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Towards Ambient Assisted Cities: Smarter, more Sustainable, Collaborative and Inclusive Cities


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Context: societal urbanization and ageing
Interdependence analysis: Ambient Assisted Cities
ICT & Social Innovation leading towards Smarter Cities

Technologies for enablement of Smarter Cities:
Internet of Things
Web of Data

Building Smarter Cities
Broad Data Analysis Tools
European projects about Smarter Ambient Assisted Cities


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Towards Ambient Assisted Cities: Smarter, more Sustainable, Collaborative and Inclusive Cities

  1. 1. 1 Towards Ambient Assisted Cities: Smarter, more Sustainable, Collaborative and Inclusive Cities Curso: Ambientes inteligentes para mejorar la salud, el bienestar y potenciar la autonomía UNIA. Baeza, Jaén, SPAIN, 31st August 2016, 12:00-14:30 Dr. Diego López-de-Ipiña González-de-Artaza
  2. 2. 2 Agenda 1. Introduction: – Context: societal urbanization and ageing – Interdependence analysis: Ambient Assisted Cities – ICT & Social Innovation leading towards Smarter Cities 2. Technologies for enablement of Smarter Cities: – Internet of Things – Web of Data – Crowdsourcing 3. Building Smarter Cities – Broad Data Analysis Tools – European projects about Smarter Ambient Assisted Cities 4. Conclusion
  3. 3. 3 Context (I) • Two of the biggest challenges faced by current society are: –continuous urbanisation process –progressive population ageing • In Spain, 70% population lives in urban centres, where 18,4% > 65 years
  4. 4. 4 Context (II) • Cities are responsible for 70% world’s GDP and 80% of CO2 emissions – Cities are more efficient environments urbanisation process • Public spending associated to ageing in Europe (pensions, health systems, long life caring and learning) is about 20% of GDP • Important to conceive and adopt solutions to counter-back the social and economic effects of urbanisation and ageing in society – Solution: technology + social innovation
  5. 5. 5 Smarter Cities to fight back Urbanisation process • Smart City is a place where urban services are improved in efficiency by applying ICT, for the benefit of its inhabitants and economic development • Smart Territories innovative geographic areas, able to build their own competitive advantages taking into account their context • Smart Places balance among economic competitiveness, social cohesion, innovative creativity, democratic governance and environmental sustainability – Satisfying the basic and self-fulfilment needs in the Maslow pyramid
  6. 6. 6 Challenges for Smarter Cities • Enable life, work and leisure environments which allow our self- fulfilment without disregarding basic needs and their development in welfare society • Answer to the urbanization demands in a economically feasible, socially inclusive and sustainable manner – BUT… apply traditional solutions to the needs of urban development  unsustainable urban ecology footprint • Generate more electricity or new water resources not addressing inefficiencies in distribution
  7. 7. 7 ICT as levers of Smarter Cities (I) • ICTs will help in the urbanization process only if the following three premises are fulfilled: 1. Social equity 2. Economic feasibility and 3. Environmental sustainability • ICTs are key to leverage the existing infrastructure and maximize the socioeconomic throughput • A more rational and extensive usage of ICT in cities a quicker and more economic fulfilment of urban challenges
  8. 8. 8 • Some interesting examples: – smart meters for water and electricity which, through technology, reduce leakage and waste and improve transparency and reliability – telehealth systems which connect hospitals with remote facilities for monitoring, diagnosis or training – smart transport solutions which help to improve efficienty and usage of air, road, train or sea transport resources – public security solutions to help preventing, detecting and answering to security requirements ICT as levers of Smarter Cities (II)
  9. 9. 9 Telehealth system
  10. 10. 10 ICT as levers of Smarter Cities (III): Big | Open | Personal Data • Big potential for enterprises, social entities and governments if there is a better usage of infrastructure and information (IoT + Open + Personal data) in urban environments: – Big Data: extensive analysis of heterogeneous urban data to offer answers, indicators and visualizations to help improvinng the decision criteria upon the challenges of cities and territory management • It will allow us to progress towards more disruptive approaches – All agents should benefit from a more efficient usage of data processing technology to give place to Urban Analytics • Great potential but huge difficulty associated!
  11. 11. 11 ICT as levers of Smarter Cities (IV): Open Collaboration • Smarter environments cannot only be reached through technological solutions – We have to take advantage of the huge potential of collective intelligence and citizenship capacity to generate knowledge through crowdsourcing techniques (or open distributed collaboration)
  12. 12. 12 ICT as levers of Smarter Cities (V): Social Innovation + Open Government • Digital Social Innovation: collaboration of citizenship through new technologies to co-create knowledge and solutions addressed to an ample range of social needs through Internet, e.g.: – Social networks for those that suffer chronic diseases – Platforms for citizen collaboration – Open data for transparency and good government linked to public expenditure • Collaborative Open Government: combination of Technology and participation of different agents and sectors of society in government – Give answer to citizen needs and demands with reduced time and budgets – Improve the business environment providing better services to enterprises and citizenship – Adapt the service provision to the needs of a more digital economy
  13. 13. 13 Open Innovation • New innovation strategy upon which companies go beyond the internal bounds of an organization and where collaboration with external professionals starts playing a key role – Collective intelligence is brought forward by the collaboration and partake of several individuals of a given spice. • Users as PROSUMERS • Some interesting initiatives in this are: – Kick starter – web site for people’s funding of creative projects (CrowdFunding), – Ushahidi – crowdsourcing crisis information, – – crowdsourcing for government problems,
  14. 14. 14 Social Open Innovation • Novel solution to a social problem that is more effective, efficient, sustainable, or just than current solutions. – New ideas (products, services and models) that simultaneously meet social needs and create new social relationships
  15. 15. 15 CAPS: Collective-awareness Platforms for Sustainability and Social Innovation • Aims at designing and piloting online platforms creating awareness of sustainability problems and offering collaborative solutions based on networks (of people, of ideas, of sensors), enabling new forms of social innovation. • Examples: – Open Democracy, Open Policy Making – Collaborative/Shared Economy – Collaborative making  co-creation
  16. 16. 16 ICT as levers of Smarter Cities (VI): Ethical Implications • Personal data are the “new petrol” of XXI century, being exploited by big corporations such as Google, Apple (publicity + marketing) BUT … – There are multiple distributed personal data silos among different Internet providers and institutions which have to be interoperable – There is a need for individuals to have a greater control of their own personal data • Governments must: – Regulate, protect, legislate to guarantee the rights and opportunities of such data providers (we) – Legislate and manage non-functional aspects (accessibility – technological inclusion, privacy, data protection and ethics to achieve responsible technological solutions • Urban knowledge management is a key to undertake innovation in the public sector and achieve the implication of private agents for a more fruitful public- private partnership – Need to progress from Urban Open Data to Open Knowledge!!!
