21 21 Web Content Management Architectures Vagan Terziyan MIT Department, University of Jyvaskyla, AI Department, Kharkov National University of Radioelectronics

Contents §Introduction §Features Used for Architectures Classification §Restrictions Applied to the Architectures §“Pull” Architectures §“Push” Architectures §“Mirror” Architectures §Transaction Schemes for the Architectures This Presentation:

Introduction §WWW currently contains about 5 billion static documents, which are accessed by about 500 million users; § a user nowadays waits longer online during search of information from submitting search query until getting search results; § the probability that search result contain information, a user is looking for, decreases and thus, to be sure that enough appropriate sites were found, a user should spend online more time trying different search queries; § the amount of returned search results increases and thus the time a user should spend online selecting needed information also increases; § the amount of appropriate sites of an average search in the web increases as well as the time a user should spend online to download and integrate selected information to one document for further use.

Motivation for Semantic Web

Semantic Web Content: New “Users” applications agents

Semantic Web: Resource Integration Shared ontology Web resources / services / DBs / etc. Semantic annotation

What else Can be Annotated for Semantic Web ? Web resources / services / DBs / etc. Shared ontology Web users (profiles, preferences) Web access devices Web agents / applications External world resources Smart machines and devices

Features Used for Architectures’ Classification (1) §I. Availability and location of semantic annotations for users (preferences, profiles, search queries, etc.): l (1) Absent; (2) Server-Sided; (3) Client-Sided; §II. Availability and location of semantic annotations for Web resources (annotations, descriptions, advertisements, etc.): l (1) Absent; (2) Server-Sided; (3) Client-Sided;

Features Used for Architectures’ Classification (2) §III. Availability and location of intelligent Web browsing/search/integration agent (application, service): l (1) Absent; (2) Server-Sided; (3) Client-Sided; §IV. The basic type of the technology used for information retrieval: l (1) Pull; (2) Push; (3) “Mirror*”. * intelligent combination of pull and push technologies

Amount of Information Retrieval Architectures §Possible Amount of Abstract Architectures: 3 × 3 ×3 × 3 = 81; §Amount of Valid/Reasonable/Principally Different Architectures: = 21.

Restrictions of “Pull” Architectures §Assume that if semantic annotations of the resources are available then there should be agent (either in user client site or in external service site) to explore these annotations; §Assume that if resources are not annotated then there might be a need to involve agents to the architecture, which can operate with not-annotated data in the web; §Assume that there is no need to have annotation of user preferences for such architectures.

Restrictions of “Push” Architectures §Assume that if semantic annotations of the users preferences are available then there should be agent (either in the resource client site or in external service site) to explore these annotations; §Assume that if user preferences are not annotated then there might be a need to involve agents to the architecture, which can operate with not-annotated data in the web; §Assume that there is no need to have annotation of the resources for such architectures.

Restrictions of “Mirror” Architectures §Assume availability of a mediation service with a service agent; §Assume availability of either semantic annotation of a user preferences or semantic annotation of a resource content; §Assume that the service is the only initiating point of all transactions (this e.g. means that there are no direct transactions between a user and a resource).

The Full List of Architectures (1) Valid Architectures

The Full List of Architectures (2)

The Full List of Architectures (3)

(1) #1: Simple Search User Web Resource

User finds and accesses external resource 1 User downloads data from resource 1 User finds and accesses external resource 2 User downloads data from resource 2 User integrates data Simple Search Example Diagram

(2) #4: User Agent Based Search User Agent

User Agent Based Search Example Diagram Agent finds and accesses external resource 1 Agent downloads data from resource 1 Agent finds and accesses external resource 2 Agents downloads data from resource 2 User integrates data User submits search query to user agent Agent reports search results to its user

(3) #5: Resource Agent Based Push Resource Agent

Resource Agent Based Push Example Diagram Agent finds User 1 and advertises resource User 1 accesses and downloads data from the resource Agent finds User 2 and advertises resource Resource submits ads query to its agent Agent reports to the resource the list of accessed users User 2 refuses to download data from the resource

