Overview of Social Networks

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Overview of Social Networks

  1. 1. Sistemi DistribuitiCorso di Laurea in Ingegneria Prof. Paolo Nesi Part 19 – Overview of social Network Department of Systems and Informatics University of Florence Via S. Marta 3, 50139, Firenze, Italy tel: +39-055-4796523, fax: +39-055-4796363 Lab: DISIT, Sistemi Distribuiti e Tecnologie Internet nesi@dsi.unifi.it paolo.nesi@unifi.it http://www.disit.dsi.unifi.it/ http://www.dsi.unifi.it/~nesi Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 1
  2. 2. Overview of Social NetworkSocial NetworksClassification of Social NetworksUser Generated Content, UGCMeasures of Social NetworksRecommendations and complexityComparison e InteroperabilityMobile Medicine A view inside a social networkECLAP Best Practice Network A view inside a social network Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 2
  3. 3. IntroductionWith the users demand in collaborating and sharinginformation some Social Networks have been createdSocial Networks are (OECD, Organisation for Economic Co-operation and Development) web portals that: Allow users to provide and share User Generated Content Allow users to valorize their creative effort, the content should be originally produced by the users, take a picture, compose a set of images, sync. images and audio, etc. Allow users to produce content by using solutions and non professional techniquesOther solutions using UGC are Blogs, Wiki, Forum, etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 3
  4. 4. TerminologySocial Network A paradigm of user interaction and behavior on the webSocial Media A Social network based on mediaSocial TV A TV based on Social Networking principles, with the support of UGC, etc.Social Network Analysis The discipline to analyze the social network in terms of user clustering and relationships, metrics for SN assessment, etc.. It can be used to better understand motivation and rationales of success and/or problems. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 4
  5. 5. Overview of Social NetworkSocial NetworksClassification of Social NetworksUser Generated Content, UGCMeasures of Social NetworksRecommendations and complexityComparison e InteroperabilityMobile Medicine A view inside a social networkECLAP Best Practice Network A view inside a social network Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 5
  6. 6. Classification of Social NetworksContent Based Social Network: Collect content and show them to users according to their preferences Content correlation, recommendations Examples: YouTube, Last.fm, FlickrUser Based Social Network : User collection, user profiled Audio and video are used to better describe the user profile, in some cases, they are only visible to their friends User Recommendations, taking into account a large number of user description aspects Examples: FaceBook, Orkut, FriendsterMySpace is a mix of both categories. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 6
  7. 7. Classification of Social NetworksBest Practice Networks Both content and users Tools for collaborative works: forum, collab. Documents Events and calendar Target is on specific goalsEnterprise Social Network Social network for companies Sharing files Planning activities and meetings Direct Chat and netmeeting Capitalize knowledge of the company, email integrated…Social Learning Management Systems Stimulating users and students Courses, search content Target is on the personal knowledge growth. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 7
  8. 8. Overview of Social NetworkSocial NetworksClassification of Social NetworksUser Generated Content, UGCMeasures of Social NetworksRecommendations and complexityComparison e InteroperabilityMobile Medicine A view inside a social networkECLAP Best Practice Network A view inside a social network Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 8
  9. 9. User Generated Content, UGCUGC is: content files, metadata, tags, GPS, etc. ….allConditions that Facilitated the grown of UGC Reduced costs for equipments which allow the personal content production: cameras, smart phones, etc. Reduced costs of connection, increment of broadband diffusion More Web Interactive capabilities: Ajax, JSP Creative Commons Licensing/formalisms, increment of confidencePros and Facilitations Growing of WEB sites that host your content and provide some tools to make them accessible on web for your friends Natural selection/emergence of better UGC items, increment of visibility for some of UGC users… Annotation and reuse of UGC of others Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 9
