Territory-Wide People-Centric Services
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Territory-Wide People-Centric Services

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1st appointment with Smart City seminar series.

1st appointment with Smart City seminar series.

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Territory-Wide People-Centric Services Territory-Wide People-Centric Services Presentation Transcript

  • Territory-wide People-centric ServicesPaolo TraversoFBK Center for Information Technology - IRSTTrento RISEVia Sommarive 18,Povo, Trento, Italy
  • Territory-wide People-centric ServicesPaolo TraversoFBK Center for Information Technology - IRSTTrento RISEVia Sommarive 18,Povo, Trento, Italy
  • Overview • The Context & the Vision • The Vision through a First Pilot: Smart Campus • Context Aware Adaptive Services • Composition and Adaptation Techniques • Conclusions 3
  • Context: Trento RISETrento RISE, the “Trento Research Innovation &Education System”: Association of the University and FBK (the Research Centers) in IT in Trento Trento RISE is Part of the European Institute of Innovation and Technology in ICT, the “EIT ICT Labs” Mission: Research to drive the innovation & internationalization of the territory
  • The Context: EIT ICT Labs
  • EIT ICT Labs – The model 6
  • Helsinki StockholmEindhoven Berlin Paris Trento
  • ● ● Helsinki Stockholm Berlin ● ●Eindhoven● Paris ● Trento
  • From Trento RISE strategy slides:
  • Territorial Labs• Key business model enabler: territorial data and apps allow for new kinds of services• Territorial labs apply to all three dimensions: • Research: results tested in realistic settings • Education: hands-on experience for students in Masters and PhD courses and/or during summer/winter schools • Business: market tests, comparison among alternative business models, assessment of the impact on organizations 10
  • Territorial Labs• Key business model enabler: territorial data and apps allow for new kinds of services• Three ingredients: • Territory-wide data, systems, and applications • Open Service Platform • People as spectautors & prosumers 11
  • People as Spectaucthors & Prosumers Service Platform Territory-wide Data & Apps
  • People as Spectaucthors & Prosumers Service Platform Territory-wide Data & Apps
  • Journey PlannerFrom Users to Prosumers and Spectauthors Green Hero Discover Trento Participation Bike Bus Car pooling Gamification Feedback Events Fidelization Walking Points of interest Socialization Social/User data Personalization Multi-modality Services Awareness
  • Vision: Territory-wide People-centric services • Territory-wide Services • There is a huge amount of data and apps available in the territory • No way to build some value added services without them • People-centric Services • People can be spectautors and prosumers • People can be an enormous source of data, information & knowledge • Service platform + value added services as the bootstrapping motivation for people – social communities • Service Platform • Need for an a platform open to third party & user development • Need for SOA based, context aware, techniques • Need for run-time flexible adaptation techniques
  • Approach A territorial lab that realizes these concepts Dealing from the very beginning with the two main and difficult problems:  Useful and really used services  Users involved and really participating to the design and realization
  • Overview • The Context & the Vision • The Vision through a First Pilot: Smart Campus • Context Aware Adaptive Services • Composition and Adaptation Techniques • Conclusions 17
  • SMART CAMPUS – FIRST PILOT
  • Smart Campus SmartCampus offers advanced ICT solutions to catalyse the creativity, participation, and enthusiasm of campus people (and institutions), and to develop innovative services supporting the social and personal everyday life as well as the functioning of the campus.Smart Campus ingredients:• A platform for the development and provisioning of services for the campus• A community of end users involved in all the phases of the project A territorial lab to experiment innovative ICT solutions
