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Knowledge Engineering
Lecture delivered by
Dr S.Natarajan
Professor , Dept of ISE, PESIT,Bangalore
Session Chair
forNational Conference on Optimization of IT
Oxford College of Engineering, Bangalore
on February 17,2010
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1. Background
What Is Knowledge?
Data
A Record of a
Change of
State
1840KL0617
Information
The flight from Delhi
leaves at 18:40 hours
Dataorganized with
a purpose. Amessage
Knowledge
thats not a good
flight; Often busyand delayed
Literally
what people
know
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Introduction 3
Knowledge engineering
process of eliciting,
structuring,
formalizing,
operationalizing
information and knowledge involved in a knowledge-intensive problem domain,
in order to construct a program that can perform adifficult task adequately
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Ontology
An ontology is a formal representation of a set of concepts within adomain and the relationships between those concepts. It is used toreason about the properties of that domain, and may be used to definethe domain
In theory, an ontology is a "formal, explicit specification of a sharedconceptualisation. An ontology provides a shared vocabulary, which
can be used to model a domain that is, the type of objects and/orconcepts that exist, and their properties and relations
Ontologies are used in artificial intelligence, the Semantic Web,systems engineering, software engineering, biomedical informatics,library science, enterprise bookmarking, and information architecture as
a form of knowledge representation about the world or some part of it. The creation of domain ontologies is also fundamental to the definition
and use of an enterprise architecture framework.
Introduction 4
http://en.wikipedia.org/wiki/Domain_of_discoursehttp://en.wikipedia.org/wiki/Reasoninghttp://en.wikipedia.org/wiki/Artificial_intelligencehttp://en.wikipedia.org/wiki/Semantic_Webhttp://en.wikipedia.org/wiki/Systems_engineeringhttp://en.wikipedia.org/wiki/Software_engineeringhttp://en.wikipedia.org/wiki/Biomedical_informaticshttp://en.wikipedia.org/wiki/Library_sciencehttp://en.wikipedia.org/wiki/Enterprise_bookmarkinghttp://en.wikipedia.org/wiki/Information_architecturehttp://en.wikipedia.org/wiki/Knowledge_representationhttp://en.wikipedia.org/wiki/Enterprise_architecture_frameworkhttp://en.wikipedia.org/wiki/Enterprise_architecture_frameworkhttp://en.wikipedia.org/wiki/Knowledge_representationhttp://en.wikipedia.org/wiki/Information_architecturehttp://en.wikipedia.org/wiki/Enterprise_bookmarkinghttp://en.wikipedia.org/wiki/Library_sciencehttp://en.wikipedia.org/wiki/Biomedical_informaticshttp://en.wikipedia.org/wiki/Software_engineeringhttp://en.wikipedia.org/wiki/Systems_engineeringhttp://en.wikipedia.org/wiki/Semantic_Webhttp://en.wikipedia.org/wiki/Artificial_intelligencehttp://en.wikipedia.org/wiki/Reasoninghttp://en.wikipedia.org/wiki/Domain_of_discourse -
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G.Tecuci, Learning Agents Laboratory
How are agents built
A knowledge engineer attempts to understand how a subject
matter expert reasons and solves problems and then encodesthe acquired expertise into the agent's knowledge base.
The expert analyzes the solutions generated by the agent(and often the knowledge base itself) to identify errors, andthe knowledge engineer corrects the knowledge base.
Knowledge
Engineer
Domain
Expert
Knowledge Base
Inference Engine
Intelligent Agent
Programming
Dialog
Results
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Introduction 6
Problems in knowledgeengineering
complex information and knowledge is difficult toobserve
experts and other sources differ
multiple representations: textbooks
graphical representations
heuristics
Skills
A study carried out in 1989 showed that the main reason whyexpert systems were not being used was an insufficiency ofmethods for development, especially in the construction ofknowledge bases, e.g. the transfer of expertise.