  17. 17. 17 Personal Data • Defined as "any information relating to an identified or identifiable natural person ("data subject")”
  18. 18. 18 From Open Data to Open Knowledge
  19. 19. 19 Ageing forecast in Europe for 2060
  20. 20. 20 • By 2060, 30% of European population will be older than 65 – Negative  smaller active population share will pay taxes to support an increasing volume of elderly people – Positive older people want, can and should play a key role in society • The “Young old” (65-74 years) undertake caring activities (other elderly, children), take part in family businesses or collaborate in NGOs – Imperative to use the capabilities and knowledge of elderly people (bigger professional involvement): » Promote their desire to improve the community and social services » Put them in contact with professional associations which demand their knowledge and contacts to sustain industrial and business processes – GDP of those countries with strong family links (south of Europe), could be improved if their non-remunerated voluntary work would be taken into account • Offer to companies an opportunity to access a growing and lucrative market which has been termed as Silver Economy Smarter Cities aware of Elderly People (I)
  21. 21. 21 Smarter Cities aware of Elderly People (II): Silver Economy • Economic opportunities resulting from public and private expenditure (users+ consumers) associated to population ageing (> 50 years) – Imperative to promote the innovation for an active and healthy ageing, helped by a network – Needed promotion of innovation for active and healthy ageing, helped by European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) or the Active and Assistive Living program • Some examples of the market opportunities for products and services are: – Smart houses for independent living – Devices to monitor personal health – E-senior tourism – Autonomous vehicles and assistive robotics • Digital Social Innovation applied to ageing challenge, brings forward: – Improve the quality of life for elderly citizens and their carers – Promotes care systems which are more efficient and sustainable – Generates new business opportunities and economic growth
  22. 22. 22 Silver Economy
  23. 23. 23 The need for Smart Cities • Challenges cities face today: – Growing population • Traffic congestion • Space – homes and public space – Resource management (water and energy use) – Global warming (carbon emissions) – Tighter city budgets – Aging infrastructure and population
  24. 24. 24 What is a Smart City? • Smart Cities improve the efficiency and quality of the services provided by governing entities and business and (are supposed to) increase citizens’ quality of life within a city – This view can be achieved by leveraging: • Available infrastructure such as Open Government Data and deployed sensor networks in cities • Citizens’ participation through apps in their smartphones – Or go for big companies’ “smart city in a box” solutions
  25. 25. 25 What is a Smart Sustainable City? A smart sustainable city is an innovative city that uses information and communication technologies and other means to improve quality of life, efficiency of urban operation and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social and environmental aspects
  26. 26. 26 Smart Diamond of Smart Cities
  27. 27. 27 Smart City Applications • sadfafd
  28. 28. 28 Ambient Assisted Cities: Age- friendly Smart Cities (I) • WHO (World Health Organization) has acknowledged the convergence of urbanisation and ageing, coining the concept of “Age-friendly cities” – promote active ageing optimizing the opportunities for health, participation and security so that quality of life is improved whilst people age • ICT, is and will be, a key factor to ensure in situ ageing, within the city, offering solutions in aspects like: – Simplified communication and involvement through ICT to be in contact with members of family and community – Security systems giving answer to personalised mobile emergencies and fall monitoring systems to provide confidence to elderly and caregivers. – Health and welfare through ICT to help elderly people to maintain active and manage chronic diseases, like diabetes and hearth problems. – Learning and contributing with innovations to allow that elderly people can keep reading, learning and keep active in society.
  29. 29. 29 Ambient Assisted Cities: Age- friendly Smart Cities (II) • The main attribute of a Smart City is efficiency • An Age-friendly city is an inclusive and accessible urban environment that promotes active ageing • The main attributes of an Ambient Assisted (Smarter) City are: – Livable – Accessible – Healthy – Inclusive – Participative [WHO Global Network of Age-friendly Cities]
  30. 30. 30 What is an Ambient Assisted City? • A city aware of the special needs of ALL its citizens, particularly those with disabilities or about to lose their autonomy: – Elderly people • The "Young Old" 65-74 • The "Old" 75-84 • The "Oldest-Old" 85+ – People with disabilities • Physical • Sensory (visual, hearing) • Intellectual
  31. 31. 31 The need for Participative Cities • Not enough with the traditional resource efficiency approach of Smart City initiatives • “City appeal and dynamicity” will be key to attract and retain citizens, companies and tourists • Only possible by user-driven and centric innovation: – The citizen should be heard, EMPOWERED! » Urban apps to enhance the experience and interactions of the citizen, by taking advantage of the city infrastructure – The information generated by cities and citizens must be linked and processed » How do we correlate, link and exploit such humongous data for all stakeholders’ benefit? • Demand for Big (Linked) Data for enabling Urban Analytics!!!
  32. 32. 32 • Smart Cities seek the participation of citizens: – To enrich the knowledge gathered about a city not only with government-provided or networked sensors' provided data, but also with highly dynamic user-generated data • BUT, how can we ensure that users and their generated data can be trusted and has enough quality? – W3C has created the PROV Data Model, for provenance interchange Citizen Participation
  33. 33. 33 • There is a need to analyze the impact that citizens may have on improving, extending and enriching the data – Quality of the provided data may vary from one citizen to another, not to mention the possibility of someone's interest in populating the system with fake data • Duplication, miss-classification, mismatching and data enrichment issues Problems associated to User-provided Data
  34. 34. 34 Urban Intelligence / Analytics • Broad Data aggregates data from heterogeneous sources: – Open Government Data repositories – User-supplied data through social networks or apps – Public private sector data or – End-user private data • Humongous potential on correlating and analysing Broad Data in the city context: – Leverage digital traces left by citizens in their daily interactions with the city to gain insights about why, how and when they do things – We can progress from Open City Data to Open Data Knowledge • Energy saving, improve health monitoring, optimized transport system, filtering and recommendation of contents and services
  35. 35. 35 Agenda 1. Introduction: – Context: societal urbanization and ageing – Interdependence analysis: Ambient Assisted Cities – ICT & Social Innovation leading towards Smarter Cities 2. Technologies for enablement of Smarter Cities: – Internet of Things – Web of Data – Crowdsourcing 3. Building Smarter Cities – Broad Data Analysis Tools – European projects about Smarter Ambient Assisted Cities 4. Conclusion
  36. 36. 36 Smarter Cities • Smarter Cities  cities that do not only manage their resources more efficiently but also are aware of the citizens’ needs. – Human/city interactions leave digital traces that can be compiled into comprehensive pictures of human daily facets – Analysis and discovery of the information behind the big amount of Broad Data captured on these smart cities deployment Smarter Cities= Internet of Things + Broad Data + Citizen Participation through Smartphones + Urban Analytics
  37. 37. 37 What is Future Internet? • Term which summarizes efforts to progress towards a better Internet, by means of: – Small incremental steps or – A thorough redesign (clean slate) and new architectural principles • Future Internet – –
  38. 38. 38 Future Internet Mission (FI) • Offer to users a a safe, efficient, trustable and robust environment, which: – Provides an an open, dynamic and desentralised access to the network and its information and – Is scalable, flexible and adapts its performance to the needs of users in their context
  39. 39. 39 Future Internet Vision
  40. 40. 40 Future Internet Pillars • Future Internet consists of 4 pillars based in a new network infrastructure as base: – Internet by and for People – Internet of Contents and Knowledge – Internet of Services – Internet of Things
  41. 41. 41 Future Internet Architecture
