Basi di dati, SQL
Casi: Analisi della struttura di documenti
Intelligenza Artificiale
Apprendimento, clustering, alberi decisionali
Web crawling, XML, analisi dei testo
Casi di studio: OSIM
Reasoning and inferential
Big data introduction: NoSQL, graph database, ..
Social Media: user profiling, recommendations
Caso di Studio: Twitter Vigilance, sentiment analysis
Architetture parallele
Casi di studio: smart city
Casi di studio: Smart Cloud
Open Data Day 2016, Km4City, L’universita’ come aggregatore di Open Data del ...Paolo Nesi
Open Data Day, UNIMORE, Modena, 5 Marzo 2016.
Aggregazione dati, experienza di Firenze,
Smart City, Km4City,
Smart Decision Support,
Data Ingestion manager,
Data aggregation,
User profiling on demand.
Mobilità: inter-modalità, bigliettazione integrata, sostenibile, scambiatori, sfruttamento stazioni, etc.,
Servizi: gov ..SUAP, edu, turismo, beni culturali, salute, etc.,
Energia: risparmio energetico, riduzione amissioni, inquinamento, etc.,
Ambiente: qualità dell’aria, fiumi, meteo, rifiuti, etc.,
… commercio, industria, etc.
... Infrastrutture critiche. resilienza
Collezionamento dati statici, quasi statici e real time, stream
Dati open: geo localizzati, servizi, statistiche, censimenti, etc.
Dati privati degli operatori: con licenze limitate per non permettere di fare profitto ad altri operatori sulla base dei loro dati
Dati personali delle persone: profili, comportamenti tramite APP, IOT, sensori, web, etc.
Integrazione dati per renderli semanticamente interoperabili, ed operare deduzioni (time, space… )
I tradizionali collettori di open data danno visioni statistiche ma non sono adatti a produrre servizi integrati
Integrazione con modelli semantici unificanti come Km4City
Control Room delle Città Metropolitane devono:
arrivare a supervisionare domini multipli e le interdipendenze fra mobilità, energia, comunicazione, servizi, flussi traffico, flussi pedonali, turismo, etc.
Migliorare la loro Resilienza, capacità di reazione ed assorbimento
ridurre i costi sociali della mobilità per le persone
consentendo minori disagi, maggiore efficienza,
maggiore sensibilità verso le necessità del cittadino,
minori emissioni, migliori condizioni ambientali;
percorsi info-formativi in modo che il cittadino cambi le abitudini non virtuose;
ridurre i costi di trasporto ed i tempi di percorrenza per gli utenti, per i gestori e le amministrazioni, tramite soluzioni di ottimizzazione.
The data!
Services from Data via Smart City API
IOT Applications and IOT
Personal Data vs Open
Big Data Analytics
App as data collection and User Engagement
Social Media Analysis
Visual Analytics and Dashboards
The Living Lab Approach
Ontology Building vs Data Harvesting and Cleaning for Smart-city ServicesPaolo Nesi
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 is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity and reasoning. In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to smart-city ontology and stored into an RDF-Store where they 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 and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection. Keywords Smart city, knowledge base construction, reconciliation, validation and verification of knowledge base, smart city ontology, linked open graph.
Graph Databases Lifecycle Methodology and Tool to Support Index/Store Versio...Paolo Nesi
Abstract— Graph databases are taking place in many different applications: smart city, smart cloud, smart education, etc. In most cases, the applications imply the creation of ontologies and the integration of a large set of knowledge to build a knowledge base as an RDF KB store, with ontologies, static data, historical data and real time data. Most of the RDF stores are endowed of inferential engines that materialize some knowledge as triples during indexing or querying. In these cases, deleting concepts may imply the removal and change of many triples, especially if the triples are those modeling the ontological part of the knowledge base, or are referred by many other concepts. For these solutions, the graph database versioning feature is not provided at level of the RDF stores tool, and it is quite complex and time consuming to be addressed as black box approach. In most cases the indexing is a time consuming process, and the rebuilding of the KB may imply manually edited long scripts that are error prone. Therefore, in order to solve these kinds of problems, this paper proposes a lifecycle methodology and a tool supporting versioning of indexes for RDF KB store. The solution proposed has been developed on the basis of a number of knowledge oriented projects as Sii-Mobility (smart city), RESOLUTE (smart city risk assessment), ICARO (smart cloud). Results are reported in terms of time saving and reliability.
Keywords — RDF Knowledge base versioning, graph stores versioning, RDF store management, knowledge base life cycle.
Open Data Day 2016, Km4City, L’universita’ come aggregatore di Open Data del ...Paolo Nesi
Open Data Day, UNIMORE, Modena, 5 Marzo 2016.
Aggregazione dati, experienza di Firenze,
Smart City, Km4City,
Smart Decision Support,
Data Ingestion manager,
Data aggregation,
User profiling on demand.
Mobilità: inter-modalità, bigliettazione integrata, sostenibile, scambiatori, sfruttamento stazioni, etc.,
Servizi: gov ..SUAP, edu, turismo, beni culturali, salute, etc.,
Energia: risparmio energetico, riduzione amissioni, inquinamento, etc.,
Ambiente: qualità dell’aria, fiumi, meteo, rifiuti, etc.,
… commercio, industria, etc.
... Infrastrutture critiche. resilienza
Collezionamento dati statici, quasi statici e real time, stream
Dati open: geo localizzati, servizi, statistiche, censimenti, etc.
Dati privati degli operatori: con licenze limitate per non permettere di fare profitto ad altri operatori sulla base dei loro dati
Dati personali delle persone: profili, comportamenti tramite APP, IOT, sensori, web, etc.
Integrazione dati per renderli semanticamente interoperabili, ed operare deduzioni (time, space… )
I tradizionali collettori di open data danno visioni statistiche ma non sono adatti a produrre servizi integrati
Integrazione con modelli semantici unificanti come Km4City
Control Room delle Città Metropolitane devono:
arrivare a supervisionare domini multipli e le interdipendenze fra mobilità, energia, comunicazione, servizi, flussi traffico, flussi pedonali, turismo, etc.
Migliorare la loro Resilienza, capacità di reazione ed assorbimento
ridurre i costi sociali della mobilità per le persone
consentendo minori disagi, maggiore efficienza,
maggiore sensibilità verso le necessità del cittadino,
minori emissioni, migliori condizioni ambientali;
percorsi info-formativi in modo che il cittadino cambi le abitudini non virtuose;
ridurre i costi di trasporto ed i tempi di percorrenza per gli utenti, per i gestori e le amministrazioni, tramite soluzioni di ottimizzazione.
The data!
Services from Data via Smart City API
IOT Applications and IOT
Personal Data vs Open
Big Data Analytics
App as data collection and User Engagement
Social Media Analysis
Visual Analytics and Dashboards
The Living Lab Approach
Ontology Building vs Data Harvesting and Cleaning for Smart-city ServicesPaolo Nesi
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 is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity and reasoning. In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to smart-city ontology and stored into an RDF-Store where they 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 and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection. Keywords Smart city, knowledge base construction, reconciliation, validation and verification of knowledge base, smart city ontology, linked open graph.
Graph Databases Lifecycle Methodology and Tool to Support Index/Store Versio...Paolo Nesi
Abstract— Graph databases are taking place in many different applications: smart city, smart cloud, smart education, etc. In most cases, the applications imply the creation of ontologies and the integration of a large set of knowledge to build a knowledge base as an RDF KB store, with ontologies, static data, historical data and real time data. Most of the RDF stores are endowed of inferential engines that materialize some knowledge as triples during indexing or querying. In these cases, deleting concepts may imply the removal and change of many triples, especially if the triples are those modeling the ontological part of the knowledge base, or are referred by many other concepts. For these solutions, the graph database versioning feature is not provided at level of the RDF stores tool, and it is quite complex and time consuming to be addressed as black box approach. In most cases the indexing is a time consuming process, and the rebuilding of the KB may imply manually edited long scripts that are error prone. Therefore, in order to solve these kinds of problems, this paper proposes a lifecycle methodology and a tool supporting versioning of indexes for RDF KB store. The solution proposed has been developed on the basis of a number of knowledge oriented projects as Sii-Mobility (smart city), RESOLUTE (smart city risk assessment), ICARO (smart cloud). Results are reported in terms of time saving and reliability.
Keywords — RDF Knowledge base versioning, graph stores versioning, RDF store management, knowledge base life cycle.
Smart Cloud Engine and Solution based on Knowledge BasePaolo Nesi
Complexity of cloud infrastructures needs models and tools for process management, configuration, scaling, elastic computing and healthiness control. This paper presents a Smart Cloud solution based on a Knowledge Base, KB, with the aim of modeling cloud resources, Service Level Agreements and their evolution, and enabling the reasoning on structures by implementing strategies of efficient smart cloud management and intelligence. The solution proposed provides formal verification tools and intelligence for cloud control. It can be easily integrated with any cloud configuration manager, cloud orchestrator, and monitoring tool, since the connections with these tools are performed by using REST calls and XML files. It has been validated in the large ICARO Cloud project with a national cloud service provider.