  • Smart Campus SmartCampus offers advanced ICT solutions to catalyse the creativity, participation, and enthusiasm of campus people (and institutions), and to develop innovative services supporting the social and personal everyday life as well as the functioning of the campus.Smart Campus main characteristics:• Need for local university services (e.g. info about lectures, programs, exams)• Need for territory-wide services (e.g. mobility, culture, social life, gov support)• Most of students’ needs are citizens’ (people’s) needs• An easy & accessible community acting as spectautors & prosumers
  • Status of the Smart Campus Project… Start: 01/01/2012 Team: 18 people Platform: version 1 delivered in June Services: first set delivered in September Community: first 50 students/explorers
  • Status of the Smart Campus Project…  Start: 01/01/2012  Team: 18 people  Platform: version 1 delivered in June  Services: first set delivered in September  Community: first 50 students/explorers • Smart Campus: Milestones 50 students First Services Student Delivery for 500 students Community University of TrentoNOW < 2012 2013 > < 2013 2014 > ICT Days + EIT Summer School SMAU Milano Hackaton on Smart City (Trento)
  • SmartCampus: Approach Territorial Lab Res. challenges CommunityNew contributions User Interaction Social Networks Service Technology Exploitations Security and Trust Platform
  • SmartCampus Platform: Objective Open platform for the provisioning of services supporting everyday life activities of all the people (individuals and communities) and institutions in the campus. Smart Campus Service Platform
  • SmartCampus Platform: Architecture Front-end (multi-channel, multi-target) Enabler Service store #1 Run-time Enabler VAS VAS #1 #n #2 environment … VAS #2 Enabler #h Back-end (multi-provider, multi-technology) Internal System #1 … Internal System #m Internal System #m+1 … Internal System #m+l 3rd Party System #1 … 3rd Party System #k
  • Platform: Service Component Architecture (SCA) VAS USER Service front end more enablers VAS (SCA) application logics semantic enabler social- social- semantic ware ware logics social social- ESB enabler social- social ware ware logics VASDEVELOPER monitoring service rules ennabler domain objects Service back end services UniTN systems 3rd party systems (ESSE3, ADA, …)
  • Services: Needs collected from students• Video streaming • Bus finder• Collaborative notes taking • Interactive map• How difficult? Check the course • Paper eater• Thesis finder • Queue skipper• Study together • Mensa queue skipper• This is me! • Menu finder• Help your mates! • Lunchmate finder• Time bank • Accommodation finder• Uni voice • Car pooling• Action in Trento • Virtual parking lot• Worldwide Uni • Card all-in-one• Where are my friends? • Time manager• Discover Trento • ...
  • Overview • The Context & the Vision • The Vision through a First Pilot: Smart Campus • Context Aware Adaptive Services • Composition and Adaptation Techniques • Conclusions 28
  • AN EXAMPLE: JOURNEY PLANNER
  • Journey PlannerPersonalized and multi-modal planning of the journey Integrates the different mobility services offered by the territory  Bus, train, car/bike sharing, car pooling, parking, road condition, … Suggests personalized and contextual solutions based on user profile, user preferences and current mobility context  User: Disability, preferred transport means, type of journey (family, work,..)  Context: Train/bus delays, parking availability, strikes, .. Supports the user not only in the planning of the journey but throughout the journey execution  Parking payment, train booking, notification of problems (delays, jams..), proposal of alternative routes/solutions, ..
  • Journey PlannerChallenges and objectivesChallenges:• Customizable solutions for each user (e.g., profile, preferences, context)• Heterogeneity of services/systems involved (e.g., traffic/road condition sensors, train/bus delay detection systems, parking availability systems, parking payment systems, car pooling services, ..)• System Dynamicity (e.g., services/systems join/leave the system, changes in procedures of system facilities, changes in regulations and norms)• Context Dynamicity (e.g., unavailability/delays/malfunctioning of the different facilities)Objective: Fully exploit the benefits of the service-oriented paradigm to develop a context-aware adaptive system.
