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MUGGES: User-aware Semantic Location Models for Service Provision
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MUGGES: User-aware Semantic Location Models for Service Provision


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Talk given at UCAmI 2011:

Talk given at UCAmI 2011:

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  • Let’s consider a quick example of how to use MUGGESJack a colleague of mine comes to UCAmIand still hasn’t made up his mind between two interesting talksthat unfortunately are at the same timeAs he arrives he checks (on his phone) the muggesNotes in his surroundings.He note one particular muggesNote that already has five comments indicating that the talk in room COBAis will be really good.Jack knew about the talk but wasn’t decided if he should go to this one or the other talk in room 301 beginning at the same time.Eventually, he decides to follow the opinions of his fellow MUGGES users at the same spot.Later at lunch he creates a new note himself thanking the speaker for his talk and another recommending the steak!
  • Let’s take a look at the architecture of MUGGESThe information between a Mugglet consumer and its provider happens directly between two mobile devicesThere is, however, a server side infrastructure that facilitates the search of Mugglets and the establishes connection between consumer and providerMoreover, the server side (infrastructure) deals with the location part of MUGGES
  • Client software on the mobile phone to download templates (from Warehouse), customize Mugglets, share MuggletsClient software also has access to phone capabilities like GPS or the phone’s cameraController as unified interface of the server for the clientsAccounting Server: association between Mugglets and users for billing scenarioslogging functionality for statisticsDirectory of running mugglets (like an index) is kept in the WarehouseUser Management Server manages user’s connections to the system, profiles, keywords of interest, group managementLocation Server is responsible to get the appropriate locations based on the phones capabilities
  • The problem with “location” is that it is not a defined conceptIt has many facetsWe may refer to certain locations by their names, e.g. “Toulouse”, “France”, “Room 312”, “Mount Everest”OR by relations to other locations “the ice cream store is right next to the church”OR if we want to exactly pin down a location we may make use of a coordinate system, e.g. the World Geodetic System, which expresses a point on earth in Latitude, Longitude and optionally AltitudeThese different aspects show how heterogeneous location isAdditionally, to obtain a mobile phone’s location there are different possible approaches,e.g. using GPS or as soon as it’s ready GalileoIndoors where GPS won’t work, we might instead try to approximate the location by using cell tower or wifi signals (if the location of the emitting objects are known)Other times it might be necessary to type in a text description, e.g. an address, of a location we are looking for, like when getting directions in a route plannerAll this different aspects of location and location retrieval make it difficult to speak of “location”
  • Therefore, in the MUGGES project, we have identified three different notions of locationsTo combine them we introduced a virtual location named “MUGGES location”They are uniqueThis way we can create a consistent location model within MUGGESSo that Mugglets have only to deal with one type of location, the MUGGES locationTo manage the different types of locations and location mechanisms we have built aLocation Server on the MUGGES infrastructure side, which deals with these aspectsIt’s the Location Server’s duty to manage the different aspects of a MUGGES location, translate between themAnd obtain them using the location detection mechanisms, which are present on the phone
  • A more detailed “Prosumer Triangle”Every Mugglet in MUGGES is connected to one unique MUGGES LocationConsumer and Provider are connected via this MUGGES LocationThe server side maps the available location features automatically to a MUGGES locatione.g. GPS / Galileo coordinates, Matrix Code, Cell-IDConsistent location model to operate onThe granularity may vary, depending on the localizing feature present on the mobile phone (Cell-ID is coarse, GPS much better, a barcode exact)MUGGES Locations are created on the fly accessing different location services such as Geonames, Google Maps etc.We merge data from different servers to match the format we use in the MUGGES Location. These depend on location categories, such as City, Street, Room etc.For a detailed location model of a specific area, they may also be created and connected by hand