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Importance of properknowledge engineering
Knowledge is valuable and often outlives a particularimplementation knowledge management
Errors in a knowledge-base can cause seriousproblems
Heavy demands on extendibility and maintenance changes over time
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A Short History ofKnowledge Systems
1965 19851975 1995
general-purposesearch engines
(GPS)
first-generationrule-based systems
(MYCIN, XCON)
emergence ofstructured methods
(early KADS)
maturemethodologies
(CommonKADS)
=> from art to discipline =>
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First generation Expert
Systems
shallow knowledge base
single reasoning principle
uniform representation
limited explanation
capabilities
reasoningcontrol
knowledgebase
operateson
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Defining problem to solve and system to be built:requirements specification
Choosing or building an agent building tool:Inference engine and representation formalism
Development of the object ontology
Development of problem solving rules or methods
Main phases of the agent development process
Refinement of the knowledge base
Feedbackloops
among allphases
Understanding the expertise domain
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A knowledge engineer is assigned the job of buildingthe system.
The knowledge engineer becomes familiar with the problem
and the domain.
The knowledge engineer finds an expert on the subjectwho agrees to collaborate in building the system.
Investigated solution
Develop a computer system that incorporates the expertiseof people familiar with spill detection and containment(i.e. a knowledge-based system, expert system or agent).
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By eliciting the expert's conception of his/herexpertise domain we mean determining whichconcepts apply in the domain, what they mean,
what is their relative place in the domain, what arethe differentiating criteria distinguishing thesimilar concepts, and what is the organizationalstructure giving these concepts a coherence forthe expert.
Elicitation of experts conception of a domain
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(based primarily on Gammack, 1987)
Elicitation methodology
1. Concept elicitation: methods(elicit the concepts of the domain i.e. anagreed vocabulary)
2. Structure elicitation: the card-sort method(elicit some structure for the concepts)
3. Structure representation(formally represent that structure in asemantic network)
4. Transformation of the representation(transform the representation to be used forsome desired purpose)
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Introduction 14
Transfer View of KE
Extracting knowledge from a human expert mining the jewels in the experts head
Transferring this knowledge into KS.
expert is asked what rules are applicable translation of natural language into rule format
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Introduction 15
Problems with transfer view
The knowledge providers, the knowledge engineerand the knowledge-system developer should share a common view on the problem solving process and
a common vocabulary
in order to make knowledge transfer a viable way ofknowledge engineering
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Introduction 16
Rapid Prototyping
Positive focuses elicitation and interpretation
motivates the expert
(convinces management)
Negative large gap between verbal data and implementation
architecture constrains the analysis hence: distorted model
difficult to throw away
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Introduction 17
Methodological pyramid
world view
theory
methods
tools
use feedbackcase studies
application projects
CASE tools
implementation environments
life-cycle model, process model,
guidelines, elicitation techniques
graphical/textual notations
worksheets, document structure
model-based knowledge engineering
reuse of knowledge patterns
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Introduction 18
World view: Model-Based KE
The knowledge-engineering space of choices andtools can to some extent be controlled by theintroduction of a number of models
Each model emphasizes certain aspects of thesystem to be built and abstracts from others.
Models provide a decomposition of knowledge-engineering tasks: while building one model, theknowledge engineer can temporarily neglect certainother aspects.
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Introduction 19
CommonKADS principles
Knowledge engineering is not some kind of `miningfrom the expert's head', but consists of constructingdifferent aspect models of human knowledge
The knowledge-level principle: in knowledgemodeling, first concentrate on the conceptualstructure of knowledge, and leave the programmingdetails for later
Knowledge has a stable internal structure that isanalyzable by distinguishing specific knowledgetypes and roles.
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KADS
Introduction 20
Knowledge Acquisition and Documentation Structuring(KADS) is a structured way of developing Knowledge-BasedSystems (KBS)(Expert Systems).
It was developed at the University of Amsterdam as an
alternative to an evolutionary approach and is now accepted asthe European standard for KBS
Its components are:
A methodology for managing knowledge engineering projects.
A knowledge engineering workbench.
A methodology for performing knowledge elicitation.