  10. 10. User Generated ContentCons and problems Restricted social penetration since only IT skilled and a certain economical capability may access now Lack of formal Privacy control Too many information are requested Some people do not expose their true personal info Problems for the direct upload of content from mobiles. IPR problems: Violation of IPR of third party, free usage of UGC Lose of control of your own UGC Reuse and annotation of professional content Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 10
  11. 11. User Generated ContentCons and problems Lack of interoperability for users and content among different social networks: Initially performed to keep connected the users Secondly a point of cons since users tend to pass from one SN to another Content is not completely defined in terns of Metadata Competitions of UGC against professional content, producers are against their support and diffusion Growing costs for the SN providers Content volume in the hand of the SN organizers is growing • Users would like to see older content still accessible Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 11
  12. 12. User Activities on Social NetworksWikipedia (2006) 68000: active users 32 millions of lurkers While the 1000 more active users produced the 66% of changes.Similar numbers in other portals: 90% lurkers 9% occasional users 1% active users 90% is produced by the 1% of active users 10% is generated by the 9% of users including the occasional Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 12
  13. 13. Social Network Activities meaningSince the 90% is managed by a small percentage ofactive users:  Votes are also produced with the same small part of the community  Comments are also produced with the same small part of the community  Pushers are frequently needed to create activities and waves into the Social Networks, they create fashions and interests among the lurkers, etc.. ..Number of plays are produced by the wholecommunity Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 13
  14. 14. Overview of Social NetworkSocial NetworksClassification of Social NetworksUser Generated Content, UGCMeasures of Social NetworksRecommendations and complexityComparison e InteroperabilityMobile Medicine A view inside a social networkECLAP Best Practice Network A view inside a social network Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 14
  15. 15. Relevance of UsersNumber of Connections with other usersDirect connections, Second and third level connections, Etc.Number of accesses to their profile page (if any) posted and/or preferred content Comments groupsUsers’ Activities Number of Posted content Number of posted comments Number of votes, etc. Number of accesses Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 15
  16. 16. Stanford Social Web Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 16
  17. 17. Main Geographical User/Group Distribution -Larger groups are represented with bigger dots -Multiple connections are represented with stronger lines Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 17
  18. 18. Issues on Communitie GraphsPresence of a main Center of gravity Presence of dense groupsPresences of remotely located smaller Groups Self connections among these people Some of these smaller remote groups are linked with the rest via 1 or more chains of single people Depending on their activities, there is a risk of losing those communities is evidentNumber of Connections Distribution of connections Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 18
  19. 19. Metriche per le Social NetworkSocial Network AnalysisDegree of Centrality per un Nodo: Numero di collegamenti incidenti sul NodoEccentricity of Centrarlity per un Nodo: La dist. massima fra le distanze minime di tale nodo e ogni altro nodo della reteCloseness of Centrality per un certo Nodo: Reciproco della somma delle distanze tra il nodo e tutti gli altri nodiBetweenness Centrality per un certo nodo: Quanta informazione passa per quel nodo. Somme delle quantita’ di informazione che passa fra tale nodo ed ogni altro nodo della rete. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 19
  20. 20. Matrix of connections CarolAndre Diane Fernando Heather Jane A[i][j]: matrix of connectionsBeverly Ike Garth Ed Dian Fernand Beverl Heath Aij Carol Andre e o y Ed Garth er Ike Jane NC Carol 0 1 1 1 0 0 0 0 0 0 3 Andre 1 0 1 1 1 0 0 0 0 0 4 Diane 1 1 0 1 1 1 1 0 0 0 6 Fernando 1 1 1 0 0 0 1 1 0 0 5 Beverly 0 1 1 0 0 1 1 0 0 0 4 Ed 0 0 1 0 1 0 1 0 0 0 3 Garth 0 0 1 1 1 1 0 1 0 0 5 Heather 0 0 0 1 0 0 1 0 1 0 3 Ike 0 0 0 0 0 0 0 1 0 1 2 Jane 0 0 0 0 0 0 0 0 1 0 1 nc 3 4 6 5 4 3 5 3 2 1 36 Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 20