  42. 42. 42 Internet of Things (IoT): Motivation • Do you want to know how many steps you have walked? • Do you want to know how many kilometres you have driven? • How many watts have you consumed? • How to improve the efficiency and security in your factory? • Internet of Things can tell you this and much more
  43. 43. 43 IoT: Infografías
  44. 44. 44
  45. 45. 45 Internet of Things … connecting information, people and things
  46. 46. 46 Internet of Things: Definition • The internet of things (IoT) is the network of physical devices, vehicles, buildings and other items— embedded with electronics, software, sensors, and network connectivity that enables these objects to collect and exchange data – Opportunity for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit – Encompasses technologies such as Smart Grids, Smart Homes, Intelligent Transportation and Smart Cities
  47. 47. 47 6 facts about IoT 1. IoT is the term used to describe any kind of application that connected and made “things” interact through the Internet 2. IoT is a communication network connecting things which have naming, sensing and processing abilities 3. IoT is the next stage of the information revolution, i.e. the inter-connectivity of everything from urban transport to medical devices to household appliances 4. Intelligent interactivity between human and things to exchange information & knowledge for new value creation 5. IoT is not just about gathering of data but also about the analysis and use of data 6. IoT is not just about “smart devices”; it is also about devices and services that help people become smarter
  48. 48. 48 Sensors
  49. 49. 49 Connectivity
  50. 50. 50 People & Process
  51. 51. 51 Internet of Things Flavours • At least two flavours: – Consumer IoT (CIoT): oriented to consumers – Industrial IoT (IIoT) • Industry 4.0
  52. 52. 52 Consumer Internet of Things (CIoT) • The Consumer Internet of Things (CIoT) represents the class of consumer-oriented applications where: – Devices are consumer devices, such as smart appliances, e.g. refrigerator, washer, dryer, personal gadgets such as, fitness sensors, Google Glasses, etc. – Data volumes and rates are relatively low – Applications are not mission or safety critical, e.g., the failure of fitness gadget will make you, at worse, upset, but won’t cause any harm – CIoT applications tend to be “consumer-centric”
  53. 53. 53 Consumer IoT Sectors
  54. 54. 54 Personal data: SmartWatch & Health- promoting Data Devices
  55. 55. 55 Quantified Self & Life Logging • Quantified self is self-knowledge through self-tracking with technology – Movement to incorporate technology into data acquisition on aspects of a person's daily life in terms of inputs (e.g. food consumed, quality of surrounding air), states (e.g. mood, arousal, blood oxygen levels), and performance (mental and physical) • Self-monitoring and self-sensing through wearable sensors (EEG, ECG, video, etc.) and wearable computing  lifelogging • Application areas: – Health and wellness improvement – Improve personal or professional productivity • Products and companies: – Apple Watch, Fitbit tracker, Jawbone UP, Pebble, Withings scale
  56. 56. 56 Industrial Internet of Things (IIoT) • The Industrial Internet of Things (IIoT) represents industry-oriented applications where: – Devices are machines operating in industrial, transportation, energy or medical environment – Data volumes and rates tend to be from sustained to relatively high – Applications are mission and or safety critical, e.g. the failure of a smart grid impacts life and economy, misbehaving of a smart traffic system can threaten drivers – IIoT applications tend to be “system-centric”
  57. 57. 57 Smart Grid • A Smart Grid is an electrical grid which includes a variety of operational and energy measures including smart meters, smart appliances, renewable energy resources, and energy efficiency resources.
  58. 58. 58 Industry 4.0 • Industry 4.0, Industrie 4.0 or the fourth industrial revolution, is the current trend of automation and data exchange in manufacturing technologies. – It includes cyber-physical systems, the Internet of things and Cloud Computing. – Industry 4.0 creates what has been called a "smart factory".
  59. 59. 59 Internet of Things: Challenges 1. To process huge amounts of data supplied by “connected things” and to offer services as response 2. To research in new methods and mechanisms to find, retrieve, and transmit data dynamically – Discovery of sensor data — both in time and space – Communication of sensor data: complex queries (synchronous), publish/subscribe (asynchronous) – Processing of great variety of sensor data streams: correlation, aggregation and filtering 3. Ethical and social dimension: to keep the balance between personalization, privacy and security
  60. 60. 60 Value of IoT • Information within the Internet of Things creates value in a never-ending value loop consisting of 5 stages (CREATE … to ACT):
  61. 61. 61 IoT Key Components
  62. 62. 62 IoT Architecture Sensing Layer Communication Layer Management Layer
  63. 63. 63 IoT Operation
  64. 64. 64 Application Domains
  65. 65. 65 Combining IoT Domains
  66. 66. 66 IoT Enabling Technologies • Low-cost embedded computing and communication platforms, e.g. Arduino or Rapsberry PI • Wide availability of low-cost sensors and networks • Cloud-based Sensor Data Management Frameworks: Xively,  Democratization of Internet-connected Physical Objects
  67. 67. 67 iBeacon – a class of BLE devices that broadcast their identifier to nearby portable electronic devices (I)
  68. 68. 68 iBeacon – a class of BLE devices that broadcast their identifier to nearby portable electronic devices (II)
  69. 69. 69 IoT & Cloud Computing Interdependency • Cloud computing and IoT are tightly coupled – The growth of IoT and the rapid development of associated technologies create a widespread connection of “things.” • Leads to production of large amounts of data, which needs to be stored, processed and accessed – Cloud computing as a paradigm for big data storage and analytics • The combination of cloud computing and IoT will enable new monitoring services and powerful processing of sensory data streams.
  70. 70. 70 Edge Computing • Pushing the frontier of computing applications, data, and services away from centralized nodes to the logical extremes of a network. – It enables analytics and knowledge generation to occur at the source of the data.
  71. 71. 71 Evolution of the Web
  72. 72. 72 Semantic Web • Problems with current web: – The meaning of the web is not understandable by machines • Semantic Web creates a universal information Exchange means, providing semantics to the documents in the web – Adds meaning understandable by machines in the Web – Applies intelligent techniques which explore semantics – Lead by Tim Berners-Lee from W3C • Mission  “turning existing web content into machine-readable content“: Web of Data
  73. 73. 73 Semantic Web Stack • Semantic Web is composed of: – XML, syntax for structured documents – XML Schema, restricts the structure of XML documents – RDF is a data model which refers to objects and their relationships – RDF Schema, vocabulary to define properties and clases of RDF resource – OWL, adds more properties to RDFS, relationships among classes, cardinality, equality …
  74. 74. 74 RDF: Resource, Property and Value • RDF identifies concepts using Web identifiers (URIs) and describes resources with properties and values • Definitions: – A Resource is any thing which can have a URI, like "" – A Property is a Resource which has a name, like “author" or “web page" – A Property Value is the value of a Property, such as “Diego Ipiña" or "" (a value of a property may correspond to a resource)
  75. 75. 75 Resource Description Framework (RDF) • An RDF graph creates a web of distributed concepts – Performs assertions among logical relationships among entities – Information in RDF can be linked with graphs in other places – Through software inferences can be performed – There are query languages over Triple Stores like SPARQL • Through RDF we make information processable through machines – Software agents can store, Exchange, and use metadata about resources in the web • Ontology  hierarchy of terms to use in resource labelling, metadata formalization for a domain
  76. 76. 76 RDF Serialization Formats • RDF/XML format: 1: <rdf:RDF xmlns:rdf="" 2: xmlns:dc="" 3: xmlns:geo="" 4: xmlns:edu=""> 5: <rdf:Description rdf:about=""> 6: <geo:lat>43.270737</geo:lat> 7: <geo:long>-2.939637</geo:long> 8: <edu:hasFaculty> 9: <rdf:Bag> 10: <rdf:li rdf:resource="" dc:title="Facultad de Ingeniería"/> 11: <rdf:li rdf:resource="" dc:title="Facultad de Empresariales"/> 12: </rdf:Bag> 13: </edu:hasFaculty> 14: </rdf:Description> 15: </rdf:RDF> • N3/Turtle format: 1: @prefix rdf: <> . 2: @prefix dc: <> . 3: @prefix geo: <http://www.> . 4: @prefix edu: <> . 5: <> geo:lat "43.270737" ; geo:long "-2.939637" . 6: <> dc:title “Facultad de Ingeniería" . 7: <> edu:hasFaculty <> .
