World Wide Value Web: Automated Design of Real-World Multi-party Services on the Web

World Wide Value Web
Automated Design of Real-World Multi-party Services on the Web

Dr. Pieter De Leenheer

Tuesday 25 December 12

The Value Web is there already ...

• Ever wondered what happens when you click these “links” ?

[Right to make tracks public] [MONEY] [Right to make tracks public] [MONEY]

[Track] [MONEY]

[MONEY] [MONEY]
[Right to make tracks public]

[Right to collect fees] [MONEY] [Right to collect fees] [MONEY]

[Right to clear a track] [MONEY] [Right to clear a track] [MONEY] [Right to clear a track] [MONEY]
[Right to clear a track] [MONEY] [Right to clear a track] [MONEY]

Value Value Value AND OR Explosion
Legend Actor interface port Transfer element element element

Market Activity Consumer Connect. Boundary Value
segment need element element object
[...]


The Value Web is there already ...

• Ever wondered what happens when you click these “links” ?


N+1 Tethered Value Webs
• walls slowing down innovation
• no matter what’s being purchased: 1 mediator who takes the cream

http://www.statista.com/


Ex. 1: High-speed Train Station in Greater London


• OR

• purchase a water-proof wall to keep water
out


• OR

out

• purchase a pump to dispense ground water
from the tunnel


• OR

out

from the tunnel

• new value object (i.e., asset): unlimited
water resource

• provided by “pumping service”


• OR

out

from the tunnel

water resource


• value integrator: the London Water
Authority (e.o.) in need of water
resources


• OR

out

from the tunnel

water resource


• value integrator: the London Water
Authority (e.o.) in need of water
resources

• new service value network that turns a problem into
opportunity is win-win for both parties


Ex. 2: Learning Languages with DuoLingo


• OR

• oﬀer certiﬁed online language courses in return
for a subscription fee


• OR


• value objects: fee, certiﬁcate


• OR



• oﬀer certiﬁed language course for free in return
for written assessments via sentence
translations

• new value object: language-to-language
sentence translations


• OR



translations


• through text translating service


• OR



translations



• value integrator: content providers


• OR



translations



• value integrator: content providers

• articulating tacit value objects hidden in existing
service relationships creates new value for both
parties.


More Examples of Successful Networked Value
Propositions Cleverly Combine Web Relations
• http://www.slideshare.net/boardoﬁnnovation/10-business-models-that-rocked-2010-6434921

• talent for the happy few, but how to automate this design process ?


Overview of the Claims



1. Our world is a large-scale non-linear network of rich relationships between technologies, people,
and organisations, emerging from the Web.




2. Web relationships are a catalyst for innovation, i.e., a Value Web, that organisations should harness
to devise new forms of value co-creation. To this end, enterprises must abandon value-chain
thinking.




thinking.

3. One challenge is to articulate the structure and composition of value objects inherent to these
relationships that would lead them to gravitate towards unanticipated value propositions.




thinking.


4. Presuming service-centric thinking, and non-linear patterns of the Web, Service Value Networks
(SVNs) lie at the center of this gravitation; forming the hubs of the Value Web.




thinking.


4. Presuming service-centric thinking, and non-linear patterns of the Web, Service Value Networks
(SVNs) lie at the center of this gravitation; forming the hubs of the Value Web.

5. (Service) Value Web technologies should embody generative principles similar to those that lead to
the success of the Web itself. In other words, Internet-based SVN technologies should allow for
unanticipated contribution of value (through service) to the Web by enabling anyone to share and
trade their value objects, just like previous generations of the Web did for knowledge and social
sharing.




How come ?


Sources: Nova Spivack, John Breslin, Mills Davis, www.opte.org

“By carefully excluding features that are not universally useful Internet
technologies became easily adopted on a massive scale and gave the Web a
generative [i.e. self-reproductive] character” (Zittrain, 2009).