  21. 21. Social Network Analysis MetricsDegree of Centrality of a node Carol Number of connections to a certain node Andre Fernando can be non symmetric if the Diane Heather Jane relationships are not symmetric, thus Beverly Ike Garth the graph is oriented Ed Diane has 6 connections is connected to others which are in turn connected each other. It is not true that to count connections is the best model to identify the most relevant node. In the above case: Diana is connected to people that are in any case connected each other. While Heather is central to keep Ike and Jane connected to the rest of the network !! Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 21
  22. 22. Averaged Number of connectionsTotal number of connections divided by the number of NodesAccording to the examples above: NC, Number of connections: 36 they are considered non bidirectional otherwise they should be 18 NN, Number of nodes: 10 Carol Andre FernandoAveraged number of connections: Diane Heather Jane ANC: 36/10, 3.6 connections per node Beverly Ike Garth ANC: 18/10, 1.8 connections per node EdIt is more similar to the user perception to say 3.6 connectionsrather then 1.8 Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 22
  23. 23. Carol Matrix of distancesAndre Diane Fernando D[i][j]: matrix of distances Heather Jane N*(N-1)/2 elementsBeverly Ike Garth Ed Ferna Beverl Heath Dij Carol Andre Diane ndo y Ed Garth er Ike Jane Carol 0 1 1 1 2 2 2 2 3 4 18 Andre 0 1 1 1 2 2 2 3 4 16 Diane 0 1 1 1 1 2 3 4 13 Fernando 0 2 2 1 1 2 3 11 Beverly 0 1 1 2 3 4 11 Ed 0 1 2 3 4 10 Garth 0 1 2 3 6 Heather 0 1 2 3 Ike 0 1 1 Jane 0 0 89 Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 23
  24. 24. Averaged shortest path from one person to anotherMIT: 6.4 hops Our example: 1.97 hops Sum of shortest paths: 89Stanford: 9.2 hops 10 Nodes 45 possible connections Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 24
  25. 25. Measures of Network StructureCohesion n-dimensions distances, reachability, flow diffusion of information / communicationRegions or Groups/Communities components, bi-components, k-cores/means partitioning of the network into distinct partsSubgroups cliques, clans, sets, factions partitioning of the network into subsets - overlap possible strategic alliances, collaborators / competitors Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 25
  26. 26. Overview of Social NetworkSocial NetworksClassification of Social NetworksUser Generated Content, UGCMeasures of Social NetworksRecommendations and complexityComparison e InteroperabilityMobile Medicine A view inside a social networkECLAP Best Practice Network A view inside a social network Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 26
  27. 27. Semantic DescriptorsModeling descriptors with formalisms: XML MPEG-7, metamodel for descriptors and descriptors MPEG-21: item descriptor and/or packageAudio, Video, images: Low level fingerprint/descriptors Hash, MD5, etc. High level fingerprint/descriptors Genre, rhythms, color, scenes/movements, etc. Evolution of them along the time, along the fileDocuments: Keywords extractions, multilingual agnostic, … Summarization Paragraphs modeling and descriptions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 27
  28. 28. Semantic Computing on SNSemantics Different types of Descriptors User, content, etc.How semantics processing has supported SN Indexing and querying Suggestions and recommendationsTools for Semantic Computing Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 28
  29. 29. The centrality of User profileUser Profile Static information Name, surname, Nationality Genre, age, languages, etc..other personal info,.. School, work, family, etc. photo, etc.. Economical dataUser Profile Dynamic Information Explicit Preferences in terms of content, friends, votes, ranks, recommendations, etc.. Actions: play, comments, votes, Frequency of access Etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 29
  30. 30. Esempio di Profilo utentiInformazioni statiche: Informazioni dinamiche: •Informazioni generali: •Lista di oggetti preferiti •nome, cognome, sesso, •Lista di amici • foto, data di nascita, •descrizione personale, •Lista gruppi •località di provenienza (ISO 3166), •Voti positivi ad oggetti •Nazione •Commenti ad oggetti •Suddivisione •… •Provincia •… •lingue parlate (ISO 369) •Informazioni sulle preferenze •Informazioni di contatto: sulla base delle •lista di contatti di instant messaging visualizzazioni degli oggetti •Scuola e Lavoro: •scelta del livello scolastico, •Format •nome della scuola, •Type •tipo di lavoro, •Taxonomy •nome del posto di lavoro •Interessi: •Vettore contenente la lista di valori del campo Type degli oggetti scelti dall’utente Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 30
  31. 31. Votes/ranks, Comments, preferredUsers may leave on Content and Users: Comments Ranks and VotesComments may be left as Text or contentUser may mark the preferred content and users (friends) Preferred content are accessible with a direct list to shortening the time for their play Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 31