  77. 77. 77 RDF Graph Example • RDF graph generator: • RDF format converter:
  78. 78. 78 What is an Ontology? • An ontology defines concepts in a domain and relationships among them • The basic blocks that compose the design of an ontology are: – classes or concepts – properties of each concept describing several features and attributes of the concept – restrictions about properties • An ontology together with the instances of its individual classes conforms a knowledge base – Ontology examples:
  79. 79. 79 Features of Ontology Web Language (OWL) • An ontology differs from an XML schema since it represents knowledge, it is not a message format • The main advantage of an ontology written in OWL is that there are tools available which can reason over it • The syntax to Exchange information in OWL is normally RDF/XML. • OWL is an extension of the RDF vocabulary – Provides more constructs for stating logical expressions such as: Equality, Property Characteristics, Property Restrictions, Restricted Cardinality, Class Intersection, Annotation Properties, Versioning, etc. • Web ontologies are distributed • They can be imported and extended to create derived ontologies • Ontologies can be aligned or interlinked with others
  80. 80. 80 Ontology Engineering • An ontology: classes and properties (also referred to as schema ontology) • Knowledge base: a set of individual instances of classes and their relationships • Steps for developing an ontology: – defining classes in the ontology and arranging the classes in a taxonomic (subclass–superclass) hierarchy – defining properties and describing allowed values and restriction for these properties – Adding instances and individuals
  81. 81. 81 A simple methodology 1. Determine the domain and scope of the model that you want to design your ontology. 2. Consider reusing existing concepts/ontologies; this will help to increase the interoperability of your ontology. 3. Enumerate important terms in the ontology; this will determine what are the key concepts that need to be defined in an ontology. 4. Define the classes and the class hierarchy; decide on the classes and the parent/child relationships 5. Define the properties of classes; define the properties that relate the classes; 6. Define features of the properties; if you are going to add restriction or other OWL type restrictions/logical expressions. 7. Define/add instances
  82. 82. 82 Example: OWL Ontology Reasoning • Let’s suppose the following RDF model in N3: @prefix foaf: <> . <> foaf:author <> . <> foaf:author <> . <> foaf:author <> . • Although they belong to the same author, they are not related with each other, with the help of OWL we can map among URIs: @prefix owl: <> . <> owl:sameAs <> . <> owl:sameAs <> . • If we mix both models and execute a reasoner, we could answer to “retrieve all publications from “<>”: <>,< x/> y <>
  83. 83. 83 SPARQL • SPARQL ( allows to query RDF graphs through a simple language • SPARQL is ideal to extract and query information kept by applications, services or third party ad-hoc repositories expressed in RDF • It consists of three elements: – Query language – Mechanism to issue a query to a remote service for query processing – XML format in which results are returned • Good examples of SQL queries available at: –
  84. 84. 84 SPARQL Examples PREFIX table: <> SELECT ?symbol ?number FROM <> WHERE { { ?element table:symbol ?symbol; table:atomicNumber ?number; table:group table:group_17. OPTIONAL { ?element table:color ?color. } } UNION { ?element table:symbol ?symbol; table:atomicNumber ?number; table:group table:group_18. } } ORDER BY DESC(?number) LIMIT 10 OFFSET 10
  85. 85. 85 Embedded Metadata • We need that our data are ready to answer suitably to the questions of browsers and software agents – “Embedded metadata” are embedded data about data in a web page • 4 main options: – RDFa – complex system linked to XHTML – Microformats – extensively used and backed, makes use of old XHTML tags <a href="" rel="sweetheart date met">Jane</a> – Microdata – based on HTML5, supported by all modern browsers, intermediary complexity level – JSON-LD – latest recommended model • All together will help us to reach a web visión with more meaning, but still understandable by both humans and machines!! – From Web of Documents to Web of Data • Example:
  86. 86. 86 Linked Data • “A term used to describe a recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF.“ • Allows to discover, connect, describe and reuse all sorts of data – Fosters passing from a Web of Documents to a Web of Data • In September 2011, it had 31 billion RDF triples linked through 504 millions of links • Thought to open and connect diverse vocabularies and semantic instances, to be used by the Semantic community • URL: • Example: –
  87. 87. 87 Linked Data Principles 1. Uses URIs to identify things 2. Uses HTTP URIs to enable those things to be dereferenced by both people and user agents 3. Provides useful info (structured description and metadata) about a thing/concept referenced by an URI 4. Includes links to other URIs to improve related information discovery in the web
  88. 88. 88 Linked Data Life Cycle • Linked Data must go through several stages (several iterations on Linkage) before are ready for exploitation:
  89. 89. 89 Linked Data Example http://…/isb n978 Programming the Semantic Web 978-0-596-15381-6 Toby Segaran http://…/publi sher1 O’Reilly title name author publisher isbn http://…/isb n978 sameAs http://…/rev iew1 Awesome Book http://…/rev iewer Juan Sequeda http://juanseque hasReview hasReviewer description name sameAs livesIn Juan Sequedaname
  90. 90. 90 JSON & JSON-LD • JSON (JavaScript Object Notation) is open-standard format using human-readable text to transmit data objects consisting of attribute–value pairs. – It is the most common data format used for asynchronous browser/server communication (AJAX), largely replacing XML which is used by AJAX. – JSON's basic data types are: Number, String, Boolean, Array, Object, null – A JSON Schema provides a contract for the JSON data required by a given application, and how that data can be modified • JSON-LD, or JavaScript Object Notation for Linked Data, is a method of encoding Linked Data using JSON. – Some interesting examples at: •
  91. 91. 91 Example JSON-LD (I) { "@id": "", "@type": "Store", "name": "Links Bike Shop", "description": "The most "linked" bike store on earth!", "product": [ { "@id": "p:links-swift-chain", "@type": "Product", "name": "Links Swift Chain", "description": "A fine chain with many links.", "category": [ "cat:parts", "cat:chains" ], "price": "10.00", "stock": 10 }, { "@id": "p:links-speedy-lube", "@type": "Product", "name": "Links Speedy Lube", "description": "Lubricant for your chain links.", "category": [ "cat:lube", "cat:chains" ], "price": "5.00", "stock": 20 } ], "@context": { "Store": "", "Product": "", "product": "", "category": { "@id": "", "@type": "@id" }, "price": "", "stock": "", "name": "", "description": "", "p": "", "cat": "" } }
  92. 92. 92 Example JSON-LD (II) • Steps to obtain RDF graph: 1. Obtain N3 format for JSON-LD chart: • Copy/paste JSON-LD code • Select N-Quads format 2. Use RDF translator to convert to RDF/XML serialization: http://rdf- • Copy/paste N-Quads format • Select conversion from N-Triples to output RDF/XML • Click on submit and click on “Copy to Clipboard” 3. Use W3C RDF Validator to obtain triples and graph: • Select Triples and/or Graph display option • Click on Parse RDF 4. Review end result: triples + chart
  93. 93. 93 Example JSON-LD (III) • Graph obtained:
  94. 94. 94 Example JSON-LD (IV) • Triples obtained: Number Subject Predicate Object 1 "10.00" 2 3 "Links Swift Chain" 4 5 6 "10"^^ eger 7 "A fine chain with many links." 8 "The most "linked" bike store on earth!" 9 10 speedy-lube 11 swift-chain 12 "Links Bike Shop" 13 14 15 "Lubricant for your chain links." 16 "5.00" 17 "Links Speedy Lube" 18 "20"^^ eger 19
  95. 95. 95 • Initiative launched in 2011 by Bing, Google, Yahoo and then Yandex • Objective: “create and support a common set of schemas for structured data mark-up on web pages.” – Propose to use their schemas to annotate contents in a web page with metadata • Metadata are recognized by search engines and other parsers, thus accessing to the “meaning” of portals • Their vocabularies were inspired by earlier formats like Microformats, FOAF, GoodRelations and OpenCyc • Offer schemas in the following domains ( – Events, health, organization, person, place, product, offer, revisión and so on. • To map declarations in microdata to RDF the following tools can be used: • More info at: • Examples: – – (microdata.reveal Chrome plugin)