Dynamic Semantics for the Internet of Things PayamBarnaghi
Ontology Summit 2015 : Track A Session - Ontology Integration in the Internet of Things - Thu 2015-02-05,
http://ontolog-02.cim3.net/wiki/ConferenceCall_2015_02_05
Snap4City has been created in response to Select4Cities PCP (http://www.select4cities.eu/) call as an open, standardized, data-driven, service-oriented, user-centric platform enabling large-scale co-creation IOT/IOE applications and services for Helsinki, Copenhagen and Antwerp.
Snap4City is a fully open source, robust, scalable, easy to use solution, provides tools for co-creation of mixt data driven, stream and batch processing, extending the powerful semantic reasoner of Km4City https://www.km4city.org, with IOT/IOE, GDPR, and city dashboards.
Km4City has been validated in multiple devices (PC, Android, Raspberry, ..), and domains: mobility and transport, tourism, health, welfare, social and cities such as Florence, Pisa, Arezzo, and large area of millions on inhabitants as Tuscany and million of data per day.
The innovation is mainly related to semantic reasoning, IOT interoperability, microservices, automated dashboard production, .. thus setting up smart city solutions in a snap
Km4City: una soluzione aperta per erogare servizi Smart CityPaolo Nesi
Km4City: Integrated Urban Platform, Open Source
Aggregate & integrate data
Multiple protocols from urban operators, ....
open data, IOT, sensors, internet of everything, cloud, mobile devices, Wi-Fi, social media, ...
Data Exploitation performing
predictions, reasoning, business intelligence, ..
users behavior analysis, decision support system, ..
Control Room, Real Time Monitoring tools, ….
Produce value from data enabling to
Stimulate virtuous behavior, influence City Users!
Put in action CITY Strategies
Km4City: Smart City HowTo and Overview, 2016Paolo Nesi
Km4City storia e roadmap
Dati e Modello
Development Tools
App: Web e Mobile
Mobile reasoning and Monitoring
Monitoring via Wi-Fi
Control Room
Twitter Vigilance
Progetti Connessi
Set up an ICT based Urban Platform integrated and unified data management among services, city operators and city users:
Control Room, Real Time Monitoring
decision support, assessing and monitoring risk and resilience
Data analytics and business intelligence
predictions, reasoning, city users behavior analysis, ….
Reading the city: data, users behavior and needs, ...
IOT, Open data sensors, private data, static and real time data.
City Strategies: stimulate virtuous behavior of City Users
participation, totem, twitter, Apps, etc.
Transform Data into value
Put in action smart city innovative solutions and services, development tools
Smart Cloud Engine and Solution based on Knowledge BasePaolo Nesi
Complexity of cloud infrastructures needs models and tools for process management, configuration, scaling, elastic computing and healthiness control. This paper presents a Smart Cloud solution based on a Knowledge Base, KB, with the aim of modeling cloud resources, Service Level Agreements and their evolution, and enabling the reasoning on structures by implementing strategies of efficient smart cloud management and intelligence. The solution proposed provides formal verification tools and intelligence for cloud control. It can be easily integrated with any cloud configuration manager, cloud orchestrator, and monitoring tool, since the connections with these tools are performed by using REST calls and XML files. It has been validated in the large ICARO Cloud project with a national cloud service provider.
Dynamic Semantics for the Internet of Things PayamBarnaghi
Ontology Summit 2015 : Track A Session - Ontology Integration in the Internet of Things - Thu 2015-02-05,
http://ontolog-02.cim3.net/wiki/ConferenceCall_2015_02_05
Snap4City has been created in response to Select4Cities PCP (http://www.select4cities.eu/) call as an open, standardized, data-driven, service-oriented, user-centric platform enabling large-scale co-creation IOT/IOE applications and services for Helsinki, Copenhagen and Antwerp.
Snap4City is a fully open source, robust, scalable, easy to use solution, provides tools for co-creation of mixt data driven, stream and batch processing, extending the powerful semantic reasoner of Km4City https://www.km4city.org, with IOT/IOE, GDPR, and city dashboards.
Km4City has been validated in multiple devices (PC, Android, Raspberry, ..), and domains: mobility and transport, tourism, health, welfare, social and cities such as Florence, Pisa, Arezzo, and large area of millions on inhabitants as Tuscany and million of data per day.
The innovation is mainly related to semantic reasoning, IOT interoperability, microservices, automated dashboard production, .. thus setting up smart city solutions in a snap
Km4City: una soluzione aperta per erogare servizi Smart CityPaolo Nesi
Km4City: Integrated Urban Platform, Open Source
Aggregate & integrate data
Multiple protocols from urban operators, ....
open data, IOT, sensors, internet of everything, cloud, mobile devices, Wi-Fi, social media, ...
Data Exploitation performing
predictions, reasoning, business intelligence, ..
users behavior analysis, decision support system, ..
Control Room, Real Time Monitoring tools, ….
Produce value from data enabling to
Stimulate virtuous behavior, influence City Users!
Put in action CITY Strategies
Km4City: Smart City HowTo and Overview, 2016Paolo Nesi
Km4City storia e roadmap
Dati e Modello
Development Tools
App: Web e Mobile
Mobile reasoning and Monitoring
Monitoring via Wi-Fi
Control Room
Twitter Vigilance
Progetti Connessi
Set up an ICT based Urban Platform integrated and unified data management among services, city operators and city users:
Control Room, Real Time Monitoring
decision support, assessing and monitoring risk and resilience
Data analytics and business intelligence
predictions, reasoning, city users behavior analysis, ….
Reading the city: data, users behavior and needs, ...
IOT, Open data sensors, private data, static and real time data.
City Strategies: stimulate virtuous behavior of City Users
participation, totem, twitter, Apps, etc.
Transform Data into value
Put in action smart city innovative solutions and services, development tools
Rights Enforcement and Licensing Understanding for RDF Stores Aggregating Ope...Paolo Nesi
Several applications are going to aggregate data on triple stores coming from different data sets and presenting different licenses. Semantic queries should provide only allowed triples, while most of the RDF stores have strong limitations in providing support for access control, licensing, rights enforcement and supporting the developers in providing tutoring information about what is possible and what is not. In this paper, a specific solution is proposed for supporting developers in understanding the licensing level of the requested triples, and the RDF stores in enforcing rights. The proposed solutions can be integrated into a range of different RDF stores for removing their limitations and assisting developers. The proposed solution has been developed and tested in the case of large smart city solution called Km4City and adopted in a number of projects: Sii-Mobility SCN, RESOLUTE H2020 and REPLICATE H2020.