Autonomic
Semantic Agent Agent webs
Artificial Intelligent Natural Ecosystems
Intelligence Agents Language
Intellectual That know,
Property
Smart
Learn & reason
Personal
Assistants
Spime Markets As humans do
Increasing Knowledge Connectivity & Reasoning

Blogjects
Semantic
The Semantic Web The Ubiquitous Web Communities
Connects Knowledge Connects Intelligence Semantic
Ontologies Semantic Enterprise
Semantic Semantic Semantic
Website & UI
Search Blog Wiki Semantic
Knowledge Social networks
Semantic
Bases Semantic
Thesauri & Desktop Context-Aware
Email
Taxonomies Games
Bots

Multi-user
Enterprise Mash-ups Wiki Community
Gaming
Search Engines Portals Portals

Marketplaces
Content Portals
Web Sites RSS & Auctions
Blogs
Social
Bookmarking
The Web PIMS The Social Web
Connects Information Connects People

Desktop Email
Databases Social
“Push” Conferencing Networks
Publish & Subscribe
P2P
File Sharing Instant Messaging
File Servers

Increasing Social Connectivity



Web Science: The Web’s relational
patterns exhibit “long tail”
distributions: “80% of sales goes to
20% of the offerings”



thinking.

Why is this not happening ?


From Chain to Network

value-in-exchange value-in-use

transaction relationships

operand resource (goods) operant resource (knowledge, consumer)

marketing push consumer pull

technology service and content

customer acquisition customer retention

Service = the applications of competences (knowledge and skills) for the beneﬁt of a party

Service = action; not object


The Problem of SOA: Service-oriented Architecture

Norman & Ramirez (1993): “the key strategic task is the reconﬁguration of roles and relationships
among this constellation of actors in order to mobilise the creation of value in new forms and by
new players.”

• believed to be core enabling technology, however no large-scale adoption for our service economy

• a componential approach inspired by product innovation: “bill of materials” and “urban architecture”

new players.”



➡ clever idea but with lack of appreciation of inherent traits of service co-production: variety, intangibility, and
coopetition

new players.”



coopetition

• biased by the enterprise-centric vision, hence electronic business implementations:

• rely on hierarchy of functional components, i.e.: Web services for exchange of data and functionality

• enforce how to execute a certain business operation in a fixed pre-defined manner: time dependency and
control flow

new players.”



coopetition



control ﬂow

➡ completely ignores aspects related to the exchange of value: e.g., strategy, proposition, roles, resourcing,
pricing, quality and regulatory compliance

new players.”



coopetition



control ﬂow

➡ completely ignores aspects related to the exchange of value: e.g., strategy, proposition, roles, resourcing,
pricing, quality and regulatory compliance

• complement SOA with value abstraction level: declare knowledge about what the business domain constitutes in
terms of assets and relationships that allows to reactively adapt its role in changing value propositions.

• Service-dominant logic: ontological analysis of “service” as a perdurant (“action”), rather than an endurant (“object”)....

new players.”

Service Network Approaches: State of the Art
Wiki- (Becker,

decentralised relationship-driven organisation
nomics Value 2009)
• dotted circles: (Tapscott,
2008)
Networks
(Allee,
e3service
(de
2002) Kinderen,
Servigu-
• process-based ration
(Baida,
2009) SNN
(Bitsaki,
2008)

Network-centric:
2006)
• planning problem
e3value
Digital (Gordijn,
METEOR
Capital 2002) -S (2005)

• solid circles:
(Tapscott,
2000)
Service u- Ontomat
Architectu-
• value-based
Service (Agarwal,
res (Booth,
(Lee, 2004)
2004)
REA 2011)
(McCarthy, Dynami-

• design problem 1982) (Razo-
Zapata,
CoS (Da
Silva,
BUSITAL 2011)
2011)

➡ low tendency towards
decentralised and automated (Gordijn et
hierarchical process-driven organisation

al., (Traverso
CPC
HICCS, , 2004)
approaches (Letia,
2008)
2011)

➡ contamination of process-thinking
Enterprise-centric:

in network-centric approaches (Kohl-
born,
(Razo-
Zapata et
al.,
2010) GVP BUSITAL,
➡ lonely at the top? (Zlatev,
2007)
2010)

O-WSP VBC
(Omela- (Nakamu
Value BMO
yenko, ra, 2006 )
Chain (Oster-
2006)
(Porter, walder,
1985) 2004)

ICT support: None Design Analysis Bundling Matching Composition Dynamic
Composition


1. Our world is a large-scale non-linear network of rich relationships between technologies, people, and
organisations, emerging from the Web.



2. Web relationships are a catalyst for innovation, i.e., a Value Web, that organisations should harness to
devise new forms of value co-creation. To this end, enterprises must abandon value-chain thinking.




3. One challenge is to articulate the structure and composition of value objects inherent
to these relationships that would lead them to gravitate towards unanticipated value
propositions.




propositions.