  96. 96. 96 • Linked Data highlights the Entities and Relationships in your data: Record Title: "War and Peace" Author: "Leo Tolstoy 1828-‐1910" ISBN: 0307266931 Entity ( Type: Work Name: "War and Peace" Author: Entity ( Type: Person Name: "Leo Tolstoy" Born: 1828 Died: 1910 Birthplace: Entity ( Type: Place Name: "Yasnaya Polyana“ SameAs:
  97. 97. 97 Google Knowledge Graph • Knowledge Graph is a knowledge base used by Google to improve the results obtained by its search engine by means of semantic search over information collected from a wide range of resources – It was added to Google search engine in 2012 • • Provides structured and detailed information about a given topic, together with a list of links to other sites • This information is derived from multiple resources including CIA World Factbook, Freebase and Wikipedia – Its semantic network contains more than 570 million objects and more than 18 million facts about – and relations among – those different objects which are used to understand the meaning of the terms used in the search query
  98. 98. 98 Google Knowledge Graph 98
  99. 99. 99 Avoiding Data Silos through Semantics in IoT • Cut-down semantics is applied to enable machine- interpretable and self-descriptive interlinked data – Integration – heterogeneous data can be integrated or one type of data combined with other – Abstraction and access – semantic descriptions are provided on well accepted ontologies such as SSN – Search and discovery – resulting Linked Data facilitates publishing and discovery of related data – Reasoning and interpretation –new knowledge can be inferred from existing assertions and rules – Knowledge extraction – for automated decision making and management
  100. 100. 100 Actionable Knowledge from Linked Data • Don’t care about the data sources (sensors) care about knowledge extracted from their data correlation & interpretation! – Data is captured, communicated, stored, accessed and shared from the physical world to better understand the surroundings – Sensory data related to different events can be analysed, correlated and turned into actionable knowledge – Application domains: e-health, retail, green energy, manufacturing, smart cities/houses
  101. 101. 101 Data Understanding through Linked Statistics & Visualizations
  102. 102. 102 Bringing together IoT and Linked Data: Sustainable Linked Data Coffee Maker • Hypothesis: “the active collaboration of people and Eco-aware everyday objects will enable a more sustainable/energy efficient use of the shared appliances within public spaces” • Contribution: An augmented capsule-based coffee machine placed in a public spaces, e.g. research laboratory – Continuously collects usage patterns to offer feedback to coffee consumers about the energy wasting and also, to intelligently adapt its operation to reduce wasted energy •
  103. 103. 103 Social + Sustainable + Persuasive + Cooperative + Linked Data Device 1. Social since it reports its energy consumptions via social networks, i.e. Twitter 2. Sustainable since it intelligently foresees when it should be switched on or off 3. Persuasive since it does not stay still, it reports misuse and motivates seductively usage corrections 4. Cooperative since it cooperates with other devices in order to accelerate the learning process 5. Linked Data Device, since it generates reusable energy consumption-related linked data interlinked with data from other domains that facilitates their exploitation
  104. 104. 104 Persuasive Interfaces to Promote Positive Behaviour Change GreenSoul, H2020 project 2016-2018, EE11
  105. 105. 105 Linked Data by IoT Devices • Modelling not only the sensors but also their features of interest: spatial and temporal attributes, resources that provide their data, who operated on it, provenance and so on – With SSN, SWEET, SWRC, GeoNames, PROV-O, … vocabularies
  106. 106. 106 Crowdsourcing • Crowdsourcing: process of obtaining needed services, ideas, or content by soliciting contributions from a large group of people, especially an online community, rather than from employees or suppliers – Collective intelligence is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making. • Some good examples: – Wikipedia & WikiData (its data version) • Tutorial: – Open Street Map – Stack Overflow: a language-independent collaboratively edited question and answer site for programmers
  107. 107. 107 CrowdSensing • Individuals with sensing and computing devices collectively share data and extract information to measure and map phenomena of common interest
  108. 108. 108 Wikipedia • Goal is to create a comprehensive and neutrally written summary of existing mainstream knowledge about a topic. – Allows you to create, revise, and edit articles – All information should be actually cited to reliable sources to evidence it is verifiable • Information is edited through wiki markup (wikitext) or newer VisualEditor – Wikipedia uses text codes called wiki tags to create particular elements (e.g. headings) • Cheat sheet: • An infobox is a fixed-format table designed to be added to the top right-hand corner of articles to present a summary of some unifying aspect that the articles share and sometimes to improve navigation to other interrelated articles. – – • Examples about editing Wikipedia contents: – –
  109. 109. 109 Wikidata & DBpedia • Wikidata is a volunteer-created knowledge base of structured data that anyone can edit – Focused on structured data: possible for humans and computers alike to use the data – Many ways to contribute to Wikidata: translate, write apps, add and edit data. – It works with: • Items – abstract concepts with theirs own and a unique identifier (Q###) and optionally a label, description and aliases • Statements are added to items: category of data as a property, while the data that describes an item for a given property is known as a value. – Example: entry for Everest mountain – Documentation: • DBpedia, a project to create a graph from Wikipedia data – allows users to semantically query relationships and properties associated with Wikipedia resources, including links to other related datasets – Wikipedia articles consist mostly of free text, but also include structured information embedded in the articles, such as "infobox" tables, categorisation information, images, geo-coordinates and links to external Web pages. • This structured information is extracted and put in a uniform dataset which can be queried:
  110. 110. 110 • OpenStreetMap is a tool for creating and sharing map information. – Anyone can contribute to OSM, and thousands of people add to the project every day. – After registering you can manipulate maps with the help of Edit with iD (in-browser editor) or JOSM (Java OpenStreetMap editor) – Represents physical features on the ground (e.g., roads or buildings) using tags attached to its data structures (nodes, ways & relations). – Data generated by the OpenStreetMap project are considered its primary output. • Its API accessed at: • Example (UNIA): – • Documentation:
  111. 111. 111 Creating a Web app with OpenStreetMap • Makes use of Leaflet: – JavaScript library for mobile-friendly interactive maps, provider-agnostic, meaning that it doesn’t enforce a particular choice of providers for tiles – Leaflet quick start guide: – You need to pick a suitable Tiles Server: • • Features of the example (Leaflet_Example-master): – set up a simple map using the Leaflet JavaScript library – load marker locations from a JSON file – change the marker icon – have the markers show some data when clicked • Documentation: – leaflet-and-openstreetmap/
  112. 112. 112 User-generated Data: Google Maps vs. Open Street Map • OSM is an excellent cartographic product driven by user contributions • Google Maps has progressed from mapping for the world to mapping from the world, where cartography is not the end product, but rather the necessary means for: – Google’s autonomous car initiative, combine sensors, GPS and 3D maps for self-driving cars. – Google’s Project Wing: a drone-based delivery systems to make use of a detailed 3D model of the world to quickly link supply to demand • By connecting the geometrical content of its Google Maps databases to digital traces that it collects, Google can assign meaning to space, transforming it into place. – Mapping by machines if not about “you are here”, but to understand who you are, where you should be heading, what you could be doing there!