  • Journey Planner:Transport services and facilities in Trento area. Driving/Walking/Buses  Parking (free/public/private))  Directions + time info  Location, cost and closure info  Info buying tickets  Availability  Bus schedule, delays  SMS Payment Taxi  Car sharing  Booking via SMS  Pick up locations, cost info  Special service for disabled  Booking Trains  Car pooling  Schedule info on costs, info on delays  Offer/search ride  Bike sharing  Pick-up points  Availability
  • A Framework for Adaptive Context-aware SBSEntity Entity Business processes: • Partial process specifications that allow dynamic refinement and adaptationProvided Provided according to available systemFragments Fragments functionalities • Modeled via Adaptive Pervasive Flow Language (APFL) an extension ofBusiness Business traditional workflow language (BPEL)Process Process with abstract activities + preconditions/effects Process Fragments: Entity Entity • Offered functionalities that can be dynamically discovered/used by other entities Provided • Modeled as business processes Fragments Provided Fragments Context Model: Business • Important characteristics of the Process Business Process environment and of the entities that operate in it • Used to define context preconditions/effects on process activities and goals on abstract activities
  • Run time compositionParking Fragment Process Fragments Context Models Reach Reach Choose Park destination Leave ParkingFree Parking Fragments Blue Parking Fragments Private Parking Fragments Drive to Find Get Start Drive to Start Park Parking Parking Location Payment Parking Payment Leave End End Leave Leave Payment Payment Core ServicesTrentinoTrasporti TrentoParkInfo BP SMS Pay GeoLocation GoogleTransit PP RFID Pay PP NFC Pay
  • Run time compositionParking Fragment Process Fragments Context Models Reach Reach Choose Park destination Leave ParkingFree Parking Fragments Blue Parking Fragments Private Parking Fragments Drive to Find Get Start Drive to Start Park Parking Parking Location Payment Parking Payment Leave End End Leave Leave Payment Payment SMS Payment Fragments RFID Payment Fragments Get Prepare Send Get Cost Info S SMS S SMS Ticket Prepare Send Get Show Pay E SMS E SMS code Result Core ServicesTrentinoTrasporti TrentoParkInfo BP SMS Pay GeoLocation GoogleTransit PP RFID Pay PP NFC Pay
  • Run time compositionParking Fragment Process Fragments Context Models Reach Reach Choose Park destination Leave ParkingFree Parking Fragments Blue Parking Fragments Private Parking Fragments Drive to Find Get Start Drive to Start Park Parking Parking Location Payment Parking Payment Leave End End Leave Leave Payment Payment SMS Payment Fragments RFID Payment Fragments Get Prepare Send Get Cost Info S SMS S SMS Ticket Prepare Send Get Show Pay E SMS E SMS code Result Directions Fragments Get Show Get bus Bus Dir Direction info Get Show Get Show Driving Dir Direction Walking Dir Direction Core ServicesTrentinoTrasporti TrentoParkInfo BP SMS Pay GeoLocation GoogleTransit PP RFID Pay PP NFC Pay
  • Run time compositionParking Fragment Process Fragments Context Models Reach Reach Choose Park destination Leave ParkingFree Parking Fragments Blue Parking Fragments Private Parking Fragments Drive to Find Get Start Drive to Start Park Parking Parking Location Payment Parking Payment Leave End End Leave Leave Payment Payment SMS Payment Fragments RFID Payment Fragments Get Prepare Send Get Cost Info S SMS S SMS Ticket Prepare Send Get Show Pay E SMS E SMS code Result Directions Fragments Bus Info Fragments Get Show Get bus Bus Dir Direction info Get Show Bus Info bus info Get Show Get Show Driving Dir Direction Walking Dir Direction Core ServicesTrentinoTrasporti TrentoParkInfo BP SMS Pay GeoLocation GoogleTransit PP RFID Pay PP NFC Pay