(4) #7: Service Agent Based Search Service Agent, e.g. Google

Service Agent Based Search Example Diagram Agent finds and accesses resource 1 Agent downloads summary from resource 1 Agent finds and accesses resource 2 Agents downloads summary from resource 2 User analyses summaries User submits search query to the external service agent Agent reports search results to the user User accesses resource 1 User downloads data from resource 1 User accesses resource 2 User downloads data from resource 2 User integrates data

(5) #8: Service Agent Based Push

Service Agent Based Push Example Diagram Agent sends advertisement mail to User 1 User 1 accesses and downloads data from the resource Agent sends advertisement mail to User 2 Resource submits ads query to the external service agent Agent reports to the resource the list of accessed users User 2 refuses to download data from the resource Agents selects appropriate mailing list

(6) #13: Semantic Search from Client Resource Annotation

Semantic Search from Client Example Diagram Agent finds and accesses annotation of resource 1 Agent downloads data from resource 1 Agent integrates data from resources based on annotations User submits search query to user agent Agent reports integrated search results to its user Resource 1 advertises itself on a server (puts semantic annotation) Resource 2 advertises itself on a server (puts semantic annotation) Agent downloads annotation of resource 1 Agent accesses resource 1 Agent finds and accesses annotation of resource 2 Agent downloads data from resource 2 Agent downloads annotation of resource 2 Agent accesses resource 2 Agent analyzes annotation

(7) #16: Semantic Search from Server Resource Annotation

Semantic Search from Server Example Diagram Agent downloads data from resource 1 Agent integrates data from resources based on annotations User submits search query to external service agent Agent reports integrated search results to the user Resource 1 advertises itself on a server (puts semantic annotation) Resource 2 advertises itself on a server (puts semantic annotation) Agent accesses resource 1 Agent downloads data from resource 2 Agent accesses resource 2 Agent analyzes annotations to find matches with query

(8) #18: Blind Centralized Mirror Resource Annotation

Blind Centralized Mirror Example Diagram Agent downloads data from the resource User 2 refuses to accept ads User 1 accepts ads Agent reports to resource the list of accessed users Resource advertises itself on the server (puts semantic annotation) Agent accesses resource Server’s agent finds user 1 and forwards ads Agent sends data to user 1 Agent finds user 2 and forwards ads External service invites resource to advertise its content User 1 sends feedback to the service User 2 sends feedback to the service

(9) #22: Smart Search from Client Resource Annotation

Smart Search from Client Example Diagram Agent finds and accesses external resource 1 Agent downloads annotation of resource 1 Agent accesses resource 1 Agents downloads data from resource 1 Agent integrates data based on annotations User submits search query to user agent Agent reports integrated search results to its user Agent analyses annotation Agent finds and accesses external resource 2 Agent downloads annotation of resource 2 Agent accesses resource 2 Agents downloads data from resource 2

(10) #25: Smart Search from Server

Smart Search from Server Example Diagram Agent finds and accesses external resource 1 Agent downloads annotation of resource 1 Agent accesses resource 1 Agents downloads data from resource 1 Agent integrates data based on annotations User submits search query to the external service agent Agent reports integrated search results to the user Agent analyses annotation Agent finds and accesses external resource 2 Agent downloads annotation of resource 2 Agent accesses resource 2 Agents downloads data from resource 2

(11) #27: Blind Distributed Mirror

Blind Distributed Mirror Example Diagram Agent downloads data from the resource User 2 refuses to accept ads User 1 accepts ads External service agent finds a resource with semantic annotation Agent accesses resource Agent finds user 1 and forwards annotation Agent sends data to user 1 Agent finds user 2 and forwards annotation Agent downloads resource annotation Agent updates its “Users’ Preferences” database User 1 sends feedback to service User sends feedback to service

(12) #32: Semantic Push from Client User Annotation

Semantic Push from Client Example Diagram Agent reports to the resource the list of accessed users User 1 puts annotated preferences to external server Resource agent accesses server User 1 accesses resource User downloads personalized data from resource Resource agent analyzes preferences to find matches with ads query User 2 puts annotated preferences to external server Agent downloads annotations of preferences Resource submits ads query to its agent Agent sends personalized ads to selected user 1 User 1 analyses and accepts ads