  113. 113. 113 Data has changed • 90% of the world’s data was created in the last two years • 80% of enterprise data is unstructured • Unstructured data growing 2x faster than structured
  114. 114. 114 IoT & Big DataTensHundredsThousandsMillionsBillionsConnections Internet of Things Machine-to-Machine Isolated (autonomous, disconnected) Monitored Smart Systems (Intelligence in Subnets of Things ) Telemetry and Telematics Smart Homes Connected Cars Intelligent Buildings Intelligent Transport Systems Smart Meters and Grids Smart Retailing Smart Enterprise Management Remotely controlled and managed Building automation Manufacturing Security Utilities Internet of Things Sensors Devices Systems Things Processes People Industries Products Services Growth in connections generates an unparalleled scale of data Source: Machina Research 2014
  115. 115. 115 From M2M to IoT towards Big Data Data Big data Changing data models Real-time Processing Aggregation Internet of Things Large estates of devices Evolving applications All forms of data Data streaming and processing Pre-IoT (M2M) Limited estate of devices Single purpose applications Structured / Semi- structured Data transfers (sensors and actuators) Source: Machina Research 2014
  116. 116. 116 Data challenges of Smart Cities • Data coverage and access (openness) • Data integration and interoperability (data standards) – overcoming the silo and resistance to change • Data quality and provenance: veracity (accuracy, fidelity), uncertainty, error, bias, reliability, calibration, lineage • Quality, veracity and transparency of data analytics • Data interpretation and management issues • Paradigm shift towards data-driven decision making • Security and privacy: stem data breaches and fraud • Skills and organizational capabilities and capacities
  117. 117. 117 Smart City Data • Data is multi-modal and heterogeneous • Noisy and incomplete • Time and location dependent • Dynamic and varies in quality • Crowded sourced data can be unreliable • Requires (near-) real-time analysis • Privacy and security are important issues • Data can be biased- we need to know our data! • Data alone may not give a clear picture -we need contextual information, background knowledge, multi-source information and obviously better data analytics solutions…
  118. 118. 118 Smart Cities Data Exploitation • Discovery: finding appropriate device and data sources • Access: Availability and (open) access to data resources and data • Search: querying for data • Integration: dealing with heterogeneous devices, networks and data (Semantic interoperability) • Large-scale data mining, adaptable learning and efficient computing and processing • Interpretation: translating data to knowledge that can be used by people and applications • Scalability: dealing with large numbers of devices and a myriad of data and the computational complexity of interpreting the data.
  119. 119. 119 Analytics in the Smart City: Data- driven decision making
  120. 120. 120 Standardization in Smart Cities: Vocabularies and Indicators • UNE 178301 rule developed by AENOR (Spanish Association of Normalization and Certification) establishes a set of requisites for the reuse of Open Data generated by Public Administrations in Smart Cities. – 4#.VjmsffmrQU1 • ISO 37120:2014 indicators a) themes and b) energy example
  121. 121. 121 Nature of Broad Data: IoT + User- generated + Open Data • Heterogeneity makes IoT devices, people’s and governments’ data hardly interoperable • Data collected is multi-modal, diverse, voluminous and often supplied at high speed • Broad Data management imposes heavy challenges on information systems
  122. 122. 122 What is Big Data? • "Big Data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization“ Gartner, 2012 – The term “Big Data” was originated within the open source community, where an efforts was made to develop analytics processes which were faster and more scalable than the ones available at traditional Data Warehousing, and could extract value from the immense non-structured data volumes daily produced by web users • It is an opportunity to encounter perceptions in new emerging types of data and contents, to: – Make the business more agile – Answer complex queries which were before beyond your scope
  123. 123. 123 Big Data Evolution • Data explosion!! – 48 hours of data from stock market ~ 5 TB – Semi and non-structured data provided in real-time through social networks – Google processes PB/hour • Bioinformatics – huge datasets about genetics and drugs • Money whitening / terrorist funding, Spatial Data • 85% of Fortune 500 organizations are not able to process Big Data to gain competitive advantage – Gartner • Currently more than 1.9 zettabytes of data are being produced
  124. 124. 124 Ned for Big Data Analytics • Data Warehousing processes perception is that they are slow and limited in scalability • The need to converge data from various sources, both structured and non-structured • It is critical the access to information to extract value from data sources including mobile devices, RFID, web and a long list of automated sensor technologies
  125. 125. 125 Big Data Features
  126. 126. 126 Big Data’s 4 Vs
  127. 127. 127 IoT & Big Data • The more data that is created, the better understanding and wisdom people can obtain
  128. 128. 128 Types of Analytics (I)
  129. 129. 129 Types of Analytics (II) • Predictive analysis enables you to move from sense and respond to predict and act
  130. 130. 130 Types of Analytics (III)
  131. 131. 131 Types of Analytics (II)
  132. 132. 132 Evolution of Analytics Source: Moving from Descriptive to Cognitive Analytics on your Big Data Projects, Gene Villeneuve, IBM, Cognitive Analytics Predictive Analytics Prescriptive Analytics Descriptive Analytics Descriptive  “After-the-facts” analytics by analyzing historical data  Provides clarity as to where an enterprise or an organization stands related to defined business measures  Applied to all LoB for fact finding, visualization of success and failure Cognitive  Pertaining to the mental processes of perception, memory, judgment, learning, and reasoning  Range of different analytical strategies that are used to learn about certain types of business related functions  Natural language processing Predictive  Leverages data mining, statistics and ML algorithms, etc. to analyze current and historical data to predict future events and business outcome.  Discovers patterns derived from historical and transactional data to optimize business measures Prescriptive  Synthesizes big data, mathematical and computational sciences, and business rules to suggest decision options  Takes advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option
  133. 133. 133 Scope of Advanced Analytics Source: Moving from Descriptive to Cognitive Analytics on your Big Data Projects, Gene Villeneuve, IBM, What could happen in the future? Information Layer How is data managed and stored? How can everyone be more right… ….more often? Descriptive What has already happened? Predictive Prescriptive How can we achieve the best outcome? Cognitive How can we learn dynamically? BusinessValue  Reasoning  Learning  Natural Language  Alerts & Drill Down  Ad hoc Reports  Standard Reports  Big Data Platforms  Content Management  RDBMS and Integration  Machine learning  Forecasting  Statistical Analysis  Optimization  Rules  Constraints Big Data & Analytics
  134. 134. 134 Techniques to perform Analytics Source: Moving from Descriptive to Cognitive Analytics on your Big Data Projects, Gene Villeneuve, IBM, Natural, Intuitive or Automated Interaction ContextSpecificUsage Opportunities to infuse cognition and collaboration in existing solutions and products for differentiation Win on Innovation Compete on time to business value – through context specific data, methods, workflow. Reasoning Learning Natural Language Optimization Rules Predictive Modeling Forecasting Statistical Analysis Alerts Drilldown Query Ad-hoc Reports Standard Reports Big Data Platforms ECM Information Integration RDBMS
  135. 135. 135 How does Big Data Analytics work? Source: Virtualisation and Validation of Smart City Data. Dr Sefki Kolozali. Dr Payam Barnaghi
  136. 136. 136 Data Management Solutions in IoT
  137. 137. 137 Agenda 1. Introduction: – Context: societal urbanization and ageing – Interdependence analysis: Ambient Assisted Cities – ICT & Social Innovation leading towards Smarter Cities 2. Technologies for enablement of Smarter Cities: – Internet of Things – Web of Data – Crowdsourcing 3. Building Smarter Cities – Broad Data Analysis Tools – European projects about Smarter Ambient Assisted Cities 4. Conclusion
  138. 138. 138 Tools for Broad Data Analytics • There are many tools to curate, transform, analyse and visualize data: – Big Data processing tools: Apache Hadoop and Spark – OpenRefine – tool to transform messy data from the Internet – D3.js – JavaScript library to process and represent data – ELK stack – suite of tools that cover the capture, transform, store, index, search and visualization of tools