Km4City: A reusable example of a Metropolitan-Wide Data Platform, MAJORCITIES...Paolo Nesi
Open Source and inter-operable tools to: (i) keep city under control via personalized dashboards - monitoring services status of city operators; - monitoring and understanding the city users behaviour; - collecting moods, contributions and data from the city users; - monitoring social media for city services and events, event predictions; (ii) improve city resilience, reducing risks and decision support by: - assessing city resilience level; - improving city resilience, providing objective hints; - improving city users awareness with personal city assistants and participatory tools; (iii) transform data in value for the city: - enabling commercial and business applications; - aggregating multi-domain data and services for SMEs and city operators; - enabling integrated city services into third party web portal for all; - providing suggestion on demand services for SMEs and city operators; - accelerating and simplifying the implementation of business and service oriented Apps. Follow the Km4City City Smartener Process
RESOLUTE: Governing for Resilience – Implementation Challenges Paolo Nesi
Conducting systematic review and assessment of the state of the art of Resilience Assessment and management concepts, national guidelines and their implementation strategies in order to develop a
conceptual framework for resilience with Urban Transport Systems, UTS
Development of European Resilience Management Guidelines, ERMG
Operationalize and validate the ERMG by implementing the Collaborative Resilience Assessment and Management Support System (CRAMSS) for UTS addressing Roads and Rails infrastructures
Enhancing resilience through improved support to human decision making processes, particularly through increased focus on the training of first responders and population on ERMG and RESOLUTE System
ERMG wide dissemination promoting acceptance and adoption at EU and Associate Countries level
Km4City, Smart City Urban Platform, From Data to Services for the Sentient Ci...Paolo Nesi
Km4City From Data to Services for the Sentient Cities
Open Source and inter-operable tools to keep city under control via personalized dashboards
- monitoring services’ status of city operators
- monitoring and understanding the city users behaviour
- collecting moods, contributions and data from the city users
- monitoring social media for city services and events, event predictions
improve city resilience, reducing risks and decision support by:
- assessing city resilience level
- improving city resilience, providing objective hints
- improving city users awareness with personal city assistants and participatory tools
transform data in value for the city:
- enabling commercial and business applications
- aggregating multi-domain data and services for SMEs and city operators
- enabling integrated city services into third party web portal for all
- providing suggestion on demand services for SMEs and city operators
- accelerating and simplifying the implementation of business and service oriented Apps
Follow the Km4City City Smartener Process
Functional Resonance Analysis Method based- Decision Support tool for Urban T...Paolo Nesi
Today, managing critical infrastructure resilience in smart city is a challenge that can be undertaken by adopting a new class of smart tools, which are able to integrate modeling capability with evidence driven decision support. The Resilience Decision Support tool, as presented in this article, is an innovative and powerful tool that aims at managing critical infrasctructure resilience through a more complex and expressive model based on the Functional Resonance Analysis Method and through the connection of such a model with a system thinking based decision support tool exploiting smart city data. Thanks to ResilienceDS, FRAM model becomes computable and the functional variability that is at the core of the resilience analysis can be quantified. Such quantification allows the decision support tool to compute specific strategies and recommendations for variability dampening at strategic, tactic and operational stage. The solution has been developed in the context of RESOLUTE H2020 project of the European Commission. Keywords—smart city, Functional Resonance Analysis Method; Decision Support System; Resilience; Urban Stransport System;
Sii-Mobility Km4City Smart City API and AppPaolo Nesi
Service search near GPS position
Service search within a GPS area
Service search within a WKT described area
Service search within a stored WKT described area
Service search by municipality
Service search by query id
Full text search
Event search
Address and geometry search by GPS
Service info
Generic Service
Event
Parking service
Traffic sensor
Weather Forecast
Bus station
Fuel Station
First aid
Smart waste container
Smart bench
Smart irrigator
Energy meter
Recharge station
Smart street light
Air quality monitoring station
(Bus) Agency list
(Bus) Lines list
(Bus) Routes list
(Bus) Stop list
Search (Bus) Routes in a geographic area
Estimated Bus position
Rating and comment API
Service Photo API
Last contributions API
Recommender API
Shortest path finder API
Image caching API
Km4City Smart City API: an integrated support for mobility servicesPaolo Nesi
AbstractThe 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. Keywords smart city, smart city ontology, smart city API, smart mobility, multidomain smart city, smart services.
IEEE Smartcomp
Twitter Vigilance: Modelli e Strumenti per l’Analisi e lo Studio di Dati Soci...Paolo Nesi
Twitter Vigilance: le analisi http://www.disit.org
Analisi e caratterizzazione della comunicazione
Percezione sociale, eventi pubblici, naturali..
Scoprire, identificare e calcolare
Nascita / crescita di nuove occorrenze in tempo reale: eventi, fatti, meteo, condizioni critiche, etc.
Supporto alla decisioni, ridurre i tempi di reazione, valutare la percezione, ridurre i costi, incrementare la resilienza come capacità di reagire, diagnosi precoce
Chi influenza la comunicazione, le comunità e come: i pusher, gli attori, i follower, le sorgenti, etc.
Predizione su eventi periodici, per esempio presenze ad eventi, presenze sui canali televisivi, vendite aziende, etc.
Misure indirette basate sulla popolazione: rischio sicurezza, degrado, neve, grandine, vento, fallimenti, etc.
Integrated infrastructure for urban platform in Florence Replicate project scc1Paolo Nesi
Integrated infrastructure for urban platform in Florence Replicate project scc1.
Aggregate & integrate data and streams of any urban system, operator,
provider, user, .., exploiting
– open data, IOT, sensors, internet of everything,
– cloud, mobile devices, Wi‐Fi, social media,
– big data analytics, ecc;
• Perform integrated and unified data management and data analytics by a set
of tools at service of city operators and city users, to:
– Control Room, Real Time Monitoring tools, ….
– Perform predictions, reasoning, business intelligence, city users behavior analysis
• Produce value from data enabling to
– Stimulate virtuous behavior, influence City Users!
– Increase efficiency in energy consumption
– Reduce pollution and traffic congestion
– Improve quality of service, quality of life
– Create an ecosystem for innovation and punt in action any smart city solutions
and services.
Km4city: Open Urban Platform for a Sentient Smart CityPaolo Nesi
Km4city: Open Urban Platform for a Sentient Smart City, presented at Smart City Expot World Congress.
Produce value from data enabling to
– Stimulate virtuous behavior, influence City Users!
– Increase efficiency in energy consumption
– Reduce pollution and traffic congestion
– Improve quality of service, quality of life
– Create an ecosystem for innovation and punt in action any smart city solutions
and services.
• Perform integrated and unified data management and data analytics by a set
of tools at service of city operators and city users, to:
– Perform predictions, reasoning, business intelligence, city users behavior analysis,
..;
– Control Room, Real Time Monitoring tools, ….
• Aggregate & integrate data and streams of any urban system, operator,
provider, user, .., exploiting
– open data, IOT, sensors, internet of everything,
– cloud, mobile devices, Wi‐Fi, social media,
– big data analytics, ecc;
1. keep city under control via personalized dashboards
2. transform data in value for the city, influence city users
3. Technical details: dashboard development and data aggregation
4. improve city resilience, reducing risks and decision support
Km4City: how to make smart and resilient your city, beginner documentPaolo Nesi
Open Source and inter-operable tools to
• keep city under control via personalized dashboards
• monitoring services’ status of city operators
• monitoring and understanding the city users behaviour
• collecting moods, contributions and data from the city users
• monitoring social media for city services and events, event predictions
• improve city resilience, reducing risks and decision support by:
• assessing city resilience level
• improving city resilience, providing objective hints
• improving city users awareness with personal city assistants and participatory tools
• transform data in value for the city:
• enabling commercial and business applications
• aggregating multi-domain data and services for SMEs and city operators
• enabling integrated city services into third party web portal for all
• providing suggestion on demand services for SMEs and city operators
• accelerating and simplifying the implementation of business and service oriented Apps
Follow the Km4City City Smartener Process
Km4city Smart City Ecosystem Urban PlatformPaolo Nesi
keep city under control via personalized dashboards
improve city resilience, reducing risks and decision support
transform data in value for the city
monitoring services’ status of city operators
Smart City Dashboards, http://dashboard.km4city.org Dashboard Builder
monitoring and understanding the city users behaviour
Recommender and User Behavior Analyzer, http://recommender.km4city.org
WiFi monitor, http://wifimap.km4city.org
Origin Destination matrix tools http://www.disit.org/6694
collecting moods, contributions and data from the city users
Collecting contributions: images, stars, comments and Social Media
monitoring social media for city services and events, event predictions
Twitter Vigilance, http://www.disit.org/tv , http://tvsolr.disit.org
assessing city resilience level
Resilience Decision Support, http://resilienceds.km4city.org
Smart decision support system, http://smartds.km4city.org
improving city resilience, providing objective hints
Resilience Decision Support implementing European Resilience Management Guidelines (ERMG) http://www.resolute-eu.org
improving city users awareness with personal city assistants and participatory tools
Dashboard: http://dashboard.km4city.org
Km4City Web App http://www.km4city.org
Km4City Mobile App: http://www.km4city.org/app
enabling commercial and business applications
decision support access
aggregating multi-domain data and services for SMEs and city operators
Data /Service Aggregator: open, flexible and suitable access
data aggregation and access Smart City API
integrated data and services, accessible as on demand basis
providing services for third party portals and Apps: geo-localized data and services, info, suggestions
suggestion on demand service for SMEs and city operators
Suggestion On Demand see above
Personal Assistance: information, engagement, soundage
development tool for fast and low cost implementation of business and service oriented Apps
Smart City API
Open Urban Platform for Smart City: Technical View Paolo Nesi
Km4City Roadmap
Data and Model
Control Room
Monitoring Traffic Flow and Parking
Monitoring City Users via Wi-Fi
Engaging Users Via Mobile App
Development Tools
Who is using it
City Resilience and DSS
Info and Documents
DISIT: Competenze per Industria 4.0
Technical Areas:
Industrial Internet: integrazione di fabbrica, ..
Smart/Advanced manufacturing: mobility, delivering, optimization, etc.
(Smart City: mobility and transport, energy, IOT/IOE, analytics, …)
(Smart Retail: user behavior analysis, engagement, …)
Technologies:
Big Data and Analytics: data management, user analysis, user engagement, prediction, early detection, data intelligence, …
Data Mining: artificial intelligence, semantic computing, semantic reasoner, expert systems, statistic analysis, ..
IOT/IOE: internet of things/everything, brokers, microservices, ..
Cloud: smart cloud, cloud simulation, optimization, ..
Mobile Computing: mobile application, user behavior analysis, ..
NLP and Sentiment Analysis: response Vigilance, interaction, answering, ..