  • The location server’s duty is to hide the complexity of location searchIt automatically uses the best available location determination technology to locate the userWithin the MUGGES system it offers a consistent and coherent location concept with which the Mugglets can workAs such, translation between different location aspects work seamlessly, e.g. translation between symbolic and coordinatesMoreover, the Location Server will provide maps for the MUGGES Locations. These can be obtained via a map service like Google Maps, but also if needed introduced manually.As Mlocs already have a granularity attached to them (e.g. city, street, room), the map is zoomed automatically
  • To manage relations between Mlocs (the semantic side), we use ontologiesThree examples of possible ontologies to manage relations between MUGGES LocationsGeographic ontology should be clear, maps physical containment relations, e.g. France is situated in EuropeThe political ontology is only a little different to the geographic ontology, e.g. France is part of the European Union, where the European union is a political entity and not a geographical entityThe administrative ontology is kind of a special ontology we use to map relations between administrative entities that do not have a certain fixed location, e.g. the University of Deusto has on campus in the city of Bilbao and one in San Sebastian. These together make up the entity University of Deusto
  • In this example relationship we have the administrative entity “University of Deusto”which has two campus sites, on in Bilbao and the other in San SebastianThe respective campus sites are geographically contained in the cities of Bilbao respectively San SebastianThe University of Deusto has a subdivision called DeustoTech which is responsible for Technology transferDeustoTech is an institution and so University of Deusto and DeustoTech are connected via the Administrative connectionThe MoreLab, the department I am from on the one hand is administered by DeustoTechand on the other hand lies in on the Campus of Bilbao.Moreover, as you can see with the rooms at the bottom, we can manually insert relations such as “isNearTo”to indicate that these two rooms are close to each other, although they do not have any coordinates attached to each otherThis could also be inferred automatically, e.g. because both rooms are in children of the same department “MoreLab”What are these relations good for?Imagine a student wants to look for talks he is interested. Maybe someone has posted a muggesJournal,a small piece of information regarding news, on interesting topics. Sometimes interesting talks are heldOn the campus of Bilbao and sometimes they are in San Sebastian.Now instead of looking for Mugglets in the campus of Bilbao and then again for the Campus of San Sebastian,The student can directly search in the MUGGES location University of Deusto to get all the Mugglets that have to dowith the institution
  • So why do we use an ontology?First of all, this improves the search capabilities for mugglets.As we have seen in the example, Mugglets connected to child locations can only appear in a search in the parent location if these are connectedMoreover, with different ontologies we can model different relationships between locations, e.g. other than geogr. polit. relations, we could think of a touristic ontology, where interesting spots for tourists are connected, e.g. the next interesting tourist spot from where you are, but also dangerous places in the cityThrough reasoning we can infer attributes that are not directly contained in an Mloc, e.g. if it’s missing coordinates (e.g. for indoor locations, rooms, where we don’t know exactly which coordinates they have), we can approximate their location by using the coordinates of the parentOut of this relation we can also infer closeness or neighborhood based on locations with the same parent
  • So this is what I want you to take out of this presentation of MUGGESFirst of all the prosumer-concept gives users the power to be consumer, producer and provider of content (one step further from the Web 2.0)Mugglets, the MUGGES applications, are small, focused services for specific groups, usually located close to each other.They are immediately available and thus contain up-to-date information for the current situationTo abstract from different location attributes we have created the idea of a MUGGES Location which incorporates the different aspects we have identifiedAND creates a consistent location model within MUGGESAs last step