4. Presuming service-centric thinking, and non-linear patterns of the Web, Service Value
Networks (SVNs) lie at the center of this gravitation; forming the hubs of the Value
Web.




propositions.

4. Presuming service-centric thinking, and non-linear patterns of the Web, Service Value
Networks (SVNs) lie at the center of this gravitation; forming the hubs of the Value
Web.

5. (Service) Value Web technologies should embody generative principles similar to those that lead to
the success of the Web itself. In other words, Internet-based SVN technologies should allow for
unanticipated contribution of value (through service) to the Web by enabling anyone to share and
trade their value objects, just like previous generations of the Web did for knowledge and social
sharing.


Service Value Networks

• service co- production, i.e. ”bundling”

• in function of well-articulated needs.

• reflects an acceptable trade-off between

• value proposition (to maximize short-
term profit) and

• market accuracy (to minimize
consumer sacrifice)

• fractal system: the Value Web is a SVN in
which every peer itself can be an SVN

• thus SVN composition becomes a
complex problem


An SVN is a complex system of peers that establish the necessary relationships to collectively
produce (hence co-produce) value (in terms of a real-world service) for their environment (Razo-
Zapata, De Leenheer, & Gordijn, 2011).




term proﬁt) and



complex problem






term proﬁt) and



complex problem teaching “introduction to databases”



ability to normalise a database


term proﬁt) and






ability to normalise a database
certiﬁcate / diploma

term proﬁt) and





SVN Composition Problem: Design vs. Planning
• Composition is a Design- rather than
Planning- problem
4.1. SVN COMPOSITION 63
• “service artifact”: what value is
exchanged rather than how and
when

• value network analysis

• patterns of exchange ?

• causal eﬀect of value within
and on environment?

• value accuracy?

• self-adaptation principles?

Tuesday 25 December 12 Figure 4.1: The SVN Composition Framework.

Articulating needs: from a long & happy life down to
toothpaste
S. de Kinderen et al. / An ontology for needs-driven service bundling in a multi-supplier setting

Problem Information Post-
Evaluation Purchase
recognition search purchase

Fig. 1. The Customer Buying Behavior Model, cf. Kotler (2000)

Most scholars refer to the above characteristics, but use them in different combinations to provide th
• I.S. requirements engineering meets marketing interpretation of what a service is. Some emphasize one specific aspect, such as “services are dee
own theory:
processes or performances” (see Bitner et al. (2008)) while others, most notably Vargo and Lusch (20
conclude that everything is a service.
• separation of structure means-end chaining, quality function
and solution (e.g., For this article, we adopt a business science interpretation of the term service. Of particular importa
deployment, problem frames, i*) reveals differentchallenging,aspects. Services have an attributes:produce valuable outcomes,
for us are the following two
about services is
functions of productproducts. Services therefore automated reason
as opposed to physical
intangible nature,

latter providing us with matchmaking capabilities with customer needs.
• product (toothpaste): attribute (minty) -> consequence (neat image, increase social
2.2. Customer needs
inclusion) -> value (sense of beloning) <= 3need for a happy life
The e service ontology is unique in a way that it considers analysis of customer needs key. To ens
a needs-driven service bundling process, we require an understanding of the steps that a customer usua
• product (toothpaste): attribute(calcium;teeth strengthener) -> consequence (stay purchase a service offeri
takes to arrive from the goals that s/he wants to achieve, to the decision to healthy)
Marketers provide us with several buying behavior theories that help us understand the main steps t
<= need for a long life [note: attribute(minty) not relevant] prominent amongst these is the Customer Buying Behavior (CB
customers use in this process. Most
model, which we find in amongst others Kotler (2000); Solomon (2003); Loudon and Della-Bitta (199
The CBB model consists of the five steps depicted in Fig. 1: (1) problem recognition, in which the c
• .... and semantically encode this in consequence aware of a needbasedbeon a customersearch, in which the customer se
tomer becomes ladders, that is to satisfied (2) information
perspective ontology: out benefits required to satisfy this need (3) evaluation, in which the customer decides upon the prod
that maximizes the desired features and minimizes the negative features (4) product buying, in wh
the customer actually buys the product and finally (5) the post-purchase phase, in which the custom
2
• Kinds of needs: physical good (house),the the stepsinofuse-situations. monetary resources, (1) separation betw
evaluates
Following
product
human resource, (Kotler, 2000, p. 177-178), we discuss
the CBB model
information, capability (course), experience (museum customer seeks products, and (3) how products arecar by b
problems and solutions, (2) how a visit), state change (hair cut, evaluated
ancing positive and negative service features.
wash, a flight)
2.2.1. Separation of problem and solution
The CBB model sees problem recognition and information search (hence finding a solution) as t
separate phases, hence emphasizing the explicit separation between problem specification and probl