See projects on: http://www.disit.org/5501
Big Data analytics Aree Applicative
-Smart manufacturing
-Personal assistants
-Autonomous engine, semantic reasoners
-Experts systems
-Smart Cloud
-Services and microservices integration
-Industrie farmaceutiche
-Mobilità e Trasporti
-Turismo e Cultura
-Smart City, Innovation Lab
Servizi alla persona
Overview on Smart City, DISIT lab solution for beginners, 2015, Part 7: Distr...Paolo Nesi
• Smart City Concepts
• Architecture of Smart City Infrastructures
• Peripheral processors
– Data collectors and Managers
– Blog Vigilance via Natural Language Processing
– Twitter vigilance
• Data ingestion and mining
– Data Mining and smart City problematic
– Km4City: Smart City Ontology
– RDF production, reconciliation
– Parallel and distributed processing
• Reasoning and Deduction
– Smart City Engine
– Decision Support System
• Data Acting processors
– Smart City Tools and API
– Service Map and Linked Open Graph
– Mobile applications
• Projects
– SmartCity Project Sii-Mobility SCN
– SmartCity Project Coll@bora SIN
– SmartCity Project RESOLUTE H2020
– Mobile Emergency
Keynote: Making Smarter Tuscany and Florence with Km4CityPaolo Nesi
Keynote at International Summit on Smart World and Smart Cities, In Conjunction With 2017 IEEE Smart World Congress
August 5, 2017, San Francisco, USA
http://smart-city-conference.com/summit2017/
Sentient Urban Platform for Smart City
Set up an ICT based Urban Platform integrated and unified data management among services, city operators and city users:
Control Room, Real Time Monitoring
decision support, assessing and monitoring risk and resilience
Data analytics and business intelligence
predictions, reasoning, city users behavior analysis, ….
Reading the city: big data, users behavior and needs, ...
IOT, Open data sensors, private data, static and real time data.
City Strategies: stimulate virtuous behavior of City Users
participation, totem, twitter, Apps, etc.
Transform Data into value
Put in action smart city innovative solutions and services, development tools
What is enabling and providing smart services
Smart Parking, in Tuscany
Smart First Aid in Tuscany
Smart Fuel pricing in Tuscany
Smart search for POI and public transport srv.
Public Transportation in Tuscany
Routing and multimodal in Tuscany
Social Media Monitoring and acting
Traffic events and Resilience in Florence
Bike Sharing in Pisa and Siena
Recharge stations for e-vehicles
Entertainment Events in Florence
Traffic Sensors in Tuscany
Weather forecast/condition in Tuscany
Pollution and Pollination in Tuscany
People Monitoring Assessment in the City, in Florence via WiFi
People Monitoring, in Tuscany via App
All Point of Interests, cultural activities, IOT, …
Over than 1.2 Million of complex events per day!
DISIT Lab overview: smart city, big data, semantic computing, cloudPaolo Nesi
Smart City
• Projects: http://www.disit.org/5501
– Sii-Mobility, http://www.sii-mobility.org
– Service Map: http://servicemap.disit.org
– Social Innovation: Coll@bora http://www.disit.org/5479
– Navigation Indoor/outdoor: Mobile Emergency http://www.disit.org/5404
– Mobility and Transport: TRACE-IT, RAISSS, TESYSRAIL
• Tools: http://www.disit.org/5489
– Data gathering, data mining and reconciliation
– Data reasoning, deduction, prediction
– Smart city ontology and reasoning tools
– Service analysis and recommendations
– Autonomous train operator, train signaling
– Risk analysis, decision support systems
– Mobile Applications
Data Analytics - Big data
• Projects: http://www.disit.org/5501
– Linked Open Graph: http://LOG.disit.org
– Sii-Mobility, http://www.sii-mobility.org
– Service on a number of projects
• Tools: http://www.disit.org/5489
– Open data and Linked Open Data
– LOG LOD service and tools
– Data mining and reconciliation
– Data reasoning, deduction, prediction, decision support
– SN Analysis and recommendations
– User behavior monitoring and analysis
Smart Cloud - Computing
• Projects: http://www.disit.org/5501
– ICARO: http://www.disit.org/5482
– Cloud ontology: http://www.disit.org/5604
– Cloud simulator:
– Smart Cloud: http://www.disit.org/6544
• Tools: http://www.disit.org/5489
– Cloud Monitoring
– Smart Cloud Engine and reasoner,
– Service Level Analyzer and control
– Configuration analysis and checker
– Cloud Simulation
Text and Web Mining
• Projects: http://www.disit.org/5501
– OSIM: http://www.disit.org/5482
– SACVAR: http://www.disit.org/5604
– Blog/Twitter Vigilance
• Tools: http://www.disit.org/5489
– Text and web mining, Natural Language Processing
– Service localization
– Web Crawling
– Competence analysis
– Blog Vigiliance, sentiment analysis
Social Media and e-Learning
• Projects: http://www.disit.org/5501
– ECLAP, http://www.eclap.eu
– ApreToscana: http://www.apretoscana.org
– Others: AXMEDIS, VARIAZIONI, SMNET, etc.
– Samsung Smart TV: http://www.disit.org/6534
• Tools: http://www.disit.org/5489
– XLMS, Cross Media Learning System
– IPR and content protection and distribution
– Mobile and SmartTv Applications
– Suggestions and recommendations
– Matchmaking solutions
– Media Tools for cross media content
Mobile Computing
• Projects:
– ECLAP: http://www.eclap.eu
– Mobile Medicine: http://mobmed.axmedis.org
– Mobile Emergency: http://www.disit.org/5500
– Smart City, FODD 2015: http://www.disit.org/6593
– Resolute: Mobiles as sensors
• Tools and support:
– Content distribution: e-learning
– Integrated Indoor/outdoor navigation
– User networking and collaboration
– Service localization
– Smart city and services
– OS: iOS, Android, Windows Phone, etc.
– Tech: IOT, iBeacoms, NFC, QR, ….
Km4City: Smart City Ontology Building for Effective Erogation of ServicesPaolo Nesi
Provides a unique point of service with integrated and aggregated data and tools for
-- Qualified users: public administrations à developers
-- Operators: mobility, energy, SME, shops, ….. à developers
-- Final users à citizens, students, pendular, tourists
Problems:
--Aggregated Data are not available:
not semantically interoperable, heterogeneous for: format, vocabulary, structure, velocity, volume, ownership/control, access / license, …
---As OD, LD, LOD, private data, ..
---Lack of Services and tools to make the adoption simple
Final Users tools:
--Km4City mobile app with personal assistant is coming…
--Km4City mobile applications: Google Play, Apple Store, …
--Km4City web application: http://www.km4city.org
--Open Source Mobile Application, FODD: an example in open source http://www.disit.org/6595
Public administrator tools:
--Smart decision support system, http://smartds.disit.org
--Developers http://www.disit.org/km4city tools:
--Service Map Server, plus API, http://servicemap.disit.org
--LOG LOD browser: an ultimate visual tool to browse the RDF Store.
--Ontology Documentation: an ultimate tool to understand,
if needed !!
The dirty work of Km4City service
--Data Ingestion Manager, DIM
--RDF Indexer Manager, RIM
--RDF Store Methodology
--RDF store enricher with dbPedia
--Distributed SCE Scheduler, DISCES
--SCE: Smart City Engine
--Doc and info on http://www.disit.org/km4city
A Smart City Development kit for designing Web and Mobile AppsPaolo Nesi
Presentation of some of the Km4City development tools: ServiceMap and App Development Kit, ADK.
ServiceMap is focused on providing information to the developers, to help them learning how to access to the data model, to exploit and use the API
ADK is a drafted modular web and mobile application based on HTML5 and JavaScript (apache Cordova) that can be used to exploit Smart City API to develop a large range of applications.
It is modular, flexible, etc. and allow performing users behavior analysis.
The solutions are currently in use on several EC and national Projects such as: Sii-Mobility, RESOLUTE, REPLICATE, Weee, …
Cities aims at providing new Smart Services to city users:
operators, final users, etc.
In most cases via Web and Mobile Apps which exploit data:
Structural data, open data, real time data, etc., private data from companies
to be aggregated and transformed in services (providing: prediction, information, early warning, relations)
at reasonable cost for: developers, operators, and SME to realize new Apps and services.
If cost is not affordable, Services and Apps are not developed, in most cases the Apps are also provided for free, so that high costs are not sustainable Public Private Partnership
Scenarious vs SmartCity API
Search data: by text, near, along, etc...