(13) #35: Semantic Push from Server User Annotation

Semantic Push from Server Example Diagram Agent reports to the resource the list of accessed users User 1 puts annotated preferences to external server User 1 accesses resource User downloads personalized data from resource Agent analyzes stored preferences to find matches with ads query User 2 puts annotated preferences to external server Resource submits ads query to external service agent Agent sends personalized ads to selected user 1 User 1 analyses and accepts ads

(14) #36: Simple Mirror User Annotation

Simple Mirror Example Diagram Resource reports “OK, it fits” External service agent sends annotated preferences to the resource with request to check whether it fits the resource content Agent sends personalized data to the user Agent downloads personalized data Agent accesses resource User submits annotated preferences to external server External service invites a user to submit his/her annotated preferences Resource analyzes preferences and decides that it fits well the resource content User sends feedback to the service Service agent updates stored user preferences

(15) #45: Public Commerce User Annotation Resource Annotation

Public Commerce Example Diagram Agent sends personalized data to the user Agent downloads personalized data Service agent accesses resource User submits annotated preferences to the service External service invites a user to submit his/her annotated preferences Service agent finds matches between resource annotations and user’s preferences User sends feedback to the service Service agent updates stored user preferences External service invites resource to advertise its content Resource submits content annotation to the service

(16) #54: Semantic Web Mirror User Annotation Resource Annotation

Semantic Web Mirror Example Diagram Agent sends personalized data to the user Agent downloads personalized data Service agent accesses resource User submits annotated preferences to the service External service invites a user to submit his/her annotated preferences Service agent finds matches between resource annotations and user’s preferences User sends feedback to the service Service agent updates stored user preferences Service agent accesses resource Agent downloads resource content annotation

(17) #59: Smart Push from Client User Annotation

Smart Push from Client Example Diagram Agent reports to the resource the list of accessed users Resource agent accesses user site User accesses the resource User downloads personalized data from the resource Resource agent analyzes preferences to find matches with ads query Agent downloads annotation of user preferences Resource submits ads query to its agent Agent sends personalized resource ads to user User analyses and accepts ads

(18) #62: Smart Push from Server User Annotation

Smart Push from Server Example Diagram Agent reports to the resource the list of accessed users Service agent accesses user site User accesses the resource User downloads personalized data from the resource Resource agent analyzes preferences to find matches with ads query Agent downloads annotation of user preferences Resource submits ads query to the external service agent Agent sends personalized resource ads to the user User analyses and accepts ads

(19) #63: Preference-Based Mirror User Annotation

Preference-Based Mirror Example Diagram Service agent accesses user site User sends feedback to the service Agent analyzes user preferences to generate appropriate search query Agent downloads annotation of user preferences Agent sends personalized resource data to the user Service agent finds and accesses the resource Agent downloads data from the resource Agent analyzes content to accept and filter resource data for the user Agent updates user preferences database

(20) #72: Smart Mirror User Annotation Resource Annotation

Smart Mirror Example Diagram Agent sends personalized data to the user Agent downloads personalized data Service agent accesses resource Resource submits content annotation to service External service invites a resource to submit its content annotation Service agent finds matches between resource annotations and user’s preferences User sends feedback to the service Service agent updates stored resource content annotations Service agent accesses a user site Agent downloads annotation of user preferences

(21) #81: Intelligent Mirror Browsing Resource Annotation User Annotation

Intelligent Mirror Browsing Example Diagram Agent sends personalized and integrated data to the user Agent downloads personalized data Service agent accesses resources Resources submit content annotations to service External service invites resources to submit their content annotations Service agent finds matches between resources annotations and user’s preferences User sends feedback to the service Service agent accesses a user site Agent downloads annotation of user preferences Service agent integrates data from resources based on annotations Service agent adapts matchmaking algorithm

Read more in: Terziyan V., Intelligent “Mirror Web Browsing” vs. Pull/Push Technology, Eastern-European Journal of Enterprise Technologies, V. 1 No. 1, 2003, pp Available online in: