overview of social networks

Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 1

Sistemi DistribuitiCorso di Laurea in Ingegneria

Prof. Paolo NesiPart 19 – Overview of social Network

Department of Systems and Informatics University of Florence

Via S. Marta 3, 50139, Firenze, Italytel: +39-055-4796523, fax: +39-055-4796363

Lab: DISIT, Sistemi Distribuiti e Tecnologie [email protected] [email protected]

http://www.disit.dsi.unifi.it/ http://www.dsi.unifi.it/~nesi


Overview of Social Network� Social Networks� Classification of Social Networks� User Generated Content, UGC� Measures of Social Networks� Recommendations and complexity� Comparison e Interoperability� Mobile Medicine

� A view inside a social network� ECLAP Best Practice Network

� A view inside a social network


Introduction� With the users demand in collaborating and sharing

information some Social Networks have been created

� Social 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 techniques

� Other solutions using UGC are Blogs, Wiki, Forum, etc.


Terminology� Social Network

A paradigm of user interaction and behavior on the web� Social Media

A Social network based on media� Social 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.


Classification of Social Networks� Content Based Social Network:

Collect content and show them to users according to their preferences

Content correlation, recommendations Examples: YouTube, Last.fm, Flickr

� User 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, Friendster

� MySpace is a mix of both categories.


Classification of Social Networks� Best Practice Networks

Both content and users Tools for collaborative works: forum, collab. Documents Events and calendar Target is on specific goals

� Enterprise 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.


User Generated Content, UGC� UGC is:

content files, metadata, tags, GPS, etc. ….all

� Conditions 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 confidence

� Pros 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


User Generated Content� Cons and problems

Restricted social penetration since only IT skilled and a certain economical capability may access now

Lack of formal Privacy controlToo many information are requestedSome 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 UGCReuse and annotation of professional content


User Generated Content� Cons and problems

Lack of interoperability for users and content among different social networks:Initially performed to keep connected the usersSecondly 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


User Activities on Social Networks� Wikipedia (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


Social Network Activities meaning� Since the 90% is managed by a small percentage of

active users:Votes are also produced with the same small part of

the communityComments are also produced with the same small

part of the communityPushers 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 whole community


Relevance of Users� Number of Connections with other users

Direct connections, Second and third level connections, Etc.

� Number of accesses to their profile page (if any) posted and/or preferred content Comments groups

� Users’ Activities Number of Posted content Number of posted comments Number of votes, etc. Number of accesses


Stanford Social Web


Main Geographical User/Group Distribution

-Larger groups are represented with bigger dots-Multiple connections are represented with stronger lines


Issues on Communitie Graphs� Presence of a main Center of gravity

Presence of dense groups

� Presences 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 peopleDepending on their activities, there is a risk of losing

those communities is evident

� Number of Connections Distribution of connections


Metriche per le Social Network� Social Network Analysis� Degree of Centrality per un Nodo:

Numero di collegamenti incidenti sul Nodo� Eccentricity of Centrarlity per un Nodo:

La dist. massima fra le distanze minime di tale nodo e ogni altro nodo della rete

� Closeness of Centrality per un certo Nodo: Reciproco della somma delle distanze tra il nodo e tutti gli altri

nodi� Betweenness 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.


Matrix of connections

Aij Carol AndreDiane

Fernando

Beverly Ed Garth

Heather Ike Jane NC

Carol 0 1 1 1 0 0 0 0 0 0 3Andre 1 0 1 1 1 0 0 0 0 0 4Diane 1 1 0 1 1 1 1 0 0 0 6Fernando 1 1 1 0 0 0 1 1 0 0 5Beverly 0 1 1 0 0 1 1 0 0 0 4Ed 0 0 1 0 1 0 1 0 0 0 3Garth 0 0 1 1 1 1 0 1 0 0 5Heather 0 0 0 1 0 0 1 0 1 0 3Ike 0 0 0 0 0 0 0 1 0 1 2Jane 0 0 0 0 0 0 0 0 1 0 1

nc 3 4 6 5 4 3 5 3 2 1 36

A[i][j]: matrix of connectionsCarol

Fernando

GarthIke

Jane

Andre

Beverly

Diane

Ed

Heather


Social Network Analysis Metrics� Degree of Centrality of a node

Number of connections to a certain node can be non symmetric if the

relationships are not symmetric, thus the graph is oriented

Diane has 6 connectionsis 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 !!

Carol

Fernando

GarthIke

Jane

Andre

Beverly

Diane

Ed

Heather


Averaged Number of connections� Total number of connections divided by the number of Nodes

� According to the examples above: NC, Number of connections: 36

they are considered non bidirectional otherwise they should be 18

NN, Number of nodes: 10� Averaged number of connections:

ANC: 36/10, 3.6 connections per node ANC: 18/10, 1.8 connections per node

� It is more similar to the user perception to say 3.6 connections rather then 1.8

Carol

Fernando

GarthIke

Jane

Andre

Beverly

Diane

Ed

Heather


Matrix of distancesD[i][j]: matrix of distances

N*(N-1)/2 elements

Dij Carol Andre DianeFernando

Beverly Ed Garth

Heather Ike Jane

Carol 0 1 1 1 2 2 2 2 3 4 18Andre 0 1 1 1 2 2 2 3 4 16Diane 0 1 1 1 1 2 3 4 13Fernando 0 2 2 1 1 2 3 11Beverly 0 1 1 2 3 4 11Ed 0 1 2 3 4 10Garth 0 1 2 3 6Heather 0 1 2 3Ike 0 1 1Jane 0 0

89

Carol

Fernando

GarthIke

Jane

Andre

Beverly

Diane

Ed

Heather


Averaged shortest path from one person to another

MIT: 6.4 hops

Stanford: 9.2 hops

Our example: 1.97 hops

Sum of shortest paths: 8910 Nodes45 possible connections


Measures of Network Structure

� Cohesion n-dimensions distances, reachability, flow

diffusion of information / communication

� Regions or Groups/Communities components, bi-components, k-cores/means

partitioning of the network into distinct parts

� Subgroups cliques, clans, sets, factions

partitioning of the network into subsets - overlap possiblestrategic alliances, collaborators / competitors


Semantic Descriptors� Modeling descriptors with formalisms:

XML MPEG-7, metamodel for descriptors and descriptors MPEG-21: item descriptor and/or package

� Audio, 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 file

� Documents: Keywords extractions, multilingual agnostic, … Summarization Paragraphs modeling and descriptions


Semantic Computing on SN� Semantics

Different types of DescriptorsUser, content, etc.

� How semantics processing has supported SN� Indexing and querying� Suggestions and recommendations

� Tools for Semantic Computing


The centrality of User profile� User Profile Static information

Name, surname, Nationality Genre, age, languages, etc..other personal info,.. School, work, family, etc. photo, etc.. Economical data

� User Profile Dynamic Information Explicit Preferences in terms of content, friends, votes, ranks,

recommendations, etc.. Actions: play, comments, votes, Frequency of access Etc.


Esempio di Profilo utentiInformazioni dinamiche:

•Lista di oggetti preferiti•Lista di amici•Lista gruppi•Voti positivi ad oggetti•Commenti ad oggetti•…•…•Informazioni sulle preferenze sulla base delle visualizzazioni degli oggetti

•Format•Type•Taxonomy

Informazioni statiche:•Informazioni generali:

•nome, cognome, sesso, • foto, data di nascita, •descrizione personale, •località di provenienza (ISO 3166),

•Nazione•Suddivisione•Provincia

•lingue parlate (ISO 369)•Informazioni di contatto:

•lista di contatti di instant messaging•Scuola e Lavoro:

•scelta del livello scolastico, •nome della scuola,•tipo di lavoro,•nome del posto di lavoro

•Interessi:•Vettore contenente la lista di valori del campo Type degli oggetti scelti dall’utente


Votes/ranks, Comments, preferred � Users may leave on Content and Users:

Comments Ranks and Votes

� Comments may be left as Text or content

� User may mark the preferred content and users (friends) Preferred content are accessible with a direct list to

shortening the time for their play


Recommendations� They 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.….


RecommendationsRecipient of the suggestions

User Content (played by a user)

Group (leader or members)

Sugg

este

d el

emen

ts

Users Proposing to a user possible colleagues / friends

--no sense--Proposing at a group responsible possible interested colleagues to be invited

Contents Proposing to a user possible interesting contents

Proposing at a play of a content similar content items

Proposing at a group members possible interesting content(not much different with respect to C-C combination)

Groups Proposing to a user possible interesting groups

Proposing at a play of a content possible interesting groups in which similar contents are discussed

--no sense--

Ads Proposing to a user possible interesting ads

Proposing at a play of a content the possible interesting ads

Proposing at a/all group member/s possible interesting ads


Different Recommendations

� FOR 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 …


Complexity of Recommendation� Each 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 respect to 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 !!!


