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2021 Journal article Open Access OPEN

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 via OpenAIRE

See at: link.springer.com Open Access | International Journal of Data Science and Analytics Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted


2021 Conference article Open Access OPEN

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 via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2020 Journal article Open Access OPEN

Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
Nanni M., Andrienko G., Barabasi A. -l., Boldrini C., Bonchi F., Cattuto C., Chiaromonte F., Comandé G., Conti M., Coté M., Dignum F., Dignum V., Domingo-ferrer J., Ferragina P., Giannotti F., Guidotti R., Helbing D., Kaski K., Kertesz J., Lehmann S., Lepri B., Lukowicz P., Matwin S., Jimenez D., Monreale A., Morik K., Oliver N., Passarella A., Passerini A., Pedreschi D., Pentland A., Pianesi F., Pratesi F., Rinzivillo S., Ruggieri S., Siebes A., Torra V., Trasarti R., Van Den Hoven J., Vespignani A.
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: Transactions on data privacy 13 (2020): 61–66.

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.tdp.cat Open Access


2020 Journal article Open Access OPEN

(So) Big Data and the transformation of the city
Andrienko G., Andrienko N., Boldrini C., Caldarelli G., Cintia P., Cresci S., Facchini A., Giannotti F., Gionis A., Guidotti R., Mathioudakis M., Muntean C. I., Pappalardo L., Pedreschi D., Pournaras E., Pratesi F., Tesconi M., Trasarti R.
The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the "City of Citizens" thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.Source: International Journal of Data Science and Analytics (Print) 1 (2020). doi:10.1007/s41060-020-00207-3
DOI: 10.1007/s41060-020-00207-3
Project(s): SoBigData via OpenAIRE, SoBigData-PlusPlus via OpenAIRE

See at: Aaltodoc Publication Archive Open Access | HELDA - Digital Repository of the University of Helsinki Open Access | Archivio istituzionale della ricerca - Università degli Studi di Venezia Ca' Foscari Open Access | link.springer.com Open Access | International Journal of Data Science and Analytics Open Access | City Research Online Open Access | ISTI Repository Open Access | Fraunhofer-ePrints Open Access | CNR ExploRA Open Access | International Journal of Data Science and Analytics Restricted | Archivio della Ricerca - Università di Pisa Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted


2020 Conference article Open Access OPEN

Towards in-memory sub-trajectory similarity search
Alamdari I., Nanni M., Trasarti R., Pedreschi D.
Spatial-temporal trajectory data contains rich information aboutmoving objects and has been widely used for a large numberof real-world applications. However, the complexity of spatial-temporal trajectory data, on the one hand, and the fast collectionof datasets, on the other hand, has made it challenging to ef-ficiently store, process, and query such data. In this paper, wepropose a scalable method to analyze the sub-trajectory simi-larity search in an in-memory cluster computing environment.Notably, we have extended Apache Spark with efficient trajectoryindexing, partitioning, and querying functionalities to supportthe sub-trajectory similarity query. Our experiments on a realtrajectory dataset have shown the efficiency and effectiveness ofthe proposed method.Source: EDBT/ICDT 2020 Joint Conference - International Workshop in Big Mobility Data Analytics, Copenhagen, Denmark, 30th March - 2nd April, 2020
Project(s): Track and Know via OpenAIRE

See at: ceur-ws.org Open Access | CNR ExploRA Open Access


2019 Journal article Open Access OPEN

Computational modelling and data-driven techniques for systems analysis
Matwin S., Tesei L., Trasarti R.
This JIIS Special Issue aimed at bringing together contributions from academia, industry and research institutions interested in the combined application of computational modelling methods with data-driven techniques from the areas of knowledge management, data mining and machine learning. Modelling methodologies of interest included automata, agents, Petri nets, process algebras and rewriting systems. Application domains included social systems, ecology, biology, medicine, smart cities, governance, education, software engineering, and any other field that deals with complex systems and large amounts of data.Source: Journal of intelligent information systems 52 (2019): 473–475. doi:10.1007/s10844-019-00554-z
DOI: 10.1007/s10844-019-00554-z
Project(s): SoBigData via OpenAIRE

See at: Journal of Intelligent Information Systems Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | Journal of Intelligent Information Systems Restricted | Journal of Intelligent Information Systems Restricted | Journal of Intelligent Information Systems Restricted | Journal of Intelligent Information Systems Restricted | Journal of Intelligent Information Systems Restricted | Journal of Intelligent Information Systems Restricted | Journal of Intelligent Information Systems Restricted | Journal of Intelligent Information Systems Restricted