  • Run time compositionParking Fragment Process Fragments Context Models G G Reach Reach Choose Park destination Parking Leave Domain • Transportation Means • Transport means per areaFree Parking Fragments Blue Parking Fragments Private Parking Fragments • Parking facilities and typesP, E P, E P, E • Parking payment methods Drive to Park Find Get Start Drive to Start • Parking availability Parking Parking Location Payment Parking Payment P, E P, E User P, E Leave End Leave End Leave • SMS Pay registration Payment Payment • RFID Pay registration • Bus pass SMS Payment Fragments RFID Payment Fragments • Disabilities/Walking limits • Preferences Get Prepare Send Get Cost Info S SMS S SMS Ticket Journey • Current location Prepare Send Get Show E SMS E SMS code Pay Result • Destination • Time • Travel mean Directions Fragments • Parking status • Payment status Bus Info Fragments Get Show Get bus Bus Dir Direction info Get Show Bus Info bus info Get Show Get Show Driving Dir Direction Walking Dir Direction Core ServicesTrentinoTrasporti TrentoParkInfo BP SMS Pay GeoLocation GoogleTransit PP RFID Pay PP NFC Pay
  • Run time composition Parking Fragment Process Fragments Context Models G G G Reach Reach Choose Park destination Parking Leave DomainG: Choosed p G: Parked in p • Transportation Means • Transport means per area Free Parking Fragments Blue Parking Fragments Private Parking Fragments • Parking facilities and types P, E P, E P, E • Parking payment methods Drive to Park Find Get Start Drive to Start • Parking availability Parking Parking Location Payment Parking Payment P, E P, E User P: pE a FreeParking P, is Leave End Leave End Leave • SMS Pay registration & p is Available Payment Payment • RFID Pay registration E: Parked in p • Bus pass SMS Payment Fragments RFID Payment Fragments • Disabilities/Walking limits • Preferences Get Prepare Send Get Cost Info S SMS S SMS Ticket Journey • Current location Prepare Send Get Show E SMS E SMS code Pay Result • Destination • Time • Travel mean Directions Fragments • Parking status • Payment status Bus Info Fragments Get Show Get bus Bus Dir Direction info Get Show Bus Info bus info Get Show Get Show Driving Dir Direction Walking Dir Direction Core Services TrentinoTrasporti TrentoParkInfo BP SMS Pay GeoLocation GoogleTransit PP RFID Pay PP NFC Pay
  • Run time composition Parking Fragment M: Available p Process Fragments Context Models G G G M Reach Reach Choose Park destination Parking Leave DomainG: Choosed p G: Parked in p • Transportation Means • Transport means per area Free Parking Fragments Blue Parking Fragments Private Parking Fragments • Parking facilities and types P, E P, E P, E • Parking payment methods Drive to Park Find Get Start Drive to Start • Parking availability Parking Parking Location Payment Parking Payment P, E P, E User P: pE a FreeParking P, is Leave End Leave End Leave • SMS Pay registration & p is Available Payment Payment • RFID Pay registration E: Parked in p • Bus pass SMS Payment Fragments RFID Payment Fragments • Disabilities/Walking limits • Preferences Get Prepare Send Get Cost Info S SMS S SMS Ticket Journey • Current location Prepare Send Get Show E SMS E SMS code Pay Result • Destination • Time • Travel mean Directions Fragments • Parking status • Payment status Bus Info Fragments Get Show Get bus Bus Dir Direction info Get Show Bus Info bus info Get Show Get Show Driving Dir Direction Walking Dir Direction Core Services TrentinoTrasporti TrentoParkInfo BP SMS Pay GeoLocation GoogleTransit PP RFID Pay PP NFC Pay
  • Adaptation Mechanisms and StrategiesAdaptation needs Need for refining an abstract activity within a process instance Violation of a precondition of an activity that is going to be executedAdaptation mechanisms Refinement: dynamic refinement of abstract activity by context-aware composition of available fragments Local adaptation: identify a fragment composition that allows to re-start a faulted process from a specific activity Compensation: dynamically compute a compensation process for a specific activityAdaptation strategies Combine adaptation mechanisms to solve complex adaptation problems  E.g., Re-refinement, Backward adaptation Search for alternative solutions  E.g., Local on current activity -> Backward on current refinement -> Re-refinement -> … One-shot vs incremental adaptation