  32. 32. RecommendationsThey are a means for the Usage of content/object info to find/propose users Usage of users info to find/propose content Usage of users info to find/propose other users Etc..Different Recommendations U  U: a user to another user on the basis of his profile O  U: an object at a user on the basis of his profile O  O: an object on the basis of a played object of a user G  U: a group to a user Etc…Objects can be Advertising, Ads, Content, Events,Groups, etc.…. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 32
  33. 33. Recommendations Recipient of the suggestions User Content Group (played by a user) (leader or members) Users Proposing to a user Proposing at a group possible colleagues / --no sense-- responsible possible friends interested colleagues to be invitedSuggested elements Contents Proposing to a user Proposing at a play of a Proposing at a group possible interesting content similar content members possible interesting contents items content (not much different with respect to C-C combination) Groups Proposing to a user Proposing at a play of a possible interesting content possible interesting --no sense-- groups groups in which similar contents are discussed Ads Proposing to a user Proposing at a play of a Proposing at a/all group possible interesting ads content the possible member/s possible interesting ads interesting ads Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 33
  34. 34. Different RecommendationsFOR YOU: Suggesting Users to another Users since they have similar preferences like/prefer what you like/prefer are friends of your friends are in one or more of the your groups are new of the SN! are the most linked, the most grouped, etc.FOR THE SN: Suggesting Users to another Users since they are important for the SN and do not have to left alone, the new entry are the only contact path for Connecting a remote group, if the path is left a peripheral group will be completely disjoined with respect to the rest of the SN … Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 34
  35. 35. Complexity of RecommendationEach day N new users reach the SN,The SN has to suggest its possible friends immediately: 1 Million of users in the SN (number of users, U=10^6) N*U distances to be estimated in real time/per day Complexity is an O(NU) Thus: 10^12 estimations of 10ms, thus 10^10s, 317 years !!!Each day M new UGC items are posted on the SN,The SN has to estimate the distance of that content with respectto all the other items/objects and users: 1 Million of content in the SN (number of content, C=10^6) M*C distances to be estimated in real time/per day M*U distances to be estimated in real time/per day Complexity is an O(MC+MU) Thus: 10^12 estimations of 10ms, thus 10^10s, 317 years !!! Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 35
  36. 36. Why to recommendThe Social Network owners aim to: reduce the number of queries to reduce costs push for the long tail content to increment of revenues stimulate the socialization to have more connections get more users, get more value for advertisingTo create more connected SNs More cohesion leads to have more resistance to close More connections means more solidity and activity Etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 36
  37. 37. Similarity DistanceThe simplest solution for the recommendations/suggestion isto estimate the closest Users or Objects with respect to thereference User/ObjectThe estimation of the closest entity between two entitiesdescribed with multiple symbolic description is an instance ofmultidomain symbolic similarity distance among theirdescriptors.We can suppose for a while to have the possibility ofestimating the similarity distance among descriptors.Some indexing tools, such as Lucene/Solr, may help in doingthis with a query based on information of the referenceuser/object. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 37
  38. 38. Linguistic Processing of TextNeeded on:  documents, comments, forum, web pages, etc.Purpose of Linguistic processing: from simplest to morecomplex:  Extraction of keywords  Summarization of documents  Multilingual translation  keywords, summary, ..  Extraction of positive/negative impressions  Assessment of intentions, understanding  Production of a semantic meaning of the comment Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 38
  39. 39. Project: Open Space Innovative MindsCreazione di un sistema per ricercare competenzeall’interno di reti di ricerca acquisizione della conoscenza del sistema di ricerca Supporto per l’inferenza Supporto per la ricerca semanticaCompenze che provengono da sorgenti multiple: Contratti, convenzioni Descrizioni di corsi Articoli CV di persone Descrizioni di dipartimenti e laboratori Documenti qualificati, come presentazioni e/o slide Descrizioni di progetti di ricerca … Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 39