KADS was further developed into CommonKADS
http://en.wikipedia.org/wiki/Knowledge-based_systemshttp://en.wikipedia.org/wiki/Knowledge-based_systemshttp://en.wikipedia.org/wiki/Expert_systemhttp://en.wikipedia.org/wiki/University_of_Amsterdamhttp://en.wikipedia.org/wiki/University_of_Amsterdamhttp://en.wikipedia.org/wiki/Expert_systemhttp://en.wikipedia.org/wiki/Knowledge-based_systemshttp://en.wikipedia.org/wiki/Knowledge-based_systemshttp://en.wikipedia.org/wiki/Knowledge-based_systemshttp://en.wikipedia.org/wiki/Knowledge-based_systems -
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KADS (contd)
Knowledge Based Systems Analysis and DesignSupport (KADS) originating in the European ESPRITproject P1098
75 men-years of work, was one of the most highlydeveloped KBs (Knowledge Based Systems) in theearly 90s.
Introduction 21
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KADS (contd)
This pioneering method provides two types ofsupport for the production of KBs in an industrialapproach:
firstly, a lifecycle enabling a response to be madeto technical and economic constraints (control of theproduction process, quality assurance of thesystem,...), and
secondly a set of models which structure theproduction of the system, especially the tasks ofanalysis and the transformation of expert knowledgeinto a form exploitable by the machine.
Introduction 22
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Introduction 23
CommonKADS theory
KBS construction entails the construction of anumber of models that together constitute part of theproduct delivered by the project.
Supplies the KBS developer with a set of model
templates. This template structure can be configured, refined
and filled during project work.
The number and level of elaboration of models
depends on the specific project context.
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Introduction 25
Model Set Overview (1)
Organization model supports analysis of an organization,
Goal: discover problems, opportunities and possibleimpacts of KBS development.
Task model describes tasks that are performed or will be performed in
the organizational environment
Agent model describes capabilities, norms, preferences and permissions
of agents (agent = executor of task).
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Introduction 26
Model Set Overview (2)
Knowledge model gives an implementation-independent description of
knowledge involved in a task.
Communication model models the communicative transactions between agents.
Design model describes the structure of the system that needs to be
constructed.
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Introduction 27
Principles of the Model Set
Divide and conquer.
Configuration of an adequate model set for a specificapplication.
Models evolve through well defined states.
The model set supports project management. Model development is driven by project objectives and risk.
Models can be developed in parallel.
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Introduction 28
Models exist in various forms
Model template predefined, fixed structure, can be configured
Model instance objects manipulated during a project.
Model versions versions of a model instance can exist.
Multiple model instances separate instances can be developed
example: ''current'' and ''future'' organization
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Introduction 29
The Product
Instantiated models represent the important aspects of the environment and the
delivered knowledge based system.
Additional documentation information not represented in the filled model templates
(e.g. project management information)
Software
R l i k l d
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Introduction 30
Roles in knowledge-systemdevelopment
knowledge provider
knowledge engineer/analyst
knowledge system developer
knowledge user project manager
knowledge manager
N.B. many-to-many relations between roles and people
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Introduction 31
Knowledge provider/specialist
traditional expert
person with extensive experience in an applicationdomain
can provide also plan for domain familiarization where would you advise a beginner to start?
inter-provider differences are common
need to assure cooperatio
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Introduction 32
Knowledge engineer
specific kind of system analyst
should avoid becoming an "expert"
plays a liaison function between application domain
and system
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Introduction 33
Knowledge-system developer
person that implements a knowledge system on aparticular target platform
needs to have general design/implementationexpertise
needs to understand knowledge analysis but only on the use-level
role is often played by knowledge engineer
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Introduction 34
Knowledge user
Primary users interact with the prospective system
Secondary users are affected indirectly by the system
Level of skill/knowledge is important factor
May need extensive interacting facilities explanation
His/her work is often affected by the system consider attitude / active tole
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Introduction 35
Project manager
responsible for planning, scheduling and monitoringdevelopment work
liaises with client
typically medium-size projects (4-6 people) profits from structured approach
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Introduction 36
Knowledge manager
background role
monitors organizational purpose of system(s) developed in a project
knowledge assets developed/refined
initiates (follow-up) projects
should play key role in reuse
may help in setting up the right project team
Roles in kno ledge s stem
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Introduction 37
Roles in knowledge-systemdevelopment
knowledge
provider/specialist
projectmanager
knowledgesystem developer
knowledgeengineer/
analyst
knowledgemanager
knowledgeuser
KS
manages
managesuses
designs &implements
validates
elicits knowledge
from
elicitsrequirements
from
deliversanalysis models
to
defines knowledge strategyinitiates knowledge development projectsfacilitates knowledge distribution
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Introduction 38
Terminology
Domain some area of interest
banking, food industry, photocopiers, car manufacturing
Task something that needs to be done by an agent
monitor a process; create a plan; analyze deviant behavior
Agent the executor of a task in a domain
typically either a human or some software system
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Introduction 39
Terminology
Application The context provided by the combination of a task and a
domain in which this task is carried out by agents
Application domain The particular area of interest involved in an application
Application task The (top-level) task that needs to be performed in a certain
application
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Introduction 40
Terminology
knowledge system (KS) system that solves a real-life problem using knowledge
about the application domain and the application task
expert system knowledge system that solves a problem which requires a
considerable amount of expertise, when solved by humans.