Customer Perspective Ontology & Example (1) in the
domain of Assisted Living for Dementia Patients 6 S. de Kinderen et al. / An ontology for needs-driven service bundling in a multi-supplier s

nominal Scale Need

ordinal Has 0...1 Specified by 1…*
Consists of Depends on
interval Has 0..1
Quality 0…*
0…*

need I cannot cope anymore, consequence
what can help? ratio Consequence
Functional
Want consequence 0…* Has 1…* 0…*
Core enhancing Optional bundling
Functional Contained in 0…*
consequence …. Physical activities for Practical support Social support for Social support
person with dementia for person with dementia person with dementia informal carer Want
Scales of 0..* 0…*
quality Core enhancing Optional bundling
consequences …..
….
Fig. 2. The e3 service customer perspective ontology
…..
…..

original social chart. Since the tool conforms exactly to the reasoning steps from e3 ser
….. tion is that we can also validate the usefulness of e3 service through such a demonstrati
involved scenario walkthroughs, where realistic consumer needs - such as a customer
Handyman Loaningservice Diningtable meal-preparationDinnerdeliverywere taken as starting points to show how the tool interacts
Transportation service -
Adjustments to Possibility to - OB carer. For each scenario delivery
Transportation Meal walkthrough, the domain expert then commented to what exten
home
OB -
loan eg. an Meal preparation - OB principles could constitute a useful addition to the existing social chart.
(electrical) Meal preparation
wheelchair
Social contacts
dementia-patient
Adjustment:
Large, eg.
4. The e3 service ontology
Social contacts
Stairlift Duration: informal carer ….
Minor, eg.
Ramps
<= 6 Months This section discusses the concepts and relationships of the e3 service ontology, e
> 6 Months Contact type: In person
Preparation: Hot
running dementia-care case study. Section 5 shows how to reason with the ontology.
Casemanagement Dagsocieteit The e3 service ontology takes two perspectives on services: the customer perspective
Diet Contact type Preparation
Keeping informed C/E -
Recreational activities
Sugar free
supplier perspective (Sect. 4.2). Additionally, there is a pricing ontology (see Sect. 4.
Internet Frozen
about dementia
patient Social contacts Kosher the e3 service ontologyHot been published earlier, in de Kinderen (2010), de Kinderen
In person have
….. Flesh as main course
dementia-patient
Meat as main course
de Kinderen et al. (2009).
We deﬁne the ontology in terms of UML class diagrams. The ontology is also av
Kinderen, de S.; De Leenheer et al. An ontology for needs-driven service bundling inMoreover, the ontology In J. been implemented in RDF (static part) and Jav
speciﬁcation. a multi-supplier setting. has of Applied Ontology, 2013 (to appear)
(see Sect. 6).

Customer Perspective Ontology & Example (1) in the
domain of Assisted Living for Dementia Patients 6 S. de Kinderen et al. / An ontology for needs-driven service bundling in a multi-supplier s

nominal Scale Need

ordinal Has 0...1 Specified by 1…*
Consists of Depends on
interval Has 0..1
Quality 0…*
0…*