  • Run time adaptation Parking Process Process Instances Context Configuration Reach Reach Choose Park destination Parking Leave Domain • Transportation Means • Transport means per area Blue Parking • Parking facilities and types • Parking payment methods Find Get Start • Parking availability Parking Location Payment User • SMS Pay registration = Yes • RFID Pay registration = No • Bus pass = Yes • Walk limits = max 0.5 km • Preferences Journey • Current loc = TrentoRISE • Destination = via Roma 5 • Time = Thu, 29 Mar, 14:35 • Travel mean = Car • Parking status = Choosed(p) • Payment status = Init Core Service InstancesGeoLocation
  • Run time adaptation Parking Process Process Instances Context Configuration Reach Reach Choose Park destination Parking Leave Domain • Transportation Means • Transport means per area Blue Parking • Parking facilities and types • Parking payment methods Find Get Start • Parking availability Parking Location Payment User • SMS Pay registration = Yes • RFID Pay registration = No • Bus pass = Yes • Walk limits = max 0.5 km • Preferences Journey • Current loc = Piazza Venezia • Destination = via Roma 5 • Time = Thu, 29 Mar, 14:45 • Travel mean = Car • Parking status = Found(p) • Payment status = Init Core Service InstancesGeoLocation
  • Run time adaptation Parking Process Process Instances Context Configuration Reach Reach Choose Park destination Parking Leave Domain • Transportation Means • Transport means per area Blue Parking • Parking facilities and types • Parking payment methods Find Get Start • Parking availability Parking Location Payment User • SMS Pay registration = Yes SMS Payment • RFID Pay registration = No • Bus pass = Yes Get Prepare Send Receive • Walk limits = max 0.5 km Cost Info S SMS S SMS Ack SMS • Preferences Journey • Current loc = Piazza Venezia • Destination = via Roma 5 • Time = Thu, 29 Mar, 14:45 • Travel mean = Car • Parking status = Found(p) • Payment status = Init Core Service InstancesGeoLocation BP SMS Pay
  • Run time adaptation Parking Process Process Instances Context Configuration Reach Reach Choose Park destination Parking Leave Domain • Transportation Means • Transport means per area Blue Parking • Parking facilities and types • Parking payment methods Find Get Start • Parking availability Parking Location Payment User • SMS Pay registration = Yes SMS Payment Adapt • RFID Pay registration = No • Bus pass = Yes Get Prepare Send Receive • Walk limits = max 0.5 km Cost Info S SMS S SMS Ack SMS • Preferences Journey Re-refinement • Current loc = Piazza Venezia Park Meter Payment • Destination = via Roma 5 Get PM Get Show Pay and • Time = Thu, 29 Mar, 14:45 Location Directions Direction Display • Travel mean = Walk • Parking status = Found(p) • Payment status = Init Core Service InstancesGeoLocation BP SMS Pay GoogleTransit TrentinoMobilitá
  • Run time adaptation Parking Process Process Instances Context Configuration Reach Reach Choose Park destination Parking Leave Domain • Transportation Means • Transport means per area Blue Parking Bus Directions Fragment • Parking facilities and types • Parking payment methods Find Get Start Get Show Get bus • Parking availability Parking Location Payment Bus Dir Direction info User • SMS Pay registration = Yes SMS Payment Adapt • RFID Pay registration = No • Bus pass = Yes Get Prepare Send Receive • Walk limits = max 0.5 km Cost Info S SMS S SMS Ack SMS • Preferences Journey Re-refinement • Current loc = Piazza Venezia Park Meter Payment • Destination = via Roma 12 Get PM Get Show Pay and • Time = Thu, 29 Mar, 14:50 Location Directions Direction Display • Travel mean = Bus • Parking status = Found(p) • Payment status = Done Core Service InstancesGeoLocation BP SMS Pay GoogleTransit TrentinoMobilitá