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Knowledge engineering
GDS
600,000 Travel Agentsuse the GDS to find and book hotels & flightsUsed by IATA approved Travel Agents world wide
40,000 Internet Distribution Systems (IDS) may use GDS *Expedia, TraveloCity etc
* IDS May pull content from GDSgiving a single point of control for multiple channels
This very powerful feature is however being depreciated asIDS opt for direct contracts with hotels
Global Exposure!!
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Knowledge engineering
GDS
Started By American Airlines to let travel agents book flights (1964*)Worlds airlines joined. All system networked togetherExpanded to include hotels (1988 chains via Thisco switch)
The reservation is sent to the hotel. It is immediately available to the travel agent via the GDSand to the hotel via the GDS agent CRS (Generares and partner system (arcRes)
Travel agents search for hotels using asecure computer terminal connected toone of the 4 GDS channels- Amadeus Galileo Sabre Worldspan
Each GDS displays your hotelscurrent rates and availability
300 million reservation per month
1964* SABRE: Semi-Automated Business Resrch Environment.The larges non government database in the world
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Knowledge engineering
GDS information is also available to display and book
on 1,000s of travel sites like Travelocity
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Knowledge engineering
GDS NEWORK
GDS-CRS RepEg: Reserv, Unirez, Utell (Pegasus)Synix.. etc and Genares
600,000 Travel Agents use the GDSto find and book hotels & flights
40,000 Internet Distribution Systems
(IDS) may use GDS *
Interfaced to your back officemanagement/accounting andfront office reservation and web
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Knowledge engineering
GDS BENEFITS
A Global Bookings Channel
Global Marketing exposing you to the global travel market
Administration
manages rates on multiple channels- integrates with backoffice, front office
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.
Knowledge engineering
GENARES GDS-CRS
Combining over 35 years of experiencein the hospitality reservation technologyindustry, GENARES has developed the
first truly integrated third partycentral reservation system for
the twenty-first century
Complete open specificationand interfaces
for 3rd party integration,using XML format
The youngest and fasting growingGDS integration company
Highly recommended by existing clients
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.
Knowledge engineering
GENARES RECOMENDATIONS
Hi Ian,
GenaRes is one of the best GDS providers Ive worked with within my 27 year in the industry!
In over two years now, our volume of business percentage has grown through bookings via the GDS.
I have been assigned my own support account manager and we have over the years established such
a great working relationship. She is always here for me and also when Im out of the office she is thecontact person regarding any issues with loading rates, opening/closing the system etc. Although theproperty can control basically everything own their own through the easy GenaRes system; my accountmanager has always proven to be our second hand person.
This is what I call a great support team!!
Thanks Ian and Ill be on my way to Barbados to pick up that Rum Punch!!
Call on me anytime and be well.
Best Regards,
Clayton C.ChanningReservations/Revenue Manager- flatotel.com646-756-7952
And many more
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.
Knowledge engineering
Genares - full featuredCRS
Reporting
Agents Commission Tracking
IntegrationRoomMaster, IQ Ware, RSI, AutoClerk,* Opera,* Check Inn,PMS Solutions/Innkeeper,Expedia Quick Connect, ezyield
& arcRes
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Knowledge engineering
Integrated with AXSES ArcRes
for fast, easy setup& management
RoomMaster, IQ Ware, RSI, Check InnExpedia Quick ConnectAutoClerk*, Opera,*PMS Solutions/Innkeeper*
* In development
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.
.
Knowledge engineering
GDS and arcRes easy to use & powerful
Just a click away
Easy navigation on your arcRes home page
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.
