References and citations of Snap4City and Km4City: former page


Warning message

You can't delete this newsletter because it has not been sent to all its subscribers.

see the Citations of Snap4City as a list of articles published, last page

See also last references in page:


C. Badii, P. Bellini, D. Cenni, A. Difino, P. Nesi, M. Paolucci, Analysis and Assessment of a Knowledge Based Smart City Architecture Providing Service APIs, Future Generation Computer Systems, Elsevier.

 Daniele Cenni, Paolo Nesi, Gianni Pantaleo and Imad Zaza. Twitter Vigilance: a Multi-User platform for Cross-Domain Twitter Data Analytics, NLP and Sentiment Analysis, IEEE international Conference on Smart City and Innovation, 2017, San Francisco.

Claudio Badii, Pierfrancesco Bellini, Paolo Nesi, Michela Paolucci. A Smart City Development kit for designing Web and Mobile Apps, IEEE international Conference on Smart City and Innovation, 2017, San Francisco.

P. Bellini, P. Nesi, "Assessing RDF Graph Databases for Smart City Services", The 23rd International Conference on Distributed Multimedia Systems, DMS 2017, Pittsburg, USA, 2017.

C. Badii, P.Bellini, D.Cenni, A. Difino, P. Nesi, M. Paolucci, "User Engagement Engine for Smart City Strategies", IEEE International Conference on Smart Computing, IEEE SMARCOMP 2017, Hong Kong.

C. Badii, E. Bellini, P. Bellini, D. Cenni, A. Difino, P. Nesi, M. Paolucci, "Km4City: una soluzione aperta per erogare servizi Smart City", GARR Conference 2016. (in Italiano)

D. Cenni, P. Nesi, G. Pantaleo, I. Paoli, I. Zaza, Twitter Vigilance: Modelli e Strumenti per l’Analisi e lo Studio di Dati Social Media ed il Monitoraggio in Real Time", GARR Conference 2016. (in Italiano)

P. Bellini, D. Cenni, P. Nesi, "AP Positioning for Estimating People Flow as Origin Destination Matrix for Smart Cities",  The 22nd International Conference on Distributed Multimedia Systems, DMS 2016, Italy, (in press), 2016.

P. Nesi, G. Pantaleo, M. Tenti, "Geographical Localization of Web-Visible Human Activities by employing Natural Language Processing, Pattern Matching and Clustering Based Solutions",  Journal: Engineering Applications of Artificial Intelligence, Elsevier. 10.1016/j.engappai.2016.01.011

P. Bellini, I. Bruno, P. Nesi, N. Rauch, "Graph Databases Methodology and Tool Supporting Index/Store Versioning", JVLC, Journal of Visual Languages and Computing, Elsevier, 2015 doi:10.1016/j.jvlc.2015.10.018 

P. Nesi, G. Pantaleo and G. Sanesi, "A Hadoop Based Platform for Natural Language Processing of Web Pages and Documents", JVLC, Journal of Visual Languages and Computing, Elsevier. 11-11-2015,

P. Bellini, M. Benigni, R. Billero, P. Nesi and N. Rauch, "Km4City Ontology Bulding vs Data Harvesting and Cleaning for Smart-city Services", International Journal of Visual Language and Computing, Elsevier, 2014,

P. Bellini, P. Nesi, A. Venturi, "Linked Open Graph: browsing multiple SPARQL entry points to build your own LOD views", International Journal of Visual Language and Computing, Elsevier, 2014, DOI information:  ,

E. Bellini, P. Nesi, G. Pantaleo, A. Venturi, "Functional Resonance Analysis Method based Decision Support tool for Urban Transport System Resilience Management", second IEEE International Smart Cities Conference (ISC2 2016), 12 to 15 September 2016, Trento, Italy, SLIDES

C. Badii, P. Bellini, D. Cenni, G. Martelli, P. Nesi, M. Paolucci, "Km4City Smart City API: an integrated support for mobility services", 2nd IEEE International Conference on Smart Computing (SMARTCOMP 2016), St. Louis, Missouri, USA, 18-20 May 2016.

P. Bellini, P. Nesi and G. Pantaleo, "Benchmarking RDF Stores for Smart City Services," 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), Chengdu, 2015, pp. 46-49, December 2015, Cina, IEEE press. doi: 10.1109/SmartCity.2015.45

M. Bartolozzi, P. Bellini, P. Nesi, G. Pantaleo and L. Santi, "A Smart Decision Support System for Smart City," 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), Chengdu, 2015, pp. 117-122, December 2015, Cina, IEEE press, doi: 10.1109/SmartCity.2015.57

·         SMAU:
· Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services
·         Semantic 2016:
·         FORUM PA challenge:
·         FORUM pa big data
·         Slide Share:
·         Maker Fair 2016: Meccanismo complesso:
·         ATAF:
·         Bartoc km4city, the DISIT Knowledge Model for City and Mobility
· Nesi ci parla di KM4City
·         GARR tv:
·         GARR tv:
·         Ontologia
· Open data, una mole di cifre in cerca di valore. Nasce Km4City per favorire l’interoperabilità
· Big data, a Firenze l’app Km4City per la smart governance urbana
·         INTOSCANA:
· km4city, the DISIT Knowledge Model for City and Mobility (km4c)
· Big Data, come creare servizi per il territorio anche dai dati non eterogenei
· Data, dall’Università di Firenze nuove soluzioni tecnologiche e un master
· Smart City and Big Data 2015
· Semantic Web and Open Data
·         Km4City, l’app toscana che ha conquistato la Spagna:
· Km4City: Accesso Semplice a


