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2016 Report Restricted

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 via OpenAIRE

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2016 Report Restricted

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 via OpenAIRE

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2016 Report Restricted

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 via OpenAIRE

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2016 Report Restricted

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 via OpenAIRE

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2016 Report Restricted

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 via OpenAIRE

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2016 Report Restricted

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

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2015 Report Restricted

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

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2015 Report Restricted

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

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2015 Report Restricted

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

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2015 Report Restricted

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

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2015 Report Restricted

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

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2015 Report Restricted

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

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2015 Report Restricted

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

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2015 Report Restricted

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

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2015 Report Restricted

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

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2015 Report Restricted

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

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2015 Report Restricted

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

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2015 Report Restricted

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

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2015 Report Restricted

M-Atlas 2.0: the architecture
Romano V., Trasarti R.
M-Atlas is an analytical platform for spatio-temporal data using data mining algorithms developed in the KDD Lab. After the first release in 2008, it has been used in several industrial and research projects and it has been in its functions and features. This technical report describes the project of renewing of the M-Atlas according to all the past experiences and the new needs of the Lab. Following we will refer to "original M-Atlas" for the first release, and M-Atlas as the new one. The new platform will be integrated in the SoBigData.eu Research Infrastructure, the ambitious EU project lead by Fosca Giannotti which aims to become the point of reference for Big Data and Social Mining in the European Union.Source: ISTI Technical reports, 2015

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