need I cannot cope anymore, consequence
what can help? ratio Consequence
Functional
Want consequence 0…* Has 1…* 0…*
Core enhancing Optional bundling
Functional Contained in 0…*
consequence …. Physical activities for Practical support Social support for Social support
person with dementia for person with dementia person with dementia informal carer Want
Scales of 0..* 0…*
quality Core enhancing Optional bundling
consequences …..
….
Fig. 2. The e3 service customer perspective ontology
…..
…..
want (≠ consequence) is a
original social chart. Since the tool conforms exactly to the reasoning steps from e3 ser
…..
set of consequences at
tion is that we can also validate the usefulness of e3 service through such a demonstrati
involved scenario walkthroughs, where realistic consumer needs likes to a customer
least one party - such as
Handyman Loaningservice Diningtable meal-preparationDinnerdeliverywere taken as starting points to show etc.)the tool interacts
Transportation service - offer (NAPCS, how
Adjustments to Possibility to - OB carer. For each scenario delivery
Transportation Meal walkthrough, the domain expert then commented to what exten
home
OB -
loan eg. an Meal preparation - OB principles could constitute a useful addition to the existing social chart.
(electrical) Meal preparation
wheelchair
Social contacts
dementia-patient
Adjustment:
Large, eg.
4. The e3 service ontology
Social contacts
Stairlift Duration: informal carer ….
Minor, eg.
Ramps
<= 6 Months This section discusses the concepts and relationships of the e3 service ontology, e
> 6 Months Contact type: In person
Preparation: Hot
running dementia-care case study. Section 5 shows how to reason with the ontology.
Casemanagement Dagsocieteit The e3 service ontology takes two perspectives on services: the customer perspective
Diet Contact type Preparation
Keeping informed C/E -
Recreational activities
Sugar free
supplier perspective (Sect. 4.2). Additionally, there is a pricing ontology (see Sect. 4.
Internet Frozen
about dementia
patient Social contacts Kosher the e3 service ontologyHot been published earlier, in de Kinderen (2010), de Kinderen
In person have
….. Flesh as main course
dementia-patient
Meat as main course
de Kinderen et al. (2009).
We deﬁne the ontology in terms of UML class diagrams. The ontology is also av
Kinderen, de S.; De Leenheer et al. An ontology for needs-driven service bundling inMoreover, the ontology In J. been implemented in RDF (static part) and Jav
speciﬁcation. a multi-supplier setting. has of Applied Ontology, 2013 (to appear)
(see Sect. 6).

This reasoning step is performed by a human user who is guided by t
customer catalogue in Fig. 4.3 and the relationships defined in the e3 servi
Customer ontology in Fig. 3.1. For instance,& Example customerthe “How can
Perspective Ontology in Fig. 4.3, the (2) in need
improve my programming skills?” can be refined into the FCs: Web Applicati
Educational Domain
Development, Event Driven Programming and Data Analysis and Design, whi
can be refined into more detailed F Cs that better describe a customer ne
in terms of specific requirements [25, 76, 79]. In this case, if the custom
• consequences generatedApplication databases: http:// be refined into three speci
chooses Web from open Developments, it can
www.accreditedqualifications.org.uk
FCs: Designing and developing a web site (F C1 ), website management (F C
and web server scripting (F C3 ), as depicted in Fig. 4.3.

Figure 4.3: Customer catalogue designed with the ontology in Fig. 3.1.

Supplier Perspective Ontology



value activity



value activity

value object



value activity

value object

value interface: reciprocity


suppliers but also between suppliers and enablers.
By making use of the supplier ontology described in Sect. 3.2.1, service sup-
pliers and enablers (actors) can describe their o↵erings in terms of functional
consequences (FCs), i.e. what functionalities they can o↵er to the customers.

Service Value Network for Edu Services
This is an o↵-line inference since the service catalogue must be previously de-
signed. In this way, when the composition starts, the service o↵erings can be
retrieved from a service catalogue.

3.2. SERVICE SUPPLIERS

(in this case an educational service) by means of the generic service
ontology explained in Sect. 3.2.2 and Sect. 3.2.3. We have harvested
licly available database of educational services and selected only on
to exemplify how the supplier ontology is used. The database is ava
http://register.ofqual.gov.uk/, the website of the National Database of
ited Qualifications (NDAQ) containing details of recognized awarding o
tions and regulated qualifications in England, Wales and Northern Irel

Figure 4.2: A service catalogue designed with the ontology in Fig. 3.3.

The Fig. 4.2 depicts a sample of a service catalogue in which both service
suppliers and enablers represent what they can o↵er to customers and service
supplier respectively. The set of service suppliers and enablers is denoted by
SC, and its cardinality is given by S = |SC| .

4.1.1.2 Laddering

Figure 3.2: Example of a service profile.

Tuesday 25 December 12 Fig. 3.2 depicts how The City and Guilds of London Institute (ac

Matching

• generating the candidate 66 Interactive Composition of SVNs
service space

• explosive !

Figure 4.4: Matching F Cs.