.
Knowledge engineering
arcRes GDS Setup and Load easy as 123
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.
.
Knowledge engineering
arcRes - Register GDS with a Click
Registering switch and contractonline for easy access and process
Automatically generate letter and contracts with a click no paperwork!
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.
.
Knowledge engineering
Load GDS rates and Content using arcRes info
We dont know of a GDSthat is as easy and ascost effective to setup
Takes all info in arcResand formats it for GDS
Allows you to save an editfor as long as you want
Saves time and improves content
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Knowledge engineering
Generes GDS Marketing Key Partners
40,000 travel Sites (IDS)May pick up GDS content
Participation in worldwidetrade shows
NBTAHITEC
HEDNAAll (GDS) conferencesWTM (World Travel Mart),attendee only
ITB, attendee onlyResExpo
RFP consortia participation
Private GDS chain levelbranding programs
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Knowledge engineering
GDS as Channel Manager
IDS pull from GDS (sometimes exclusively *)Some have Direct (net rates) contactsMany do both
IDS Net Rates Contract 25 - 35%Barbados Hotels pay for placement(loss of contract = loss of position)
Marketing Constraints
Being on GDS may not eliminateneed to manage direct contract(net rates) with companies like Expedia
Genares offer IDS Direct like Expedia Quick connect
40,000 travel Sites (IDS)May pick up GDS content
* priceline
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.
Knowledge engineering
AFORDABLE GDS .. 5% GDS rep fee
GDS Setup $150- arcRes time saver load $ 50-Pass through $ 5.75-GDS Rep 5%- minimum monthly maintenance fee is $25.00- ODD monthly connection fee is $25.00
WebsitearcRes Bookings (i) $ 0Dynamic rates arcRes $ 75arcRes channel Management option $ 2.00
Agent Commission (TACS)per reservation to GDS $ 0.45per reservation to Perot Systems $ 0.55
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.
Knowledge engineering
AFORDABLE GDS .. Just Got Better!!
Combined with arcRes for
- one of a kind marketing
- full service
- easy GDS setup
- savings
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.
Knowledge engineering
arcRes Interactive services Increase bookings by 25%
ThemesSpecials channel
Pre-made packagesGroups managementDynamic rates & accom.CMSAffiliate marketing
Reservations Booking engine
BookingsBarbados channel
Search engine
Comparison Shopping
Dynamic packages
* Bookable Advertising!
GDS Global Distribution
750 3000 / 1500 3750
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.
Knowledge engineering
Dynamic Bookable Advertising
rates
quotes
Full shopping cart cost and compare**** huge increase in bookability***
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.
Knowledge engineering
Dynamic Bookable Advertising
quotes
Cost Compare of all point select.
Quotes, invoice, save, book
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Knowledge engineering
Dynamic Bookable Advertising
Linked to website if advertising
- no rates
- no quotes
- no shopping
- no consistent information
NOT Bookable xx
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.
Knowledge engineering
Dynamic Bookable Advertising everywhere!!
Soon. A bookable map for every arcres advertisier.
>>>>>>
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Knowledge engineering
Advertise - Search Shop Buy . Blur >> The Perfect Storm
Each element in the search-shop-buy triumvirate is undergoing a periodof intense innovation, making each increasingly significant, yetinterdependent. In fact, searching, shopping and buying once distinctterms describing different behaviorsare blurring at a furious pace. -Philip C. Wolf, President and CEO, PhoCusWright Inc.
AXSES is there. We have already integratedadvertising with all phases of the shoppingcycle. This gives you complete flexibility inrevenue and marketing models; including anymix of transaction, commission and
subscription.
Focus direct sales facilitating distribution
Interactive advertising works! Travelers stay longer and use all options
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Saves $1000
Applies full GDS costs $300Full RH Cost $500Plus 50% on remainder
Knowledge engineering
arcRes GDS PACKAGES - combinations
Saves $450
Applies full GDS costs $300Plus 50% on remainder
Non hosted - clients pay additional $250 setup and $250 pa 50% $250
Search, comparison shoppingquotes, reservations, bookingsDynamic packaging, Bookable-ads
Search, comparison shopping,quotes, reservations, bookingsBookable-ads
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