2019 Journal article Open Access OPEN

Finding roles of players in football using automatic particle swarm optimization-clustering algorithm
Behravan I., Zahiri S. H., Razavi S. M., Trasarti R.
Recently, professional team sport organizations have invested their resources to analyze their own and opponents' performance. So, developing methods and algorithms for analyzing team sports has become one of the most popular topics among data scientists. Analyzing football is hard because of its complexity, number of events in each match, and constant flow of circulation of the ball. Finding roles of players with the purpose of analyzing the performance of a team or making a meaningful comparison between players is crucial. In this article, an automatic big data clustering method, based on a swarm intelligence algorithm, is proposed to automatically cluster the data set of players' performance centers in different matches and extract different kinds of roles in football. The proposed method created using particle swarm optimization algorithm has two phases. In the first phase, the algorithm searches the solution space to find the number of clusters and, in the second phase, it finds the positions of the centroids. To show the effectiveness of the algorithm, it is tested on six synthetic data sets and its performance is compared with two other conventional clustering methods. After that, the algorithm is used to find clusters of a data set containing 93,000 objects, which are the centers of players' performance in about 4900 matches in different European leagues.Source: Big data (Online) 7 (2019): 35–56. doi:10.1089/big.2018.0069
DOI: 10.1089/big.2018.0069

See at: ISTI Repository Open Access | Big Data Restricted | Big Data Restricted | CNR ExploRA Restricted | Big Data Restricted | Big Data Restricted | Big Data Restricted | Big Data Restricted


2018 Contribution to journal Open Access OPEN

Guest editorial special issue on knowledge discovery from mobility data for intelligent transportation systems
Moreira-matias L., Gama J., Olaverri Monreal C., Nair R., Trasarti R.
Source: IEEE transactions on intelligent transportation systems (Print) 19 (2018): 3626–3629. doi:10.1109/TITS.2018.2877063
DOI: 10.1109/tits.2018.2877063

See at: ieeexplore.ieee.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | IEEE Transactions on Intelligent Transportation Systems Restricted | IEEE Transactions on Intelligent Transportation Systems Restricted | IEEE Transactions on Intelligent Transportation Systems Restricted | IEEE Transactions on Intelligent Transportation Systems Restricted | IEEE Transactions on Intelligent Transportation Systems Restricted


2018 Journal article Open Access OPEN

PRUDEnce: A system for assessing privacy risk vs utility in data sharing ecosystems
Pratesi F., Monreale A., Trasarti R., Giannotti F., Pedreschi D., Yanagihara T.
Data describing human activities are an important source of knowledge useful for understanding individual and collective behavior and for developing a wide range of user services. Unfortunately, this kind of data is sensitive, because people's whereabouts may allow re-identification of individuals in a de-identified database. Therefore, Data Providers, before sharing those data, must apply any sort of anonymization to lower the privacy risks, but they must be aware and capable of controlling also the data quality, since these two factors are often a trade-off. In this paper we propose PRUDEnce (Privacy Risk versus Utility in Data sharing Ecosystems), a system enabling a privacy-aware ecosystem for sharing personal data. It is based on a methodology for assessing both the empirical (not theoretical) privacy risk associated to users represented in the data, and the data quality guaranteed only with users not at risk. Our proposal is able to support the Data Provider in the exploration of a repertoire of possible data transformations with the aim of selecting one specific transformation that yields an adequate trade-off between data quality and privacy risk. We study the practical effectiveness of our proposal over three data formats underlying many services, defined on real mobility data, i.e., presence data, trajectory data and road segment data.Source: Transactions on data privacy 11 (2018): 139–167.
Project(s): SoBigData via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.tdp.cat Open Access


2018 Conference article Open Access OPEN

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 via OpenAIRE

See at: dl.acm.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | dl.acm.org Restricted | dl.acm.org Restricted