  40. 40. Architettura: Open Space Innovative Minds Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 40
  41. 41. Overview of Social NetworkSocial NetworksClassification of Social NetworksUser Generated Content, UGCMeasures of Social NetworksRecommendations and complexityComparison e InteroperabilityMobile Medicine A view inside a social networkECLAP Best Practice Network A view inside a social network Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 41
  42. 42. Interoperability among Social NetworksSN may be interoperable with other portals and SNAllowing:  importing user registration/profile and info or directly with some SSO  posting comments and contributions via the so called Social Icon interface  exporting SN content in other portals, for example via some API.  hosting SN players into other WEB portal pages, via some HTML segment to be copied  hosting widgets/applications into the WEB pages of the Social Network, via some programming model Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 42
  43. 43. Interoperability and standardsInteroperability for user profiles migration/interchange AuthenticationLack of standards to cope with these aspectsPossible future standards coming from W3C Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 43
  44. 44. Interoperability for UsersInterchange of user profiles OpenID: user identity standard, to allow user profile and credential interoperability among portals, is SSO method OAuth: delegation protocol for accessing to credentials, user authentication OpenSocial (by Google with MySpace): exchange of user profile. Many big Social Networks have joined the OpenSocial API movement, including hi5, LinkedIn, Netlog, Ning, Plaxo, Orkut, Friendster, Salesforce, Yahoo, Ning, SixApart, XING, etc. Facebook Connect is in competition with OpenSocial Other technologies: XUP of W3C, XMPP, FOAF, XFN, etc.. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 44
  45. 45.  Knowledge Management SECI Model (Nonaka & Takeuchi, 1995) Technological Pedagogical And Content Knowledge (TPACK) (Koehler & Mishra, 2009) Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 45
  46. 46. ADA tacita esplicita Socializzazione Esternalizzazione gruppi, forum, pubblicazione, chat, meeting, produczione,tacita workflow indicizzazione aggregazione, studio, associazioni,esplicita apprendimento, annotazioni, newsletter, Natural Lang. e-learning Processing Interiorizzazione Aggregazione Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 46
  47. 47.  Gestione della conoscenza in un’organizzazione sociale come evoluzione continua:  dimensione epistemologica & ontologica della conoscenza Epistemological Dimension So Scambio di Tacit Knowledge S1 S2 informazioni I0 E0 S3 I1 E1  gruppi, E2 Externalization I2 organizzazioni, Explicit C0 E3 Internalization I3  inter-organiz. Knowledge I4 Socialization C1 Lavoro interno Individual C2 Combination personale Group C3 Organization Ontological Dimension Inter-Organization Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 47
  48. 48. Functionalities and flows of knowledge Conversioni di Start End conoscenza: Kind of Action flow Result of Kind of user user type the action esternalizzazione: T  E Suggestions Platform EE 4 ET All users  to users  combinazione: E  E Searching Individual TE 2 ET Individual  internalizzazione: E  T socializzazione: T  T (TT)  (TE) Flussi  Direct flow (User to Users)  Mediated flow (User to Platform and Platform to users)  Locked flow (User to Platform)  Platform flow (Platform to users) (ET) (EE) Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 48
  49. 49.  Personalizzazione, Profiling  Raccomandazioni, suggerimenti  U U, CU, CC, GU, ….. Socialization Externalization Profilo Utente S+D: high level  Statico: eta’, CV, lingua, sesso, etc. medium level  Dinamico:  Azioni: su persone e contenuti, annotazioni, … low level  Relazioni: fra persone e contenuti, …  Contenuti: letti, interiorizzati, prodotti, etc. Modelli computazionali Analisi NLP Fattori Moltiplicativi Internalization Sistemi Distribuiti, Univ. Firenze, Paolo Combination Nesi 2011-2012 49
  50. 50. YouTube Flickr FaceBook LikedIn MySpace ECLAPUser profile, descriptors Y Y Y Y Y YFriends Y Y Y Y Y YQuery on Users Y Y Y YGroups and Forums Y Y Y Y Y YMultilingual pages Y Y Y Y Y YInvitations of users Y Y Y Y Y YChats, on line, messages Y Y Y Y Y NRecommendation UU N N Y Y Y YRecommendation GU N N N Y NUser Relevance, User,Obj,Group Y(UO) Y(OG) Y(UG) Y(UG) Y(UG) (Y)User Lists, gen rec. of users Y N Y Y Y Y(G)Taxonomy on Users N N N N N YDirect call, SMS, Email Y Y Y Y Y Y(SE)Privacy support, Black List users Y N Y Y Y YEvents N N Y Y Y (Y)E-learning N N N N N Y Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 50