Km4City Smart City API: an integrated support for mobility servicesThe main technical issues regarding smart city solutions are related todata gathering, aggregation, reasoning, access, and service delivering via Smart City APIs (Application Program Interfaces). Aggregated and re-conciliated data (open and private, static and real time) should be exploitable by reasoning/smart algorithms for enabling sophisticated service delivering. Different kinds of Smart City APIs enable Smart City Services and Applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators. To this end, a comparison of the state of the art solutions for data aggregation was performed, by putting in evidence the needs of semantic interoperable aggregated data, to provide smart services. This paper presents the work performed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with services. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation and service production. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Finally, the API consumption related data in the recent period are presented.

Km4City as Smart City Semantic Model and Tools (submitted for Open Track)Km4City is partecipating at the Open Challenge of 14th International Semantic Web Conference (ISWC2015) Bethlehem, PA, USA, October 11-12, 2015 !!!! Abstract. our cities are not so smart as they could be Many times on traveling we realize the needs of getting more specific information that what we can get from Goole, OpenStreet Map, Here, TomTom, etc. In most cases, the information is too complex and expensive for them to be profitable. This is true since the information is in the hands of public administrations, mobility operators, private services, parking companies, etc. Km4city provides a unique point of access for interoperable data of a city metropolitan area via web and mobile applications. Km4City covers aspects of mobility and transport, energy, banks, parking, commercial, culture, bike paths, garden areas, health, tourism, end much more. Florence area in Italy has, since July 2015, a demonstrator of this solution.

Km4City White Paper: Production tools for Smart City, from data to services for citizens and companiesIn the Smart City context, hundreds of data sets are available. Many of them are open data, accessible from local, regional, national, European public administrations, national institute of statistics, etc. These data can be static, statistical data or real time. In addition, many other data are produced by other institutions like Europeana, ECLAP, Getty, Voc, dbPedia, etc. Typically most of the data are geolocated and can be accessed as files in various formats (CSV, XLS, KMZ, JSON, XML, HTML, MySQL, ZIP, LSMA, SHP, etc.), other are accessible as Linked Data, Linked Open Data or via RDF Store end points (see dpPedia, Europeana, Senate of the Republic, Chamber of Deputies, ECLAP, km4city, etc.). Personal, private and critical data can be added to these open data. Some private data are produced by companies, like for example the position of car sharing vehicles, the position of taxis, busses, flows in the city, energy consumption data in a neighborhood, etc. Many of these data can be useful for public administrations to take decisions and to provide services. Personal data are related to a person, include personal identifications, the position of the person, its profile, etc., and need to be managed in accordance with terms of use and privacy policies. Finally, critical and personal data may be used by bad-intentioned to take actions against citizens security and infrastructures, and thus licensing and conditional access solutions are adopted. Data are typically produced by central data producers, and many of these can provide their data in different ways and formats. Among them: traffic management systems, fleet management, LTZ management, hospitals, weather, social network, etc. These data have to be accessible by an aggregator that makes queries, understanding and data integration. This is not a trivial operation since it implies the semantic understanding of the data that have to be uniformed in a single data model. A single and unified model of aggregated data allows making integrated queries to provide these data via API, and the possibility to realize services and applications. Examples of services could be those that allow geographical search, the production of suggestions based on statistical evaluations, geographic structure, similarities, etc., also on the basis of citizen behaviors on the city and with respect to the available services. Data aggregation and provision services enable the development of apps for tourism, cultural heritage, transport and mobility, personal services, wellness, energy saving, etc. actually these opportunities are difficult to be exploited for public administrations and companies. Mainly obstacles are related to the high costs of data integration and aggregation, due the limited interoperability among data that are produced in different periods by different entities and companies.

Ontology Construction and Knowledge Base Feeding and Cleaning for Smart-city Services

Presently a very large number of public and private data sets are available around the local governments. In most cases, they are not semantically interoperable and a huge human effort is needed to create integrated ontologies and knowledge base for smart city. Smart City ontology are not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation and the management of the complexity. In this paper, a system for the ingestion of data for smart city related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows to manage a big volume of data coming from a variety of sources considering both static and dynamic data, this data is then mapped to a smart-city and mobility ontology and stored into an RDF-Store where this data are available for applications via SPARQL queries to provide new services to the users. The paper presents the process adopted to produce the ontology and the knowledge base and the mechanisms adopted for the verification, reconciliation and validation. Some examples about the possible usage of the coherent knowledge base produced are also offered and are accessible from the RDF-Store. Keywords— Smart city, knowledge base construction, reconciliation, validation and verirication of knowledge base, linked open graph.