2017 Journal article Open Access OPEN

MyWay: location prediction via mobility profiling
Trasarti R., Guidotti R., Monreale A., Giannotti F.
Forecasting the future positions of mobile users is a valuable task allowing us to operate efficiently a myriad of different applications which need this type of information. We propose MyWay, a prediction system which exploits the individual systematic behaviors modeled by mobility profiles to predict human movements. MyWay provides three strategies: the individual strategy uses only the user individual mobility profile, the collective strategy takes advantage of all users individual systematic behaviors, and the hybrid strategy that is a combination of the previous two. A key point is that MyWay only requires the sharing of individual mobility profiles, a concise representation of the user's movements, instead of raw trajectory data revealing the detailed movement of the users. We evaluate the prediction performances of our proposal by a deep experimentation on large real-world data. The results highlight that the synergy between the individual and collective knowledge is the key for a better prediction and allow the system to outperform the state-of-art methods.Source: Information systems (Oxf.) 64 (2017): 350–367. doi:10.1016/j.is.2015.11.002
DOI: 10.1016/j.is.2015.11.002

See at: ISTI Repository Open Access | Information Systems Restricted | Information Systems Restricted | Information Systems Restricted | Information Systems Restricted | Information Systems Restricted | CNR ExploRA Restricted | Information Systems Restricted


2017 Journal article Open Access OPEN

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 via OpenAIRE

See at: D-Lib Magazine Open Access | D-Lib Magazine Open Access | D-Lib Magazine Open Access | D-Lib Magazine Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | OpenAIRE Open Access | D-Lib Magazine Open Access


2017 Report Closed Access

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 via OpenAIRE

See at: CNR ExploRA Restricted


2017 Conference article Open Access OPEN

There's a Path for Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas
Guidotti R., Trasarti R., Nanni M., Giannotti F., Pedreschi D.
The avalanche of mobility data like GPS and GSM daily produced by each user through mobile devices enables personalized mobility-services improving everyday life. The base for these mobility-services lies in the predictability of human behavior. In this paper we propose an approach for reproducing the user's personal mobility agenda that is able to predict the user's positions for the whole day. We reproduce the agenda by exploiting a data-driven personal mobility model able to capture and summarize different aspects of the systematic mobility behavior of a user. We show how the proposed approach outperforms typical methodologies adopted in the literature on four different real GPS datasets. Moreover, we analyze some features of the mobility models and we discuss how they can be employed as agents of a simulator for what-if mobility analysis.Source: 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 303–312, Tokyo, Japan, 19-21/10/2017
DOI: 10.1109/dsaa.2017.12
Project(s): SoBigData via OpenAIRE

See at: ieeexplore.ieee.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | academic.microsoft.com Restricted | core.ac.uk Restricted | dblp.uni-trier.de Restricted | Archivio della Ricerca - Università di Pisa Restricted | xplorestaging.ieee.org Restricted


2017 Journal article Open Access OPEN

Discovering and understanding city events with big data: the case of Rome
Furletti B., Trasarti R., Cintia P., Gabrielli L.
The increasing availability of large amounts of data and digital footprints has given rise to ambitious research challenges in many fields, which spans from medical research, financial and commercial world, to people and environmental monitoring. Whereas traditional data sources and census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge providing useful and hidden information to analysts and decision makers. With this paper, we focus on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study and evaluate the impact of these events over the typical city dynamics. We present an analytical process able to discover, understand and characterize city events from Call Detail Record, designing a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users. The methodology provides an useful tool for city mobility manager to manage the events and taking future decisions on specific classes of users, i.e., residents, commuters and tourists.Source: Information (Basel) 8 (2017). doi:10.3390/info8030074
DOI: 10.3390/info8030074

See at: Information Open Access | Information Open Access | Information Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | Information Open Access | Information Open Access


2017 Contribution to book Restricted

Movement behaviour recognition for water activities
Nanni M., Trasarti R., Giannotti F.
This work describes an analysis process for the movement traces of users during water activities. The data is collected by a mobile phone app that the Navionics company developed to provide to its users sea maps and navigation services. The final objective of the project is to recognize the prevalent activity types of the users (fishing, sailing, cruising, canoeing), in order to personalize services and advertising.Source: Personal Analytics and Privacy. An Individual and Collective Perspective, edited by Guidati R.; Monreale A.; Pedreschi D.; Abiteboul S., pp. 64–75, 2017
DOI: 10.1007/978-3-319-71970-2_7

See at: academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | doi.org Restricted | link.springer.com Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted


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

See at: CNR ExploRA Restricted


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

See at: CNR ExploRA Restricted


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

See at: CNR ExploRA Restricted


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

See at: CNR ExploRA Restricted