  51. 51. YouTube Flickr FaceBook LikedIn MySpace ECLAPMultimedia, crossmedia UGC Y(M) Y(M) Y(M) N N Y(MC)Audio, Video, Images, Doc V I, V I, D, V I, D I, V A,V,I,DModerated UGC Y N N Y/NQuery on content Y Y N N Y YComments on Content Y Y -- -- Y YRanking and voting Y N -- -- Y YGeneral Recommendation O Y Y Y Y Y YRecommendation OU Y Y -- -- Y (Y)Recommendation OO Y N -- -- N YTaxonomy for content/profile N N N N N YPlay Lists of content Y N N N N YCollection N N N N N (Y)RSS Feeds for content Y Y Y Y Y NLinks with other SN Y Y Y Y Y (Y)Mobile Support Y Y Y Y Y YDRM/CAS Support Y(D) N N N N Y (D)GeoTagging Y Y N N N (Y) Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 51
  52. 52. YouTube Flickr FaceBook LikedIn MySpace ECLAPImporting RegistrationsSingle Sign On, SSO Y Y Y Y Y NImporting contacts from other SNSearching contact, inviting Y Y Y NImporting contacts from local listSearching contact, inviting Y NAPI to provide access content info Y Y NAccepting Social Icons posting Y Y Y Y Y NProducing Links via Social Icons Y Y Y Y Y YExporting Player to be embedded Y Y NAllowing Importing Players into localweb pages N N Y Y Slides Y NAccepting Widget applications Y Y Y Y Y NExporting Widget applications (Y) (Y) N Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 52
  53. 53. Overview of Social NetworkSocial NetworksClassification of Social NetworksUser Generated Content, UGCMeasures of Social NetworksRecommendations and complexityComparison e InteroperabilityMobile Medicine A view inside a social networkECLAP Best Practice Network A view inside a social network Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 53
  54. 54. Mobile MedicineAutomatedBack office -PC, MACos, linux, … -iPhone, iPod, Windows Mobile,Complex content Android(*), …. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 54
  55. 55. Utenti e Servizi: registrazione via email, profilo utente, … ricerche di altri utenti per stabilire relazioni sociali, … upload di contenuti, User Generated Content, UGEsperiences, … conversioni automatiche dei loro contenuti per la distribuzione multicanale, …Aspetti Sociali, Social Network: commenti su contenuti, creazioni di discussioni sui contenuti, etc. gestione Contenuti Preferiti, visione dei contenuti caricati/preferiti da/di amici, … gestione dei propri Amici, Gruppi (ancora non attivo), … Produzione raccomandazioni per trovare altri amici Produzione raccomandazioni per trovare contenuti, … (ancora non attivo), … …… Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 55
  56. 56. Mobile Medicine Content 56 Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 56
  57. 57. Intelligent Cross Media ContentEvolved Business Models:  Educational: Sliding Shows, video, document, audio, images…  Procedures/protocols: (mini applications) Assessing conditions: emergency.. Guidelines, routines/procedures, flows, …  Calculators for several aspects: (mini applications) Dosages and formulas for intensive therapy Estimation of rule for assessing conditions Risk analysis, …e.g.: pulmonary emboli…. Classification of conditions/damages, …  Wizards: active and proactive content Self-unpacking, guiding the user Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 57
  58. 58. Factory and integration Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 58
  59. 59. Overview of Social NetworkSocial NetworksClassification of Social NetworksUser Generated Content, UGCMeasures of Social NetworksRecommendations and complexityComparison e InteroperabilityMobile Medicine A view inside a social networkECLAP Best Practice Network A view inside a social network Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 59
  60. 60. Paolo Nesi (Project coordinator) TEL: +39-055-4796523, 567, FAX: +39-055-4796363, +39-055-4796469 Email: nesi@dsi.unifi.it, www: http://www.disit.dsi.unifi.itUniversity of Florence, Department of Systems and Informatics, DSI DISIT-Lab, Distributed Systems and Internet Technologies Via S. Marta 3, 50139, Firenze, Italy Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 60
  61. 61. Archive partner Archive Original Content/PortalsLibrary partner Archive Library partner partner Library Search/Query partner Content ECLAP Social Agg. Content Performing Service Metadata art Portal Services Institutions, Social Srv. students, Search and browsing metadata lovers Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 61
  62. 62. ECLAPSistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 62