Resolving text to GPS and formal city nodes model
Empowering the city users
Access to event information
Supporting City Users in using Public Mobility
Supporting City Users in using Private Mobility
New Experience to access at Cultural and Touristic info
New way to access at health services
Access at Environmental information
Profiled Suggestions to City Users
Personal Assistant
Sharing knowledge among cities
ServiceMap tool
with Km4City are substantially a Smart City Expert System, SCES
includes the Smart City API
is a for developers to: search and browse on Smart City Knowledge, also to generate examples of the Smart City API call to be used in the development of Web and Mobile Apps
The IEEE Smart World Congress originated from the 2005 Workshop on Ubiquitous Smart Worlds (USW, Taipei) and the 2005 Symposium on Ubiquitous Intelligence and Smart World (UISW, Nagasaki). SmartWorld 2017 in San Francisco is the next edition after the successful SmartWorld 2016 in Toulouse France and SmartWorld 2015 in Beijing China. SmartWorld 2017 is to provide a high-profile, leading-edge platform for researchers and engineers to exchange and explore state-of-art advances and innovations in graceful integrations of Cyber, Physical, Social, and Thinking Worlds for the theme
http://ieee-smartworld.org/2017/smartworld/
RESOLUTE: Resilience management guidelines and Operationalization applied to ...Paolo Nesi
--Conducting a systematic review and assessment of the state of the art of the Resilience assessment and Management concepts, national guidelines and their implementation strategies in order to develop a conceptual framework for resilience within Urban Transport Systems
--Development of European Resilience Management Guidelines (ERMG)
--Operationalize and validate the ERMG by implementing the RESOLUTE Collaborative Resilience Assessment and Management Support System (CRAMSS) for Urban Transport System (UTS) addressing Roads and Rails Infrastructures
--Enhancing resilience through improved support to human decision making processes, particularly through increased focus on the training of final users (first responders, civil protections, infrastructure managers) and population on ERMG and RESOLUTE system
--ERMG wide dissemination, acceptance and adoption at EU and Associated Countries level
DRS-7-2015 - RIA – start 1/5/2015 - end 30/4/2018 Budget 3.8M
Smart City Strategic Forecast, SmartCity360, BratislavaPaolo Nesi
Smart City strategy, city smartening, big data amanagement,
-Taking into account results of participatory actions
-Smart city strategic plan, city agenda: prioritizing interventions
-Agreements for collaborations with main actors:
main research centers, main City Operators, etc.
-Direct collaborations on specific projects on:
ICT, Mobility, Culture, Energy, etc.
Experimenting on specific projects of the Smart City Strategic Plan
-Needs of harmonizing results and aggregating data towards dashboards
Big Data Smart City processes and tools, Real Time data processing toolsPaolo Nesi
Big Data Smart City Architecture
Smart-city Ontology
Data Ingestion and Mining
-Data Ingestion Manager
-DataSets already integrated
-Static Data: harvesting
-Data Quality Improvement
-Data mapping to Triples
Distributed and real time processes
-Distributed Scheduler
-Real Time Data Ingestion
-Blog Vigilance, NLP, Text Mining
-Parallel and distributed processing
RDF processing
-RDF Store Indexing
-RDF Store Validation
-Semantic Interoperability, reconciliation
-RDF Store Enrichment, for link discovering
-RDF Store Enrichment, for service discovering via web crawling
Smart City Engine
-Service Level Agreements
-Distributed SPARQL queries
-Decision Support System Processes
Development Interfaces
-Service map: http://servicemap.disit.org
service based on OpenStreetMaps that allows to search services available in a preset range from the selected bus stop.
-Linked Open Graph: http://log.disit.org
a tool developed to allow exploring semantic graph of the relation among the entities. It can be used to access to many different LOD repository.
-Ontology Documentation: http://www.disit.org/6507,
http://www.disit.org/5606, http://www.disit.org/6461
-Data Status Web pages: active
Visual Query Graph: under development
Sii-Mobility
Overview on Smart City: Smart City for BeginnersPaolo Nesi
Smart City Concepts
Architecture of Smart City Infrastructures
Peripheral processors
Data ingestion and mining
Reasoning and Deduction
Data Acting processors
SmartCity Project Coll@bora
SmartCity Project Sii-Mobility
Data Mining and smart City problematics
DISIT Smart City Ontology
Data ingestion and integration
Service Map and Linked Open Graph
Blog Vigilance via Natural Language Processing
Smart City and Open Data Projects and tools of DISIT LabPaolo Nesi
Current research topics
• Social media, collaborative work, Mobile computing, OpenData, LOD
• SmartCity, BigData, data analytics
• Railway signaling, autonomous driving systems, formal methods
• Cloud Computing, grid computing, smart cloud
• Data Mining, Knowledge Acceleration, natural language processing
Main research results
• Knowledge Management and Natural Language Processing: OSIM, CoSkoSAM
• Content and Protection Management, grid computing: AXMEDIS AXCP
• Social Media, recommendations and tool: ECLAP.eu, MyStoryPlayer, Social Graph, IPR Wizard…
• Mobile Computing: Mobile Medicine, Mobile Emergency, etc.….
• Music Transcode, winner of MIREX for piano
• Awards: IEEE ICECCS, DMS, Italia degli Innovatori, etc.
Main sources of funding
• European Commission: ECLAP (social media, Cultural Heritage, open data), AXMEDIS (DRM, protection, automation e grid computing), WEDELMUSIC, IMAESTRO, VARIAZIONI, IMUTUS, MUSICNETWORK, MOODS, MUPAAC, OFCOMP, etc. ……
• Italian Ministry: Smart Cities COLL@BORA (collaborative work, social media), FIRB e PRIN
• Regional: SACVAR (knowledge mining and reasoning), TRACE‐IT (Railway signalling), RAISSS (Railway signalling), ICARO (cloud)
• Fondations: MatchMaking (NLP), OSIM (Knowledge Acceleration, NLP)
DISIT Potential challenges and interests
DISIT is interested in participating in the next calls of the European Commission and in particular for:
• Working on open data and linked open data for smart city, smart cloud, smart manufacturing, smart museum, etc.
• Creating semantic models and reasoning engines
• Creating data mining and natural language processing tools as SACVAR/OSIM
• Working on defining big data solutions and infrastructures
• Working on data analytics algorithms computing:
• Predictions and trends,
• unexpected correlations,
• data inconsistencies and incompleteness,
• etc.
Open Urban Platform: Technical View 2018: Km4CityPaolo Nesi
Aggregate & integrate data
Multiple protocols from urban operators, ....
open data, IOT, sensors, internet of everything, cloud, mobile devices, Wi-Fi, social media, ...
Data Exploitation performing
predictions, reasoning, business intelligence, ..
users behavior analysis, decision support system, ..
Control Room, Real Time Monitoring tools, ….
Produce value from data enabling to
Stimulate virtuous behavior, influence City Users!
Put in action CITY Strategies
DAI DATI INTELLIGENTI AI SERVIZI Smart City API HackathonPaolo Nesi
DAI DATI INTELLIGENTI AI SERVIZI
Smart City API Hackathon
Premi per 14.000 euro
Data: 7 e 8 aprile 2017
Luogo: Scuola di Ingegneria, Università degli Studi di Firenze
Il progetto Sii-Mobility, Smart City nazionale (MIUR), organizza il primo hackathon per promuovere lo sviluppo di applicazioni fisse e mobili sulla base delle http://www.disit.org/6991che si basano sul modello http://www.km4city.org .
Scopo dell'evento di hackathon è identificare nuove applicazioni che possano essere sviluppate sulla base di dati ed elaborazioni messi disposizione dalle smart city API di Sii-Mobility. I Dati sono in tutta la toscana e come dagli scenari http://www.disit.org/6995, sono relativi alla mobilità pubblica e privata, alla partecipazione, alle informazioni geolocalizzate dei punti di interesse, della salute, ambiente, e servizi di suggerimento e di coinvolgimento e assistenza.
Le tematiche affrontate dalle App proposte dovranno essere relative ad aspetti di mobilità, e in particolare ai seguenti 5 temi: Trasporto pubblico; Coinvolgimento dei cittadini, mobilità e turismo, mobilità e servizi, giochi in mobilità.
http://www.sii-mobility.org/index.php/eventi/hackathon-sii-mobility/registrati-all-evento-del-7-mattina
Documentazione e informazioni dalla pagina: http://www.sii-mobility.org/
Scadenza sottomissione delle proposte: 31 marzo 2017.