  139. 139. 139 Apache Hadoop • Hadoop is a free Java framework to process huge volumes of data in a distributed computing environment – Makes possible the execution of applications over system with thousands of notes which process thousands of terabytes – Its distributed file system facilitates the quick data transfer among nodes and permits to the system to operate continuously in the case of one node’s failure – Inspired by Google MapReduce, it is computation model where an applications is divided into various parts • Each of those parts (fragments or blocks) can be executed in any node of the cluster – The current ecosystem of Apache Hadoop consists of: • Hadoop kernel, MapReduce, the Hadoop Distributed File System (HDFS) and other related projects like Apache Hive, HBase and Zookeeper. – Used by big industry agents such as Google, Yahoo and IBM
  140. 140. 140 Apache Spark • Apache Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. – It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflow structure on distributed programs – Oriented to stream data processing allowing for CEP (Complex Event Processing)
  141. 141. 141 OpenRefine • OpenRefine (formerly Google Refine) is a powerful tool for working with messy data: – cleaning it; transforming it from one format into another; and extending it with web services and external data. • Available at: – Execute: google-refine.exe – Access in your browser: • Features: – Import data in various formats – Explore datasets – Apply basic and advanced cell transformations – Deal with cells that contain multiple values – Create instantaneous links between datasets – Filter and partition your data easily with regular expressions – Use named-entity extraction on full-text fields to automatically identify topics – Perform advanced data operations with the General Refine Expression Language •
  142. 142. 142 Open Refine Usage Example (I) • Click on "Create Project", then "Choose Files". Select the file from your computer (universityData.csv), then click on "Next“ • Click on Create Project, check that the box, "Parse cell text into numbers, dates, etc", is ticked! • The data contains variants of the names for several countries. To fix this, use Edit cells->Cluster and edit on the country column • To fix the number of students, use a Numeric facet, select non- numeric values and then Edit cells -> Transform and use GRE expressions value.replace("+", "") , value.unescape('url') , value.replace("~", "") , replace(",",""). Convert all cells into numbers with Edit cells -> Common transforms -> To number. Transform contents of non-numeric values containing text “Total” with value.replace(" total", "").replace(" -", ""). Wipe out rows containing values which could not be fixed: All -> Edit rows -> Remove all matching rows  remove the ones not complying
  143. 143. 143 Open Refine Usage Example (II) • Create a new numeric facet for endowment. Select only the non-numeric values, as was done for the number of students. • Other GRE expressions of interest, applied to endowment field: – value.replace("US $","").replace("US$", "") – toNumber(value.replace(" million", ""))*1000000 – toNumber(value.replace(" billion", ""))*1000000000 • Some expressions which can be used to process dates: – Edit cells -> Common transformations -> To date – Edit cells -> Transform  value.match(/.*(d{4}).*/)[0] – Edit cells -> Common transformations -> To date – Edit cells -> Transform, value.toString('yyyy'), value.toDate('MM/yy','MMM-yy').toString('yyyy-MM') • To remove duplicate keys: – Click on the column with duplicates "Sort". here is a new "Sort" menu at the top. – Click on this and select "Reorder rows permanently". – Edit cells -> Blank down, Facet -> Customized facets -> Facet by blank
  144. 144. 144 D3.js • D3.js (or just D3 for Data-Driven Documents) is a JavaScript library for producing dynamic, interactive data visualizations in web browsers. – Makes use of the widely implemented SVG, HTML5, and CSS standards • Embedded within an HTML webpage, D3.js uses pre-built JavaScript functions to select elements, create SVG objects, style them, or add transitions, dynamic effects or tooltips to them. • Binds arbitrary data to a Document Object Model (DOM), and then applies data-driven transformations to the document. For example: – Use D3 to generate an HTML table from an array of numbers – Use same data to create an interactive SVG bar chart with smooth transitions and interaction • Examples: • Documentation:
  145. 145. 145 D3.js Example // Data var data = [ { name:"Ireland", income:53000, life: 78, pop:6378, color: "green"}, { name:"Norway", income:73000, life: 87, pop:5084, color: "blue" }, { name:"Tanzania", income:27000, life: 50, pop:3407, color: "grey" } ]; // Create SVG container var svg ="#hook").append("svg") .attr("width", 120) .attr("height", 120) .style("fill", "#D0D0D0"); // Create SVG elements from data svg.selectAll("circle") // create virtual circle template .data(data) // bind data .enter() // for each row in data... .append("circle") // bind circle & data row such that... .attr("id", function(d) { return }) // set the circle's id according to the country name .attr("cx", function(d) { return d.income /1000 }) // set the circle's horizontal position according to income .attr("cy", function(d) { return }) // set the circle's vertical position according to life expectancy .attr("r", function(d) { return d.pop /1000 *2 }) // set the circle's radius according to country's population .attr("fill",function(d){ return d.color }); // set the circle's color according to country's color
  146. 146. 146 ELK Stack • ELK stands for Elasticsearch Logstash and Kibana which are technologies for creating visualizations from raw data. – Elasticsearch is an advanced search engine to search and filter through all sorts of data via a simple API. • The API is RESTful, so you can not only use it for data-analysis but also use it in production for web-based applications. – Logstash is a tool intended for organizing and searching logfiles • It is used for cleaning and streaming Big Data from all sorts of sources into a database. • Logstash also has an adapter for Elasticsearch, so these two play very well together. – Kibana is a visual interface for Elasticsearch in the browser. • The visualizations are configured completely through the interface. • Tutorial:
  147. 147. 147 ElasticSearch • Elasticsearch is a distributed, open source search and analytics engine: – Designed for horizontal scalability, reliability, and easy management. – Combines the speed of search with the power of analytics via a sophisticated, developer-friendly query language covering structured, unstructured, and time-series data • More details at: – • Run as elasticsearch.bat on Windows – curl -X GET http://localhost:9200
  148. 148. 148 LogStash • Tool to collect, process, and forward events and log messages – Collection is accomplished via configurable input plugins including raw socket/packet communication, file tailing, and several message bus clients. – Once an input plugin has collected data it can be processed by any number of filters which modify and annotate the event data. – Finally logstash routes events to output plugins which can forward the events to a variety of external programs including Elasticsearch, local files and several message bus implementations. • More details at: ‒ Run logstash
  149. 149. 149 Kibana • Kibana is an open source data visualization plugin for Elasticsearch. – Provides visualization capabilities on top of the content indexed on an Elasticsearch cluster. • Users can create bar, line and scatter plots, or pie charts and maps on top of large volumes of data • More details at: – Run ./bin/kibana (or binkibana.bat on Windows) – Point your browser to:
  150. 150. 150 Steps to use ELK 1. Choose the datasets 2. Data-cleaning with Logstash, e.g. csv source file into the database 3. Streaming the data into Elasticsearch 4. Creating visualizations using Kibana
  151. 151. 151 IES Cities Project • The IES Cities project promotes user-centric mobile micro-services that exploit open data and generate user-supplied data – Hypothesis: Users may help on improving, extending and enriching the open data in which micro-services are based • Its platform aims to: – Enable user supplied data to complement, enrich and enhance existing datasets about a city – Facilitate the generation of citizen-centric apps that exploit urban data in different domains European CIP project 2013-2016, Zaragoza & Majadahonda involved
  152. 152. 152 IES Cities Stakeholders • Citizens: – Users collaborate in the definition of the digital entity of the city. – Citizen produce and consumes contents (super-prosumer concept). • SMEs: – IES Cities will allow the creation of services benefiting the local businesses. • ICT-developing companies: – The platform will enable the chance to create new apps and services based on user needs, bringing new possibilities and added value. • Public administration: – The interaction with the users will enable them to improve and foster the use of their deployed sensors in urban areas and open databases
  153. 153. 153 IES Cities Objectives • To create a new open-platform adapting the technologies and over taking the knowledge from previous initiatives. • To validate and test a set of predefined urban apps across the cities. • To validate, analyse and retrieve technical feedback from the different pilots in order to detect and solve the major incidences of the technical solutions used in the cities. • To adequately achieve engagement of users in the pilots and measure their acceptability during the validations. • To maximize the impact of the project through adequate dissemination activities and publication of solutions upon a Dual-license model.