  63. 63. O AXCP back office servicesECLAP Social ECLAP A Metadata I Ingestion and Harvesting Service Portal Networking Ingestion P M Server H Resource Injection IPR Wizard/CAS Content Analysis Archive Metadata Editor Content partner Archive Retrieval Content ProcessingLibrary partner Archive Library partner Content Aggregation and Play partner Library Semantic Computing and Sugg. partner Content Indexing and Search Content Upload Management Metadata Metadata Database + Export semantic database Content Upload Social Network connections E-Learning Support Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 63
  64. 64. A largeset ofcontentitemformats>300A largeset offacetedresults Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 64
  65. 65. database F database ! F Upload via  form PROPOSED  by IPR wizard (axdbv4 F) UNDER Upload via  Upload  ‐IPR rule Rules IPR assessment  IPR completed Rules by Metadata  Status to be  UNDER‐ Editor Automated  enrich  rule or  ENRICH UNDER‐ Enrich Rules user request UPLOA AXCP DED Enrichment Done Enrichment Doneby RULE assessing Rule passing to  validation Rules Validation  non  by Drupal form Done TOBEAPP approved ROVED UNDER‐ by Metadata  final publication Rule Editor VALID ECLAP and/or EDL PUBLISHE D by RULE Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 66
  66. 66. AXCP backoffice Grid Scheduler Grid Node Grid Node Grid Node contributions,•Rule based system •User Profile •Local User Profile•Automated formatting •Dynamic User Profile actions on •Local Dynamic User Profile •Inferential engine •User behavior •Local User behavior content, processing •Use data •Local Use data social actions, •Adaptation •Content •Content preferences, •enrichement •DC+IDs •DC+IDs queries, •AXInfo: ver, prod., •AXInfo: ver, prod, rights,•Multilingual index and use data,.. rights,.. ....search •Descriptors •Descriptors •Text Analysers •Groups: users, content.. •Groups •Indexer •Ontology/Taxonomy Domain •Taxonomy classification •Fuzzy search•Suggestions •Suggestions on the basis of: •Similarity distances • Static and dynamic user •Clustering profile, decriptors, domain Content Organiser •Local Suggestions on the basis of user profiles, local AXCP BackOffice Front End Portal content, local collected data Content Organizer and Players Users Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 67
  67. 67. Gestionepersonale deicontenutiformativi: Ricerca Navigazione Suggerimenti QR, GPS Upload ….cloudVideo of the ContentOrganizer:  http://bpnet.eclap.eu/drupal/?q =en- US/home&axoid=urn:axmedis:00 000:obj:ea5eb861-18b6-4262-8d4b- 77b3d3c084f0 Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 68 2011-2012
  68. 68. Sistemi Distribuiti, Univ. Firenze, Paolo69 Nesi 2011-2012
  69. 69.  http://www.eclap.eu This slide link: http://bpnet.eclap.eu/drupal/?q=en- US/home&axoid=urn:axmedis:00000:obj:6f371ec5-b5d5-46e5-bd5e-a10a3e4228b4 ECLAP workflow and IPR wizard tools can be taken from the ECLAP user manual:  http://bpnet.eclap.eu/drupal/?q=en- US/home&axoid=urn:axmedis:00000:obj:b828710e-b77c-4074-993c- 3efddfbfaad7&section=pagesupport Online manual/help:  http://bpnet.eclap.eu/help/eclap_bpnet_user_manual_v1-0.htm Video of the Content Organizer:  http://bpnet.eclap.eu/drupal/?q=en- US/home&axoid=urn:axmedis:00000:obj:ea5eb861-18b6-4262-8d4b- 77b3d3c084f0 ECLAP infrastructure document  http://bpnet.eclap.eu/drupal/?q=en- US/home&axoid=urn:axmedis:00000:obj:a345a84f-6fdf-4f84-a412- 88094ce363e2 Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 70
  70. 70.  P. Bellini, I. Bruno, D. Cenni, P. Nesi, "Micro grids for scalable media computing and intelligence on distributed scenarious", IEEE Multimedia, in press, IEEE Computer Soc. Press. P. Bellini, I. Bruno, D. Cenni, A. Fuzier, P. Nesi, M. Paolucci, "Mobile Medicine: Semantic Computing Management for Health Care Applications on Desktop and Mobile Devices", in press on Multimedia Tools and Applications, Springer. P. Bellini, I. Bruno, P. Nesi, "EXPLOITING INTELLIGENT CONTENT VIA AXMEDIS/MPEG-21 FOR MODELLING AND DISTRIBUTING NEWS", International Journal of Software Engineering and Knowledge Engineering, World Scientific Publishing Company, in press. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 71
  71. 71. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 72
  72. 72. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 73
  73. 73. Links For Further ResearchFlickr: photo sharing community http://www.flickr.com/YouTube: video sharing community http://www.youtube.com/Myspace. www.myspace.comFacebook. www.facebook.comFriendster. www.friendster.comOrkut. www.orkut.comCrossMediaFinder, XMF. http://xmf.axmedis.org/Mobile Medicine: http://mobmed.axmedis.orgLast.FM: social networking through music interestshttp://www.last.fm/create your own social network http://www.ning.com/MOODLE: open source e-learning system http://moodle.org/ Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 78

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