Premi per 14.000 euro, #hackathon #smartcity API, #bigdata #opendata della #Toscanadigitale #firenze #pisa #arezzo #direfare #forumpa
#hackathon #smartcity #bigdata #opendata #Toscanadigitale #firenze #pisa #arezzo #direfare #forumpa
Smart City API, 14.000 euro di premi, Hackathon
Snap4City November 2019 Course: Smart City IOT Geernal overview, from dashboa...Paolo Nesi
• Overview
• Urban Platform (main concepts vs Living Lab)
• Snap4City Architecture, roadmap, logos, innovations
• Dashboards: from City Dashboards to Applications
• Trajectories and real time tracking
• Dashboards Intelligence and web and mobile devices
• Dashboard chatrooms and notifications
• Smart City Control Room
• Dashboards production
• Data Gathering and City Data Knowledge Management
• Protocol vs Data
• Data Gathering processes
• GIS Data Import, Export and Exploitation
• Semantic Modeling and City Knowledge Base: Km4CIty
• IOT Applications, Devices and Dashboards
• IOT Devices
• Forging & Managing Flexible Mobile Apps, Web App, MicroApplications
• Web and Mobile App with Open Development Kit
• Understanding how city users are using the city services
• Engaging City Users Towards Virtuous Behaviour
• Data Analytic, Big Data Science
• Data Analytics: predictions
• Smart Parking: predictions
• User behaviour Analysis via Wi-Fi, OD Matrices, Trajectories
• Recognition of Used Transportation Means
• Traffic Flow Reconstruction, from traffic sensors data
• Quality of Public Transport
• Origin Destination Matrices
• Demand of Mobility vs Offer of Transportation
• Modal and Multimodal Routing for Navigation and Travel Planning
• Environmental Data Predictions
• Prediction of Qir Quality
• Anomaly Detection
• Environmental data prediction
• Social Media Analysis
• Snap4City Living Lab for Collaborative Work
• Development Life Cycle
• Development tools
• Data protection, personal da vs GDPR
• Snap4City and Km4City Projects
• Acknowledgment
KM4city, Il Valore degli #OpenData: Esperienze a confrontoPaolo Nesi
le città si stanno adeguando alle crescenti necessità cercando di: garantire elevati livelli di qualità della vita, fornire nuovi servizi; limitando i costi, aumento di efficienza; allestire strutture decisionali adeguate; facilitare la creazione di nuovi servizi anche da parte di terzi:
-Pubblicazione Open Data
-Creare i presupposti per un mercato dei dati anche privati ma connessi agli -OpenData
->per una la crescita sostenibile da vari punti di vista
I dati, statici e real time sono stati resi interoperabili tramite algoritmi di data mining che possono essere applicati anche alle vostre problematiche
I dati aggregati ora sono accessibili in modo semplice tramite degli strumenti di sviluppo ed accesso che permettono di abbattere I costi di sviluppo delle applicazioni web e mobili
Service Map:http://servicemap.disit.org
Permette allo sviluppatore di realizzare delle query in modo visuale e farsi mandare il codice di richiesta tramite email.
Questo codice può essere utilizzato in App mobili e web per semplificare la programmazione e realizzare app che non devono essere manutenute quanto il server cambia…
La selezione effettuata può essere richiamata e anche inserita in pagine web di terzi, l’applicazione web è già pronta.
Manteniamo le App Vive, la complessità sta sul server e non sulle App !!
LOG: http://log.disit.org
Permette allo sviluppatore di navigare nelle strutture complesse di uno o più database RDF accessibili per formulare dei grafici e delle query in modo visuale e farsi mandare il codice di richiesta tramite email.
Questo codice può essere utilizzato in App mobili e web per semplificare la programmazione e realizzare app che non devono essere manutenute quanto il server cambia…
Il grafo puo’ essere richiamato e anche inserito in pagine web di terzi, l’applicazione web è già pronta.
Manteniamo le App Vive !!
Km4City: Smart City Model and Tools for City Knowledge ExploitationPaolo Nesi
The proposed presentation is going to expose the integrated solutions around Km4City model which has been set up by the DISIT lab in Florence (http://www.disit.org/6056 ) and adopted in some EC and national smart city projects (Sii-Mobility Smart City MIUR project, RESOLUTE H2020, Km4City service and tools in place in the Florence Area, with many industrial partners as Thales, Swarco, ECM, etc.). The solution is based on Km4City model that is capable to model a large set of the above data kind and provides support for inference and reasoning, on time and space, on public and private data, on static and real time data. In more details, the solution developed is open and accessible for city providing models and tools for its adoption and exploitation, also enabling the full customization. It includes a set of tools:
• Service map: http://servicemap.disit.org is a tool for PA administrators and for developers. For the PA administrators provide access to several kinds of geospatial queries in Florence and in the whole Tuscany region, taking as a results static and real time data, geo-localized. The ServiceMap is also a tool for developers, which can be used to understand the usage of API to access at the Km4City services http://www.disit.org/6597 . The ServiceMap facility allows the visual creation of queries on the city, and may send to the connected user via email the SPARQL code of the visual queries performed, and in addition also a simple Query ID. The Query ID can be used to pose the query without writing it, from any Mobile and Web applications without the needs of learning complex ontological and SPARQL models;
• Linked Open Graph for browsing LOD RDF Stores model including the Km4City Smart City model in Florence and Tuscany and thus for learning how to formulate SPARQL queries http://LOG.disit.org. See for example a view of Florence http://log.disit.org/service/?graph=0f50fffc5bcfc205de5a19b606b61310
• Demonstrative mobile application exploiting ServiceMap API, also presented at the Florence Open Data Day and accessible as open source via: http://www.disit.org/6595 .
• Km4City ontology model and documentation [1], http://smartcity.linkeddata.es/, document http://www.disit.org/5606 , http://www.disit.org/km4city/schema/
• Parallel and distributed architecture based on ETL, scheduler, HBase, Hadoop, for massive big data ingestion (static, quasi static, and real time data), reconciliation, data enrichment (for connecting Km4City URI to dbPedia, geonames, etc. [3]) and for making decision: [1], slide http://www.disit.org/6566 with thousands of accesses on SlideShare. Several examples are accessible about the ETL transformation for data ingestion, quality improvement, conversion in triples, reconciliation in SILK, [1], etc. This engine is also adopted in other Smart City Projects as SMST national cluster.
CINI icitie workshop on smart city and communities, palermo, ottobre 2015
Smart City at DISIT Lab, step two after smart city for beginnersPaolo Nesi
Smart City Concepts
Architecture of Smart City Infrastructures
Peripheral processors
Data ingestion and mining
Reasoning and Deduction
Data Acting processors
SmartCity Project Coll@bora
SmartCity Project Sii-Mobility
Data Mining and smart City problematic
DISIT Smart City Ontology
Data ingestion and integration
Service Map and Linked Open Graph
Blog Vigilance via Natural Language Processing
Mobile Emergency
Smart Health
Smart Education
Smart Mobility
Smart Energy
Smart Governmental
Smart economy
Smart people
Smart environment
Smart living
Smart Telecommunication
Snap4City has been created in response to Select4Cities PCP (http://www.select4cities.eu/) call as an open, standardized, data-driven, service-oriented, user-centric platform enabling large-scale co-creation IOT/IOE applications and services for Helsinki, Copenhagen and Antwerp.
Snap4City is 100% open source:
robust, scalable, easy to use solution, provides tools for co-creation of mixt data driven, stream and batch processing, GDPR, and city dashboards.
extending with IOT/IOE the semantic reasoner of Km4City https://www.km4city.org
Km4City has been validated in multiple devices (PC, Android, Raspberry, ..), and domains: mobility and transport, tourism, health, welfare, social and cities such as Florence, Pisa, Arezzo, and large area of millions on inhabitants as Tuscany and million of data per day.
The innovation is mainly related to semantic reasoning, IOT interoperability, microservices, automated dashboard production, .. thus
setting up smart city solutions in a snap
Serve as a City Dashboard, App User Interface, etc.
Real time and historical data, any device, sensors and actuators
Sensors, KPI, maps, data trends, real time data, charts, etc.
Referral / historical data, and Open Data:
shadow, access (API, storage, any protocol), production of OD, export
Data Driven Real Time communication & processing:
IOT Applications, IOT edge, multiple operating systems, embedded systems, MicroServices
in/out data driven from/to the field into: applications, notifications, etc.
Data Analytics: Machine Learning, statistics, reasoning, …
Serve as Living Lab: open innovation, coworking; collaborative work; sharing: data, processes, dashboard, experiences, solutions, ….
Experimented on large scale cases
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Accelerate your Kubernetes clusters with Varnish Caching
Overview la componente ICT vs Big Data
1. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA
Overview la componente ICT vs Big
Data
Prof. Paolo Nesi
DISIT Lab
Dipartimento di Ingegneria dell’Informazione
Università degli Studi di Firenze
Via S. Marta 3, 50139, Firenze, Italia
tel: +39-055-2758515,
fax: +39-055-2758570
http://www.disit.dinfo.unifi.it
paolo.nesi@unifi.it
Master MABIDA, overview ICT, 2016
2. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Aree di Intervento
• Basi di dati, SQL
– Casi: Analisi della struttura di documenti
• Intelligenza Artificiale
– Apprendimento, clustering, alberi decisionali
• Web crawling, XML, analisi dei testo
– Casi di studio: OSIM
• Reasoning and inferential
• Big data introduction: NoSQL, graph database, ..