  • Transcript

    • 1. Introduction Architecture HS-LMS Trials User-Aware Semantic Location Models for Service Provision Dr. Diego López-de-Ipiña, Bernhard Klein, Christian Guggenmos, Jorge Pérez, Guillermo Gil DeustoTech – Deusto Institute of Technology, University of Deusto, Bilbao, Spain UCAmI 2011, Wednesday, December 7th 2011, Riviera Maya, MexicoUser-Aware Semantic Location Models for Service Provision 1 / 21
    • 2. Introduction Architecture HS-LMS Trials Table of Contents  MUGGES concept  The MUGGES platform architecture  A Hybrid User-aware LMS  Trialling an LBS Service  ConclusionUser-Aware Semantic Location Models for Service Provision 2 / 21
    • 3. Introduction Architecture HS-LMS Trials Introduction to MUGGES Mobile User Generated Geo Services  Mobile services directly accessed, created, and launched from mobile devices  User Generated users create content and type of services  Geo Services always connected to a locationUser-Aware Semantic Location Models for Service Provision 3 / 21
    • 4. Introduction Architecture HS-LMS Trials MUGGES Goals and objectives Beyond user generated contents  Users act as producer, consumer, and provider  From their mobile device Consumer Producer Super Prosumer Provider MobileUser-Aware Semantic Location Models for Service Provision 4 / 21
    • 5. Introduction Architecture HS-LMS Trials Mugglets MUGGES micro-applications  Sharply focused applications  Every user is potential creator and provider (“prosumer”)  From mobile to mobile  Instantaneously available  Short-term, i.e. up-to-date information  Location-basedUser-Aware Semantic Location Models for Service Provision 5 / 21
    • 6. Introduction Architecture HS-LMS Trials Use Case  Jack comes to the UCAmI conference  imagine it was a multi-track conference!!  Many interesting talks at the same time  “muggesNote” indicates an interesting talk in conference room COBA  At lunch Jack creates a note recommending the burritosUser-Aware Semantic Location Models for Service Provision 6 / 21
    • 7. Introduction Architecture HS-LMS Trials Prosumer Triangle  Information flows MUGGES server side directly between User Location Management Server mobile devices Server Accounting Server  Infrastructure to Location Profile Management Management Warehouse Billing (REST) (REST)  Enable search Management (REST) Mugglet Templates & Instances Controller (REST)  Provide Mugglet templates Search & instantiate Update location Templates (REST) (REST)Update location Search mugglets (REST) (REST)  Manage location mugglets (exchange information) (sockets) Provider Consumer MUGGES client sideUser-Aware Semantic Location Models for Service Provision 7 / 21
    • 8. Introduction Architecture HS-LMS Trials Mugglet CreationUser-Aware Semantic Location Models for Service Provision 8 / 21
    • 9. Introduction Architecture HS-LMS Trials MuggletsUser-Aware Semantic Location Models for Service Provision 9 / 21
    • 10. Introduction Architecture HS-LMS Trials Location Server Consumer Accounting Location MUGGES Server Server Client Software Controller User Warehouse Management Provider Server MUGGES ServerUser-Aware Semantic Location Models for Service Provision 10 / 21
    • 11. Introduction Architecture HS-LMS Trials Location Problem  Location has many facets  “Riviera Maya, Mexico”, “Room 312”  “COBA room is next to Hotel Riviera lobby”  “(lat: 20.894739516479788, lon: -87.2039794921875)”  Several location mechanisms  GPS / Galileo  Cell tower or Wi-Fi network ids  QR tags encoding location information  Text inputUser-Aware Semantic Location Models for Service Provision 11 / 21
    • 12. Introduction Architecture HS-LMS Trials MUGGES Location  Combination of location attributes  Physical (coordinates [43.604, 1.443])  Symbolic (“Riviera Maya”, “Coba Room, Paladium, Riviera Maya”)  Semantic (“next to Cancun, within Quintana Roo, Mexico”)  Unique location abstraction  Brings together the benefits of all worlds!User-Aware Semantic Location Models for Service Provision 12 / 21
    • 13. Introduction Architecture HS-LMS Trials Location Server Mugglet 1 Mugglet 3 “muggesNote” Mugglet 2 “muggesTrail” “muggesNote” Provider Consumer GPS Wi-Fi MUGGES Location Wi-Fi Bluetooth Cell-ID location/124531 coor- symbolic dinates semantics Map GPS to Map Wi-Fi+Cell-ID to MUGGES Location MUGGES Location Barcode Browse GPS Matrix Wi-Fi Location Hierarchy Galileo Code Cell-ID Geonames RFID... Google Maps precise mapping exact mapping Yahoo Maps manual mapping coarse mapping external OpenStreetMap Location Data services ... MUGGES Location ServerUser-Aware Semantic Location Models for Service Provision 13 / 21