Why to recommend� The 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 advertising

� To create more connected SNs More cohesion leads to have more resistance to close More connections means more solidity and activity Etc.


Similarity Distance� The simplest solution for the recommendations/suggestion is

to estimate the closest Users or Objects with respect to the reference User/Object

� The estimation of the closest entity between two entities described with multiple symbolic description is an instance of multidomain symbolic similarity distance among their descriptors.

� We can suppose for a while to have the possibility of estimating the similarity distance among descriptors.

� Some indexing tools, such as Lucene/Solr, may help in doing this with a query based on information of the reference user/object.


Linguistic Processing of Text� Needed on:

documents, comments, forum, web pages, etc.

� Purpose of Linguistic processing: from simplest to more complex: Extraction of keywordsSummarization of documentsMultilingual translation keywords, summary, ..Extraction of positive/negative impressionsAssessment of intentions, understandingProduction of a semantic meaning of the comment


Project: Open Space Innovative Minds� Creazione di un sistema per ricercare competenze

all’interno di reti di ricerca acquisizione della conoscenza del sistema di ricerca Supporto per l’inferenza Supporto per la ricerca semantica

� Compenze 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 …


Architettura: Open Space Innovative Minds


Interoperability among Social Networks

� SN may be interoperable with other portals and SN � Allowing:

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, forexample 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


Interoperability and standards� Interoperability for user profiles

migration/interchangeAuthentication

� Lack of standards to cope with these aspects� Possible future standards coming from W3C


Interoperability for Users� Interchange 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..

Knowledge Management SECI Model (Nonaka & Takeuchi, 1995)

Technological Pedagogical And Content Knowledge (TPACK) (Koehler & Mishra, 2009)



Socializzazione Esternalizzazione

Interiorizzazione Aggregazione

tacita esplicita

tacita

esplicita

gruppi, forum, chat, meeting, workflow

aggregazione, associazioni, annotazioni, Natural Lang. Processing

studio, apprendimento,newsletter, e-learning

pubblicazione, produczione, indicizzazione

ADA

Gestione della conoscenza in un’organizzazione sociale come evoluzione continua: dimensione epistemologica & ontologica della

conoscenza

So

S1

S3E0

E1E2

E3

S2

C0

C1

C2

C3

I0

I1

I2

I3

I4

Tacit Knowledge

ExplicitKnowledge

Externalization

Internalization

Combination

Socialization

EpistemologicalDimension

Ontological Dimension

Individual

Group

Organization

Inter-Organization

Scambio di informazioni gruppi,

organizzazioni, inter-organiz.

Lavoro interno personale


Conversioni di conoscenza: esternalizzazione: T E combinazione: E E internalizzazione: E T socializzazione: T T

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)

Functionalities and flows of knowledgeStart

flowtype

End Kind of

userAction Result of

the actionKind of user

Suggestionsto users

Platform EE 4 ET All users

Searching Individual TE 2 ET Individual

(EE)(ET)

(TE)

(TT)



Internalization

Socialization Externalization

Combination

high level

medium level

low level

Personalizzazione, Profiling Raccomandazioni, suggerimenti U U, CU, CC, GU, …..

Profilo Utente S+D: Statico: eta’, CV, lingua, sesso, etc. Dinamico: Azioni: su persone e contenuti,

annotazioni, … Relazioni: fra persone e contenuti, … Contenuti: letti, interiorizzati,

prodotti, etc. Modelli computazionali Analisi NLP Fattori Moltiplicativi

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 UU N N Y Y Y YRecommendation GU 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


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 OU Y Y -- -- Y (Y)Recommendation OO 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)


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 local web pages N N Y Y Slides Y NAccepting Widget applications Y Y Y Y Y NExporting Widget applications (Y) (Y) N



Mobile MedicineAutomated Back office

Complex content

-PC, MACos, linux, …-iPhone, iPod, Windows Mobile, Android(*), ….