  154. 154. 154 IES Cities Player
  155. 155. 155 Bristol’s Democratree App
  156. 156. 156 Zaragoza’s Your Opinion Matters
  157. 157. 157 What´s WeLive (I) A novel We-Government ecosystem of tools (Live) that is easily deployable in different PA and which promotes co- innovation and co-creation of personalised public services through public-private partnerships and the empowerment of all stakeholders to actively take part in the value-chain of a municipality or a territory Open Data Open Services Open Innovation H2020 project 2015-2017, Bilbao council involved
  158. 158. 158 What´s WeLive (II) Stakeholder Collaboration + Public-private Partnership  IDEAS >> APPLICATIONS >> MARKETPLACE WeLive offers tools to transform the needs into ideas Tools to select the best Ideas and create the B. Blocks A way to compose the Building Blocks into mass market Applications which can be exploited through the marketplace
  159. 159. 159 WeLive proposes… Transform the current e-government approach into… WeLive Open and Collaborative Government Solution = We- government + t-government + I-government + m-government We- All stakeholders are treated as peers and prosumers t- Providing Technology tools to create public value l- To do more with less by involving other players and the PA as orchestrator m- Utilisation of mobile tech. for public services delivery
  160. 160. 160 How? (I) A novel We-Government ecosystem of tools (Live) that is easily deployable in different PA and which promotes co- innovation and co-creation of personalised public services through public-private partnerships and the empowerment of all stakeholders to actively take part in the value-chain of a municipality or a territory Open Data Open Services Open Innovation
  161. 161. 161 How? (II) Stakeholder Collaboration + Public-private Partnership  IDEAS >> APPLICATIONS >> MARKETPLACE WeLive offers tools to transform the needs into ideas Tools to select the best Ideas and create the B. Blocks A way to compose the Building Blocks into mass market Applications which can be exploited through the marketplace
  162. 162. 162 How? (III): WeLive Service co- creation approach Inspire and involve Challenge and Idea Generation Idea Evaluation and Selection Idea Refinement Suggested collaborators list Suggested PSAs list to user Suggested BBs and Datasets list to realize the PSA Get required BBs list Publish PSAs Idea Implementation PSA Deployment Open Service Layer Core BB Open Data Stack Research Query Get required Datasets list BBs and PSAs Registry and Execution environment Communities Analytics Dashboard Query mapper
  163. 163. 163 Page 163WeLive: A neW concept of pubLic administration based on citizen co-created mobile urban services Service co-creation and innovation vs Idea lifecycle Co- experience Co- definitionCo- development Co- delivery Inspire and involve Idea generation Evaluation and selection Refinement Implementation Monitoring
  164. 164. 164 Scenario-driven Artefact Definition per City 25/05/2016 1 – Agree a common methodology for stakeholders involvement and scenarios definition (benchmarking) 2a – Activities execution for insights gathering – stakeholder consultation process 2b –Deep analysis of city strategy and pilot focus, current IT and open data infrastructure 3a – Scenario #1 definition 3b – Scenario #2 definition 4a – New public service #1 4b – New public service #2 4g – New public service #7 4h – New public service #8 5 – Set of building blocks
  165. 165. 165 WeLive Vision/Architecture
  166. 166. 166 WeLive Web UI Controller
  167. 167. 167 WeLive RESTful API
  168. 168. 168 WeLive Apps: Bilbozkatu
  169. 169. 169 City4Age: Elderly-friendly City services for active and healthy ageing • Aims to act as a bridge between the European Innovation Partnerships (EIP) on Smart Cities and Communities & Active and Healthy Ageing (EIP AHA) • Demonstrate that Cities play a pivotal role in the unobtrusive collection of “more data”and with “increased frequency” for comprehending individual behaviours and improving the early detection of risks H2020 project 2016- 2018, PHC 21, Madrid is involved
  170. 170. 170 SIMPATICO • Addresses the need to offer a more efficient and more effective experience to companies and citizens in their daily interaction with Public Administration (PA) – Providing a personalized delivery of e- services based on advanced cognitive system technologies and by promoting an active engagement of people for the continuous improvement of the interaction with these services. H2020 project 2016-2018, EURO6, Xunta Galicia is involved
  171. 171. 171 PA traditional e-services vs. SIMPATICO approach
  172. 172. 172 Agenda 1. Introduction: – Context: societal urbanization and ageing – Interdependence analysis: Ambient Assisted Cities – ICT & Social Innovation leading towards Smarter Cities 2. Technologies for enablement of Smarter Cities: – Internet of Things – Web of Data – Crowdsourcing 3. Building Smarter Cities – Broad Data Analysis Tools – European projects about Smarter Ambient Assisted Cities 4. Conclusion
  173. 173. 173 Conclusion • We need cooperative cities and territories which are inclusive, participative, aware to the needs of all societal sectors • Technologies are the solution to do more with what we have, without having to invest in big amounts, but taking advantage of all the information that is already available, transforming knowledge, democratizing its access and usage, protecting and regulating its usage, and easing decision making among differnet actors
  174. 174. 174 I have a dream … the citizen- empowered inclusive City • Smart Cities must ensure social equity, economic viability and environmental sustainability, enabled by: – IoT: Smart Objects, e.g. enabling technology for inclusive cities which allows to collect data, e.g. people transiting through a given area – Web of Data: Open Data from a given council should be linked to real- time data gathered by sensor data (physical) and prosumed data by users (virtual sensors)  BROAD DATA – Citizen participation: smartphones running Location-aware Open Data apps which recommend to surrounding citizens and visitors according to their profile and capabilities • User-conscious apps should adapt to the capabilities of different users, their devices and current context
  175. 175. 175 I have a dream … the citizen- empowered inclusive City
  176. 176. 176 Towards Ambient Assisted Cities: Smarter, more Sustainable, Collaborative and Inclusive Cities Curso: Ambientes inteligentes para mejorar la salud, el bienestar y potenciar la autonomía UNIA. Baeza, Jaén, SPAIN, 31st August 2016, 12:00-14:30 Dr. Diego López-de-Ipiña González-de-Artaza
  177. 177. 177 References • Innovating the Smart Cities, Syam Madanapalli | IEEE Smart Tech Workshop 2015, smart-cities • Kitchin, R., Lauriault, T. and McArdle, G. (2015) Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards. Regional Studies, Regional Science 2: 1-28, • Towards Smart City: Making Government Data Work with Big Data Analysis, Charles Mok, 24 September 2015, data-work-with-big-data-analysis-53176591 • Mining in the Middle of the City: The needs of Big Data for Smart Cities, Dr. Antonio Jara, of-the-city-the-needs-of-big-data-for-smart-cities
  178. 178. 178 References • - Extending Benefits, Richard Wallis, benefits?qid=9ecd1fa6-cfac-460d-9bbb- 065879c46aee&v=&b=&from_search=3 • SDA-SmartCity – Overview and Challenges, Alessandra Mileo. Senior Research Fellow, Insight Centre for Data Analytics, NUI Galway, tutorial-eswc2015_files/ESWC-SD-INTRO-small.pdf • Internet of Things: Concepts and Technologies, Payam Barnaghi, things-concepts-and-technologies
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