    • 14. Introduction Architecture HS-LMS Trials Location Server  Transparent mapping of locations  Consistent location concept between Mugglets  Translation between different location aspects  Mapping service  Client-side provides context to personalize search results (mobility status aware)User-Aware Semantic Location Models for Service Provision 14 / 21
    • 15. Introduction Architecture HS-LMS Trials Location ontologies P:isParentOf A:isParentOf MUGGES MUGGES MUGGES MUGGES Location Location Location Location P:isChildOf A:isChildOf Political Ontology Administrative Ontology G:contains MUGGES MUGGES Location G:isNearTo Location G:isContainedIn Geographic OntologyUser-Aware Semantic Location Models for Service Provision 15 / 21
    • 16. Introduction Architecture HS-LMS Trials Location relations University A:isParentOf of Deusto A:isParentOf G:contains G:contains Campus of A:isParentOf Bilbao Campus of San San Bilbao Sebastian Sebastian P:isParentOf P:isParentOf G:contains DeustoTech Mugglet MoreLab A:isParentOf “Talk 1” Mugglet “Talk 2” G:contains G:contains Room 123 isNearTo Room 321User-Aware Semantic Location Models for Service Provision 16 / 21
    • 17. Introduction Architecture HS-LMS Trials Benefits  Better search capabilities  Mugglets in child show up in parent  Different ontologies cover different types of locations  Domain-specific semantic location overlays (e.g. touristic places)  Reasoning to enhance location-awareness:  Missing coordinates taken from parent location  Automatic adaptations on the location view depending on the mobility status  User-Aware LMS:  Dynamically choose location technology, meaning of “nearby”, adaptation of resultsUser-Aware Semantic Location Models for Service Provision 17 / 21
    • 18. Introduction Architecture HS-LMS Trials Trialling LBS Services  Two functional and technical evaluation of the MUGGES system were carried out:  Performance, acceptance, spatial-temporal usage and workflow were assessed :  Mugglet creation  Mugglet provisioning  Discovery and consumption patterns  Settings:  17 users aged 20-25 with Nokia 5800 XpressMusic  10 QR-instrumented areas to enable indoor location  Trial restricted to a 3km area  4 deployed social LBS applicationsUser-Aware Semantic Location Models for Service Provision 18 / 21
    • 19. Introduction Architecture HS-LMS Trials Trialling LBS Services  Observations:  MUGGES was mainly used in time passing situations  Mugglet creation is focused on meaningful locations or event locations  Provision presented performance problems when providing more than 30 mugglets or accepting more than 5 consumers in a mobile  Discovery is efficient when combining location restrictions accompanied by keyword search  Free-text search should be included  Wizards to enhance location-based searching should be added  Quantitative vs. qualitative location queriesUser-Aware Semantic Location Models for Service Provision 19 / 21
    • 20. Introduction Architecture HS-LMS Trials Summary  MUGGES service provisioning infrastructure eases LMS services through a user-aware Hybrid and Semantic Location Management System (HS-LMS)  Hybrid location model  Consistent concept of “location”  Reasoning between locations  User-mobility aware  A trial of MUGGESS has been carried out:  Highlighting some usability issues of our solution  Performace issues derived from prosumer implementation and 3G network behaviour  Service prosuming is promising but some usability and technical hurdles still need to be overcome!!User-Aware Semantic Location Models for Service Provision 20 / 21
    • 21. Introduction Architecture HS-LMS Trials Related Projects and Activities  Long term programme on the “super-prosumer” concept (2006-2012)  MUGGES trial took place in July/September 2010 +Ideas (PSE 2006/07 (Cénit, 2008) Basic New R&D Activities Research (FP7, others) (FP7, ICT) (ITEA2, 2009) (+New activities: (FP7, GALILEO) trials, demos, Market etc.) impactUser-Aware Semantic Location Models for Service Provision 21 / 21
    • 22. Introduction Architecture HS-LMS Trials User-Aware Semantic Location Models for Service Provision Questions???? This project has been supported by the FP7 MUGGES project grant no. 228297User-Aware Semantic Location Models for Service Provision 22 / 21