  • Run time adaptation Parking Process Process Instances Context Configuration Reach Reach Choose Park destination Parking Leave Domain • Transportation Means • Transport means per area Blue Parking Bus Directions Fragment • Parking facilities and types • Parking payment methods Find Get Start Get Show Get bus • Parking availability Parking Location Payment Bus Dir Direction info User • SMS Pay registration = Yes Adapt Bus Info • RFID Pay registration = No SMS Payment • Bus pass = Yes Get Show Get Prepare Send Receive Bus Info bus info • Walk limits = max 0.5 km Cost Info S SMS S SMS Ack SMS • Preferences Journey Re-refinement • Current loc = Piazza Venezia Park Meter Payment • Destination = via Roma 5 Get PM Get Show Pay and • Time = Thu, 29 Mar, 14:51 Location Directions Direction Display • Travel mean = Bus ADVANTAGES • Parking status = Found(p) • No need to think and implement all possible cases at design • Payment status = Done time • Run-time refinements consider the current context configuration • New services/fragments can be plugged-in at run-time simply wrapping them / annotating them (P,E,G) Core Service InstancesGeoLocation BP SMS Pay GoogleTransit TrentinoMobilitá TrentinoTrasporti
  • Overview • The Context & the Vision • The Vision through a First Pilot: Smart Campus • Territory-wide People-centric Mobility Services • Composition and Adaptation Techniques • Conclusions 48
  • ADAPTATION TECHNIQUES
  • Adaptation as AI Planning Problem BUILDER GOAL Adaptation G GΣ Goal Planning Goal Σ C1 PLANNER C1 STS2APFL CM2STS Context ... ΣC ...Configuration Madapt Σ Cm Synthesized Cm plan Σ || Adaptation Process Σ P1 Planning APFL2STS P1 Process domain ... ... Fragments Pn Σ Pn State Transition Systems Madapt composition of fragments that, if executed from the current configuration and in the absence of exogenous events, ensures that the resulting context configuration satisfies G.
  • Adaptation as AI Planning Problem BUILDER GOAL Adaptation G GΣ Goal Planning Goal Σ C1 PLANNER C1 STS2APFL CM2STS Context ... ΣC ...Configuration Madapt Σ Cm Synthesized Cm plan Σ || Adaptation Process Σ P1 Planning APFL2STS P1 Process domain ... ... Fragments Pn Σ Pn State Transition Systems APFL2STS, CM2STS Transformation of fragments and context configuration in STSs and removal of improbable events GOAL BUILDER Translation of the adaptation goal in EAGLE planning goal
  • Adaptation as AI Planning Problem BUILDER GOAL Adaptation G GΣ Goal Planning Goal Σ C1 PLANNER C1 STS2APFL CM2STS Context ... ΣC ...Configuration Madapt Σ Cm Synthesized Cm plan Σ || Adaptation Process Σ P1 Planning APFL2STS P1 Process domain ... ... Fragments Pn Σ Pn State Transition Systems Σ || Product of fragment and context STSs synchronized on preconditions and effects
  • Adaptation as AI Planning Problem BUILDER GOAL Adaptation G GΣ Goal Planning Goal Σ C1 PLANNER C1 STS2APFL CM2STS Context ... ΣC ...Configuration Madapt Σ Cm Synthesized Cm plan Σ || Adaptation Process Σ P1 Planning APFL2STS P1 Process domain ... ... Fragments Pn Σ Pn State Transition Systems PLANNER sophisticated AI planning techniques for WS composition developed (2002 – Today) within the ASTRO project (non-determinism, extended goals, data flow requirements)
  • Adaptation as AI Planning Problem BUILDER GOAL Adaptation G GΣ Goal Planning Goal Σ C1 PLANNER C1 STS2APFL CM2STS Context ... ΣC ...Configuration Madapt Σ Cm Synthesized Cm plan Σ || Adaptation Process Σ P1 Planning APFL2STS P1 Process domain ... ... Fragments Pn Σ Pn State Transition Systems STS2APFL Translation of the synthesized plan into an APFL executable adaptation process
  • Sara Lifecycle Marco John Platform App App provider provider usersHCI HCI Tech HCI Tech HCI Tech HCI TechI/P M Interaction & Presentation Models CustomizationC&A & Adaptation Techniques Process Context Fragments ModelsDMSW Core Services