• Social Media: user profiling, recommendations
– Caso di Studio: Twitter Vigilance, sentiment analysis
• Architetture parallele
• Casi di studio: smart city
• Casi di studio: Smart Cloud
Master MABIDA, overview ICT, 2016
3. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
4. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
5V of Big Data
Variety
Volume
Variabil
ity
Velocity
Value
5V’s of
Big Data
5. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
5V of Big Data…or more?
• In 2010 it was estimated a production of 1.2
zettabytes of data (1ZB = one trillion GB).
• In 2011, grew up in 1,8ZB.
• In 2013 came to 2,7ZB.
• The prediction for 2015 is about 4,8ZB.
6. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
7. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
8. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
http://pennystocks.la/internet‐in‐real‐time/
Master MABIDA, overview ICT, 2016
http://www.webpagefx.com/internet‐real‐time/
http://www.retale.com/info/retail‐in‐real‐time/
9. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
10. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
Application Fields
Increasing investments in Big Data can lead to interesting
discoveries in science, medicine, benefits and gains in the
ICT sector and in business contexts, new services and
opportunities for digital citizens and web users.
• Healthcare and Medicine
• Data Analysis – Scientific Research
• Educational
• Energy and Transportation
• Social Network – Internet Service – Web Data
• Financial/Business
• Security
11. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
Pipeline
Acquisi
tion/Re
cording
Extracti
on/Ann
otation
Integrati
on/Aggre
gation/R
epresent
ation
Analysi
s/Mode
ling
Interpr
etationINPUT OUTPUT
ALGORITMI
12. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
13. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
• The CAP theorem (Consistency ‐ Availability ‐
Partition tolerance) is essential to understand the
behavior of distributed SW systems, and how to
design the architecture in order to meet stringent
requirements, such as:
– High performance.
– Continued availability.
– Geographically distributed systems.
• Working on billions and trillions of
data every day, scalability became a
key concept.
Master MABIDA, overview ICT, 2016
CAP theorem
14. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
15. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Privati Tempo reale Pubblici Tempo reale (open data)
Pubblici statici (open data)Privati Statici
statistiche: incidenti, censimenti, votazioni
• Codice fiscale
• Foto non condivise
• Aspetti legali
• Cartella clinica
• ..
Master MABIDA, overview ICT, 2016
16. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.itKm4City on Firenze & Tuscany
Master MABIDA, overview ICT, 2016
Road Graph (Tuscany region)
132,923 Roads
389,711 Road Elements
318,160 Road Nodes
1,508,207 Street Numbers
110,374 Services (20 cat, 512 cat.)
2,326 Bus stops & 86 bus lines
210 Parking areas
424 Traffic Sensors
Info on: points, paths, areas, etc.
Dynamic/real‐time
• bus lines: 200 updates/day per line
• Parking status: 36 updates/day
• Traffic Sensors: 48 updates/day
• Weather: 2 updates/day for 285 areas
• Events: 60 new events/day
• Wi‐Fi: 250,000 measures per day
17. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
18. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Decisioni supportate dai dati
periodiche ed in tempo reale
• Condivisione e Integrazione Dati
multidominio: semantica e bigdata
• Dati Smart City Engine Control Room
• analisi: monitoraggio, flussi e
comportamenti, sondaggi, mining,
correlazioni, cause – effetti, etc.
– Per il miglioramento di servizi
correnti
– Per reagire ad eventi,
incremento della resilienza,
– Per la creazione
servizi innovativi
– …
Master MABIDA, overview ICT, 2016
19. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Smart City Decision Support
• Smart Decision Support
System based on
System Thinking plus
• Actions to city reaction,
resilience, smartness..
Enforcing
• Mathematical model for
propagation of decision
confidence..
• Collaborative work…,
• Processes connected to
city data: DB, RDF Store,
Twitter, etc.
• Production of
alerts/alarms
• Data analytics process
• Twitter Processes
• reuse, copy past, …
Master MABIDA, overview ICT, 2016
20. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.itAll Channels (private information)
Master MABIDA, overview ICT, 2016
1) General view with top
explosive events on
commenting tv Serials
2) This is what there in the ground when
the top explosive events are hidden
21. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Modelli
predittivi
Master MABIDA, overview ICT, 2016
Sentiment
Analisis
22. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Twitter Vigilance
• http://www.disit.org/tv
• Citizens as sensors to
– Assess sentiment on
services, events, …
– Response of
consumers wrt…
– Early detection of
critical conditions
– Information channel
– Opinion leaders
– Communities
– formation
Master MABIDA, overview ICT, 2016
23. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
Firenze dove, cosa.. Km4City
24. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
http://www.disit.org
Tourists in Florence
Master MABIDA, overview ICT, 2016
25. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
http://www.disit.org Hot WiFi in Florence
Master MABIDA, overview ICT, 2016
26. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.itTraffic and People Flow Assessment
• Origin Destination Matrix
– Specific Sensors, vehicle
Kits, mobile App, Wi‐Fi
Access Points, etc.
• Assess people and traffic
flows to
– improve services
– predict critical conditions
on Crit. Infra.
– take real time decisions
and sending messages in
push to population
– Increase city resilience
– optimize traffic flow
– take decision of routing
Master MABIDA, overview ICT, 2016
http://www.disit.org/6694
27. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
28. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Km4CityMobile App: all stores
Master MABIDA, overview ICT, 2016
http://www.km4city.org
web application
29. Knowledge Management and Protection Systems (KMaPS): 2015-2016 29
Proximity Suggestion Architecture
Km4City
Km4CitySmartCityAPI
Suggestions on
Demand, machine
learning
Proximity
search
Suggestion
request
User Profiling
Collective Profiling
30. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
http://www.disit.org
Recommender
Master MABIDA, overview ICT, 2016
Chi
Quando e chi
Cosa
Dove come, perché.......
31. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Km4City Smart City Engine
Transport systems
Mobility, parking
Smart Decision Support
Http://Smartds.disit.org
Twitter Vigilance
Http://www.disit.org/tv
Flow and Origin Destination Matrix
Http://www.disit.org/odsf
Service map browser
Http://servicemap.disit.org
Smart City Dashboard
Http://www.disit.org/dash
Km4City Smart City API Mobile e Web Apps
Public Services
Govern, events,
…
Sensors, IOT
Cameras, ..
Environment,
Water, energy
Social Media
WiFi, network
DISCES ‐‐Distributed and parallel architecture on Cloud
Shops, services,
operators
User Profiling and
Suggestions on Demand
Http://www.km4city.org
Km4City
Tools for Final Users
Tools for City Operators and Decision Makers
Km4City Tools for Developers
Collective User behavior Analyzer
32. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Example of Ingestion process
Master MABIDA, overview ICT, 2016
33. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
RDF KB life cycle methodology
Master MABIDA, overview ICT, 2016
34. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
http://www.disit.org
I Dati
• Collezionamento dati statici, quasi statici
e real time, stream
– Dati open: geo localizzati, servizi,
statistiche, censimenti, etc.
– Dati privati degli operatori: con licenze
limitate per non permettere di fare
profitto ad altri operatori sulla base dei
loro dati
– Dati personali delle persone: profili,
comportamenti tramite APP, IOT, sensori,
web, etc.
• Integrazione dati per renderli
semanticamente interoperabili, ed
operare deduzioni (time, space… )
– I tradizionali collettori di open data danno
visioni statistiche ma non sono adatti a
produrre servizi integrati
– Integrazione con modelli semantici
unificanti come Km4City
Master MABIDA, overview ICT, 2016
35. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
http://log.disit.org
Master MABIDA, overview ICT, 2016
Linked Open Graph
http://log.disit.org
36. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
http://log.disit.org
37. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
• Experimentations and validation in Tuscany
• Integration with present central station and subsystems
• DISIT lab, Università di Firenze, is the tech-scientific coordinator
Sii‐Mobility
Master MABIDA, overview ICT, 2016
http://www.Sii-Mobility.org
ECM; Swarco Mizar;
Inventi In20; Geoin;
QuestIT; Softec;
T.I.M.E.; LiberoLogico;
MIDRA (autostrade,
motorola); ATAF;
Tiemme; CTT Nord;
BUSITALIA; A.T.A.M.;
Effective Knowledge;
eWings; Argos
Engineering; Elfi;
Calamai & Agresti;
Project; Negentis
39. Obiettivi Generali (sintesi)
• ridurre i costi sociali della mobilità
per le persone
– consentendo minori disagi, maggiore
efficienza,
– maggiore sensibilità verso le
necessità del cittadino,
– minori emissioni, migliori condizioni
ambientali;
– percorsi info‐formativi in modo che il
cittadino cambi le abitudini non
virtuose;
– ridurre i costi di trasporto ed i tempi
di percorrenza per gli utenti, per i
gestori e le amministrazioni, tramite
soluzioni di ottimizzazione.