� 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), … ……


Mobile Medicine Content

56


Intelligent Cross Media Content� Evolved 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 therapyEstimation of rule for assessing conditionsRisk analysis, …e.g.: pulmonary emboli…. Classification of conditions/damages, …

Wizards: active and proactive contentSelf-unpacking, guiding the user


Factory and integration

Paolo Nesi (Project coordinator)TEL: +39-055-4796523, 567,

FAX: +39-055-4796363, +39-055-4796469Email: [email protected], www: http://www.disit.dsi.unifi.it

University of Florence, Department of Systems and Informatics, DSIDISIT-Lab, Distributed Systems and Internet Technologies

Via S. Marta 3, 50139, Firenze, Italy 60Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012

Library

ECLAP Social

Service Portal

LibrarypartnerLibrary

partner

ArchivepartnerArchive

partnerArchivepartner

Content

Agg. Content

Metadata

Services

Original Content/Portals

Social Srv.

Search and browsing metadata

Search/Query

Performing art Institutions,

students,

lovers

61Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012

ECLAP


Ingestion and Harvesting

ECLAP MetadataIngestion

Server

OAI

PMH Resource Injection

Content Retrieval

Database + semantic database

LibraryLibrarypartnerLibrary

partner

ArchivepartnerArchive

partnerArchivepartner

ECLAP Social Service Portal

63

IPR Wizard/CAS

AXCP back office services

Content Analysis

Content Indexing and Search

Metadata Editor

Content Aggregation and Play

Content Processing

MetadataExport

Semantic Computing and Sugg.

Content Upload Management

Content Upload

Networking

Social Network connections

Metadata

E-Learning Support

Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012

64

A largeset ofcontentitem formats>300

A largeset offacetedresults

Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012

PROPOSED (axdbv4 F)

Upload via form

Upload via rule

UPLOADED

Upload Rules

UNDER‐IPR

IPR assessment Rules

IPR completed

UNDER‐ENRICH

Status to be enrich rule or user request

Enrichment DoneUNDER‐AXCP

Automated Enrich Rules

Enrichment Done

UNDER‐VALID

Validation Done

passing to validation Rules

TOBEAPPROVED

PUBLISHED

assessing Rule

final publication Rule

non

approved

ECLAP and/or EDL

by IPR wizard

by Metadata Editor

by Metadata Editor

by RULE

by Drupal form

by RULE

database F database ! F


•User Profile•Dynamic User Profile

•User behavior•Use data

•Content •DC+IDs•AXInfo: ver, prod., rights,..•Descriptors

•Groups: users, content..•Ontology/Taxonomy Domain

•Suggestions on the basis of:• Static and dynamic user

profile, decriptors, domain

•Local User Profile•Local Dynamic User Profile

•Local User behavior•Local Use data

•Content •DC+IDs•AXInfo: ver, prod, rights, ....•Descriptors

•Groups•Taxonomy classification

•Local Suggestions on the basis of user profiles, local content, local collected data

contributions, actions on content, social actions,preferences, queries, use data,..

Front End Portal

Content Organizer and Players Users

Grid SchedulerGrid Node

Grid Node

Grid Node

AXCP backoffice

•Rule based system•Automated formatting

•Inferential engine processing•Adaptation•enrichement

•Multilingual index and search

•Text Analysers•Indexer•Fuzzy search

•Suggestions•Similarity distances•Clustering

AXCP BackOffice

Content Organiser


Gestione personale dei contenuti formativi: Ricerca Navigazione Suggerimenti QR, GPS Upload ….cloud

Video of the Content Organizer:

http://bpnet.eclap.eu/drupal/?q=en-US/home&axoid=urn:axmedis:00000:obj:ea5eb861-18b6-4262-8d4b-77b3d3c084f0

68Sistemi Distribuiti, Univ. Firenze, Paolo Nesi

2011-2012

69Sistemi Distribuiti, Univ. Firenze, Paolo

Nesi 2011-2012

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


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.



Links For Further Research� Flickr: photo sharing community http://www.flickr.com/� YouTube: video sharing community http://www.youtube.com/� Myspace. www.myspace.com� Facebook. www.facebook.com� Friendster. www.friendster.com� Orkut. www.orkut.com� CrossMediaFinder, XMF. http://xmf.axmedis.org/� Mobile Medicine: http://mobmed.axmedis.org� Last.FM: social networking through music interests

http://www.last.fm/� create your own social network http://www.ning.com/� MOODLE: open source e-learning system http://moodle.org/

overview of social networks

Technology

social network analysis

social networking principles

content files

terminologysocial network

otherssistemi distribuiti

direct upload of content

search content target

user generated contentcons