  • Sara Lifecycle Marco John Platform App App provider provider usersHCI HCI Tech HCI tech HCI Tech HCI Tech HCI Tech HCI Tech HCI Tech HCI Tech selectionI/P M Interaction & Presentation Models CustomizationC&A C&A Strategies & Adaptation configuration Rule1 Rule1 Rule2 Rule2 Techniques Rule1 Rule1 Rule2 Rule2 Rule1 Rule2 Rule1 Rule2 Rule1 Rule2 Process Context Fragments Models FragmentsDM customization Service selectionSW Core Services
  • Sara Lifecycle Marco John Platform App provider providerHCI HCI Tech HCI tech HCI Tech HCI Tech HCI Tech HCI tech HCI Tech HCI Tech HCI Tech selection enactmentI/P M Interaction & I/P instances Presentation Models CustomizationC&A C&A Strategies C&A & Adaptation configuration Rule1 Rule1 Rule2 Rule2 enactment Techniques Rule1 Rule1 Rule2 Rule2 Rule1 Rule2 Rule1 Rule2 Rule1 Rule2 Process Context Fragments Models Context FragmentsDM customization configurations Fragment instances Service selection Service instancesSW Core Services
  • Sara Lifecycle Marco John Platform App provider providerHCI HCI Tech HCI tech HCI Tech HCI Tech HCI Tech HCI tech HCI Tech HCI Tech HCI Tech selection enactmentI/P M Interaction & I/P instances Presentation Models CustomizationC&A C&A Strategies C&A & Adaptation configuration Rule1 Rule1 Rule2 Rule2 enactment Techniques Rule1 Rule1 Rule2 Rule2 Rule1 Rule2 Rule1 Rule2 Rule1 Rule2 Process Context Fragments Models Context FragmentsDM customization configurations Fragment instances Service selection Service instancesSW Core Services
  • Sara Lifecycle Marco John Platform App provider providerHCI HCI Tech HCI tech HCI Tech HCI Tech HCI Tech HCI tech HCI Tech HCI Tech HCI Tech selection enactmentI/P M Interaction & I/P instances Presentation Models CustomizationC&A C&A Strategies C&A & Adaptation configuration Rule1 Rule1 Rule2 Rule2 enactment Techniques Rule1 Rule1 Rule2 Rule2 Rule1 Rule2 Rule1 Rule2 Rule1 Rule2 Process Context Fragments Models Context FragmentsDM customization configurations Fragment instances Service selection Service instancesSW Core Services
  • Adaptation Techniques and Demonstrator Demonstrator ASTRO-CAptEvo:  WINNER ServicesCUP 2012  Advanced service-based solutions for real- world problems  Best Paper Award ICWS 2012Demo: http://www.astroproject.org/downloads/captevoDemoVideo: http://www.astroproject.org/downloads/captevoVideo.zip
  • CONCLUSIONS
  • Conclusions• Territory-wide Services • There is a huge amount of data and apps available in the territory • No way to build value added services without them• People-centric Services • People can be spectautors and prosumers • People can be an enormous source of data, information & knowledge • Pure social nets - data driven eager approaches bound to failure (the next incoming “big data” failure?) • Service platform + value added services as the bootstrapping motivation for people – social communities• Service Platform • Need for SOA based, context aware, techniques • Need for run-time flexible adaptation techniques
  • Special Thanks to Annapaola Marconi, Marco Pistore, and the Smart Campus Team
  • Territory-wide People-centric ServicesPaolo TraversoFBK Center for Information Technology - IRSTTrento RISEVia Sommarive 18,Povo, Trento, Italy
  • JourneyPlanner: High-level Architecture Marco Sara John Representation of concepts and data for the interaction with the user Service Store Interaction/Presentation ENABLERS Models Techniques for dynamic Run-time user-driven VAS2 environment Customization & Adaptation composition/adaptation of VAS1 VAS3 service-based applications VASx Domain Models Formal representation of domain concepts and expected behavior Back-endTechnology- and provider- Mobility services and facilities providedindependent wrapping of by the territory (WS, HTML/PhP, Javaterritorial services API, SMS-based, e-mail based, .. )