Master MABIDA, overview ICT, 2016
• semplificare l’uso dei sistemi di
mobilità
– sensori innovativi per AVM e mezzi
privati sul territorio
– Sistemi integrati di pagamento e di
identificazione
– soluzioni di guida/percorso connesso
(connect drive, smart drive o walk)
– Integrazione di dati provenienti da
gestori e sorgenti di tipo diverso
– Gestione avanzata di mezzi
– misurazione di flussi
– realizzazione di sensori, attuatori
• Sperimentazione su comuni e province della Toscana
• Contribuire al miglioramento degli standard nazionali ed internazionali
40. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
http://www.disit.org
• Develop European Resilience Management
Guidelines (ERMG)
– Develop a conceptual framework for creating/
maintaining Urban Transport Systems
• Enhance resilience through improved support of
human decision making processes, particularly by
training professionals and civil users on the ERMG
and the RESOLUTE system
• Operationalize and validate the ERMG by
implementing the RESOLUTE Collaborative
Resilience Assessment and Management Support
Systems (CRAMSS) for Urban Transport Systems
addressing Road and Urban Rail Infrastructures
– Pilots in Florence and Athens
• Adoption of the ERMG at EU and Associated
Countries level
Master MABIDA, overview ICT, 2016
http://www.resolute‐eu.org
University of Florence:
DISIT lab DINFO (Proj
coordinator), DISIA and DST
UNIFI IT
THALES THALES IT
ATTIKOMetro ATTIKO GR
Comune di Firenze CDF IT
Centre for Research and
Technology Hellas
CERTH GR
Fraunhofer‐Gesellschaft zur
Förderung der angewandten
Forschung e.V.
FHG DE
HUMANIST
HUMANIS
T
FR
SWARCO Mizar SWMIZ IT
Associação para o
Desenvolvimento da
Investigação no Instituto
Superior de Gestão
ADI‐ISG PT
Consorzio Milano Ricerche CMR IT
41. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
42. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
http://www.disit.org
• demonstrate Smart City technologies in energy,
transport and ICT in districts in:
– San Sebastian, Florence and Bristol,
– follower cities of Essen, Nilufer and Lausanne
• Cities are the customer: considering local
specificities
• Solutions must be replicable, interoperable and
scalable.
– Integrated Infrastructure: deployment of ICT
architecture, from internet of things to
applications
– Low energy districts
– Urban mobility: sustainable and smart urban
services
1 (coordinator) FOMENTO DE SAN SEBASTIAN FSS SPAIN
2 AYUNTAMIENTO DE SAN SEBASTIAN SAN SEBASTIAN SPAIN
3 COMUNE DI FLORENCE FLORENCE ITALY
4 BRISTOL COUNCIL BRISTOL UNITED KINGDOM
5 STADT ESSEN ESSEN GERMANY
6 NILUFER BELEDIYESI NILUFER TURKEY
7 VILLE DE LAUSANNE LAUSANNE SWITZERLAND
8 IKUSI ANGEL IGLESIAS, S.A. IKUSI SPAIN
9 ENDESA ENERGÍA, S.A. ENDESA SPAIN
10 EUROHELP CONSULTING, S.L. EUROHELP SPAIN
11 ILUMINACION INTELIGENTE LUIX, S.L. LUIX SPAIN
12 FUNDACION TECNALIA RESEARCH & INNOVATION TECNALIA
SPAIN
13 EUSKALTEL, S.A. EUSKALTEL SPAIN
14 COMPAÑÍA DEL TRANVÍA DE SAN SEBASTIÁN DBUS SPAIN
15 CONSIGLIO NAZIONALE DELLE RICERCHE CNR ITALY
16 ENEL DISTRIBUZIONE, SPA ENEL ITALY
17 MATHEMA, SRL MATHEMA ITALY
18 SPES CONSULTING SPES ITALY
19 TELECOM ITALIA, SPA TELECOM ITALY
20 UNIVERSITA DEGLI STUDI DI FLORENCE UNIFI ITALY:
DINFO.DISIT, DIEF
21 THALES ITALIA, SPA THALES ITALY
22 ZABALA INNOVATION CONSULTING ZABALA SPAIN
23 TECHNOMAR TECHNOMAR GERMANY
24 UNIVERSITY OF BRISTOL UOB UNITED KINGDOM
25 UNIVERSITY OF OXFORD UOXF UNITED KINGDOM
26 BRISTOL IS OPEN, LTD BIO UNITED KINGDOM
27 ZEETTA NETWORKS ZEETTA UNITED KINGDOM
28 KNOWLE WEST MEDIA CENTRE, LGB KWMC UNITED KINGDOM
29 TOSHIBA RESEARCH EUROPE, LTD TREL UNITED KINGDOM
30 ROUTE MONKEY, LTD ROUTE MONKEY UNITED KINGDOM
31 ESOTERIX SYSTMES, LTD ESOTERIX UNITED KINGDOM
32 NEC LABORATORIES EUROPE, LTD NEC UNITED KINGDOM
33 COMMONWHEELS CAR CLUB CIC CO-WHEELS UNITED
KINGDOM
34 UNIVERSITY OF THE WEST OF ENGLAND UWE UNITED
KINGDOM
35 ESADE BUSINESS SCHOOL ESADE SPAIN
36 SISTELEC SOLUCIONES DE TELECOMUNICACION, S.L.
SISTELEC SPAIN
43. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
http://www.disit.org
REPLICATE a Firenze: Energia, ICT e Mobilità
Master MABIDA, overview ICT, 2016
44. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
MatchMaking: demand vs offers
45. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Knowledge analysis
http://OSIM.disit.org
Master MABIDA, overview ICT, 2016
46. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016Best practice network for performing arts
48. Knowledge Management and Protection Systems (KMaPS): 2015-2016 49
Semantic Flows: ECLAP
•User Profile
•Dynamic User Profile
•User behavior
•Use data
•Content
•DC+IDs
•AXInfo: ver, prod.,
rights,..
•Descriptors
•Groups: users, content..
•Ontology/Taxonomy Domain
•Suggestions on the basis of:
• Static and dynamic user
profile, decriptors, domain
•Local User Profile
•Local Dynamic User Profile
•Local User behavior
•Local Use data
•Content
•DC+IDs
•AXInfo: ver, prod, rights,
....
•Descriptors
•Groups
•Taxonomy classification
•Local Suggestions on the
basis of user profiles, local
content, local collected data
contributions,
actions on
content,
social actions,
preferences,
queries,
use data,..
Front End Portal
Content Organizer and Players Users
Grid Scheduler
Grid Node
Grid Node
Grid Node
AXCP backoffice
•Rule based system
•Automated formatting
•Inferential engine
processing
•Adaptation
•enrichement
•Multilingual index and
search
•Text Analysers
•Indexer
•Fuzzy search
•Suggestions
•Similarity distances
•Clustering
AXCP BackOffice
Content Organiser
49. Knowledge Management and Protection Systems (KMaPS): 2015-2016 50
La validazione
-0,4
0,1
3,2
1,6 1,3
0,3
-1
0
1
2
3
4
5
contenuti
fruiti
categorie
di
interesse
età lingua località gruppi
Incidenza sul voto
Voto ≥
3
70%
Voto <
3
30%
Voto ≥
3
91%
Voto <
3
9%
Tipologia Serendipity
Competenze
Gruppi di appartenza
Tipologia Strategici
Popolarità
Statistica della regressione
R multiplo 0, 9624
F - Value 131,7795
Significatività di F 2,3389E-33
50. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016http://www.cloudicaro.it
51. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Cloud Supervisor & Monitor
• Monitoring real business
configuration, SLA
• Uplayer wrt classical
monitoring tools
Master MABIDA, overview ICT, 2016
http://www.cloudicaro.it
52. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Master MABIDA, overview ICT, 2016
Smart Cloud Engine
53. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
http://www.disit.org
P. Bellini, M. Di Claudio, P.
Nesi, N. Rauch, "Tassonomy
and Review of Big Data
Solutions Navigation", in "Big
Data Computing", Ed. Rajendra
Akerkar, Western Norway
Research Institute, Norway,
Chapman and Hall/CRC press,
ISBN 978‐1‐46‐657837‐1,
eBook: 978‐1‐46‐657838‐8,
july 2013, in press.
http://www.tmrfindia.org/bigd
ata.html
Master MABIDA, overview ICT, 2016
54. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Main & Recent Projects
http://www.disit.org/6588
http://www.disit.org/5530
http://www.disit.org/5479
http://www.cloudicaro.it
http://www.disit.org/foddhttp://www.sii-mobility.org
http://www.axmedis.org
http://www.eclap.eu
RAISSS
Trace-IT
http://www.apretoscana.org
http://osim.disit.org
Master MABIDA, overview ICT, 2016