities of the European project, we decided to start a census of the methods available among the partners. We decided to: define a collection of metadata fields; to describe the methods; build a method's census in order to collect the metadata about methods; part of the information collected can be published on the website.Source: ISTI Technical reports, 2015
See at: CNR ExploRA
2015
Report
Unknown
SoBigData.it: the website
Romano V.
As the SoBigData.it partners want to be one of the leading European laboratories on Big Data and Social Mining, having a website containing some information is a must. In order to quickly build a modern and dynamic website, we decided to use Apache and Drupal using the internal competences. A new website has been designed in order to adopt a strong content structure easily add and edit contents automatically link related contents intuitive site navigation intuitive user interface good visualization on small devices.Source: ISTI Technical reports, 2015
Romano V.
As the SoBigData.it partners want to be one of the leading European laboratories on Big Data and Social Mining, having a website containing some information is a must. In order to quickly build a modern and dynamic website, we decided to use Apache and Drupal using the internal competences. A new website has been designed in order to adopt a strong content structure easily add and edit contents automatically link related contents intuitive site navigation intuitive user interface good visualization on small devices.Source: ISTI Technical reports, 2015
See at: CNR ExploRA
2015
Report
Unknown
TagMyDay
Romano V.
One of the main research topics of the KDD Lab is about understanding human mobility and, in particular, how humans behave in a city. These studies are usually based on the analysis of GPS or GSM tracks that describe with high accuracy the movements of the subjects around the city. This kind of analysis helps to infer multiple "hidden" details like where is the subject's home and where he works. Other types of destinations of the human movements are a little bit harder to detect, as well as the transportation mean used. In order to build a dataset of the human mobility that includes the type of destination and the transportation mean used, we decided to ask directly the subjects to tag their movements. Thus we wanted to build an integrated system that allows people to voluntarily participate to a data collecting project to: register, track their movements, send their tracks, tag their movements.Source: ISTI Technical reports, 2015
Romano V.
One of the main research topics of the KDD Lab is about understanding human mobility and, in particular, how humans behave in a city. These studies are usually based on the analysis of GPS or GSM tracks that describe with high accuracy the movements of the subjects around the city. This kind of analysis helps to infer multiple "hidden" details like where is the subject's home and where he works. Other types of destinations of the human movements are a little bit harder to detect, as well as the transportation mean used. In order to build a dataset of the human mobility that includes the type of destination and the transportation mean used, we decided to ask directly the subjects to tag their movements. Thus we wanted to build an integrated system that allows people to voluntarily participate to a data collecting project to: register, track their movements, send their tracks, tag their movements.Source: ISTI Technical reports, 2015
See at: 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