MP contains the services A, B, C, D and E since A o↵ers F C1 , B o↵ers F C3 ,
C o↵ers F C1 , D o↵ers F C3 and E o↵ers F C5 .
The M P is actually a subset of SC, i.e. M P ✓ SC, where SC represents all
Defining M P the services stored in the service catalogue as defined in Sect. 4.1.1.1. Once the
Tuesday 25 December 12 M P has been computed, the next step is to find the combinations of services

FCs: Data analysis and data structure design (F C4 ), and Database Software
Use (F C3 ). As can be observed, it would be meaningless to o↵er both services
in the same bundle since they both provide Database Software Use.
Furthermore, if the customer need requires F C1 and F C4 , two incomplete
Bundling solutions must be presented to the customer, i.e. two bundles composed of one
service each: the ﬁrst bundle composed of the service o↵ering the Diploma in
ICT and the second bundle composed of the service o↵ering the Certiﬁcate in
ICT Skills.
• clustering
Service ID F C1 F C2 F C3 F C4
• push heuristic S17 1 1 1 0
S16 1 1 0 1
S15 1 0 1 1
S14 1 0 1 1
S13 1 0 0 1
S12 1 0 0 0
S11 1 0 0 0
S10 0 1 1 1
S9 0 1 0 1
S8 0 1 0 1
S7 0 1 0 0
S6 0 0 1 1
S5 0 0 1 0
S4 0 0 1 0
S3 0 0 0 1
S2 0 0 0 1
S1 0 0 0 1

Table 4.1: Matrix representation for a matching pool (MP). S15 and S14 are
Tuesday 25 December 12 services, that might be provided by di↵erent suppliers, o↵ering exactly the

are assigned to the same cluster if they o↵er exactly the same F Cs. The purpose
of a cluster is to group services o↵ering exactly the same F Cs as well as to focus
earching focus on clusters the searching of possible bundles on the clusters , i.e. explore combinations of
clusters rather than combinations of a huge number of individual services. In
Bundling addition, since a cluster contains one or more services, it can be seen as a set of
partial solutions for a customer need.
We identify two types of clusters, upper and lower clusters. The Table 4.2
depicts the set of upper clusters. As can be observed, because all the upper
• clustering clusters provide F C1 , i.e. they are overlapped in F C1 , they cannot be combined
with each other. The name upper cluster comes from F C1 being the most
signiﬁcant bit (msb) in the vector , i.e. they have the highest values.
• push heuristic
Cluster ID Elements Cluster. Cluster.msb
C14 {S17 } [1110] 8
C {S16 } [1101] 8
4.1. SVN 13
COMPOSITION 69
C11 {S14 , S15 } [1011] 8
C9 {S13 } [1001] 8
Cluster ID {S11 , S12 }
C8 Elements [1000]
Cluster. 8
Cluster.msb
C7 {S10 } [0111] 4
C5 {S8 , S9 } [0101] 4
C4 {S7 } Table 4.2: Upper Clusters.
[0100] 4
C3 {S6 } [0011] 2
C2 {S4 , S5 } [0010] 2
2) Cluster combinationS2 ,Contrary to the[0001] clusters, some of the lower
C1 {S1 , S3 } upper 1
clusters can be combined with each other. Two lower clusters (see Table 4.3) can
be combined if, and only if, their F Cs do not overlap. E.g., since C1 . = [0001]
and C2 . = [0010] do not Table 4.3: Lower C2 can be combined and added to
overlap, C1 and Clusters.
C3 ’s elements (C3 becoming a merging cluster, see Table 4.6 for the ﬁnal result).
with higher msb. Moreover, we apply a bottom-up approach starting can the
We use the C1 C2 expression to denote that the services inside C1 at be
to denote a combination combined with the services and sequentially simple words, it reaching the three
cluster with the lowest msb within C2 . In moving up until means that lower
clustersDecember 12
Tuesday 25 steps are performed. 1) take an element the heuristic take an element from of 2a
cluster with the highest msb. In short, from C1 , 2) is that the elements C ,

Bundling

• clustering
70 Interactive Composition of SVNs
• push heuristic
Clusters to be combined Merging Cluster
C1 C2 ! C3
C2 C4 ! C6
C2 C5 ! C7
C3 C4 ! C7
C4 C8 ! C12
C4 C9 ! C13
C5 C8 ! C13
C6 C8 ! C14
C6 C9 ! C15
C7 C8 ! C15

Table 4.4: Operations.

C. ID Elements Cluster. Cluster.msb
C15 {C6 C9 , C7 C8 } [1111] 8
C14 {S17 , C6 C8 } [1110] 8

C5
2 C8
5 ! C13
7

C6
3 C8
4 ! C14
7
C6
C4 C9
C8 !
! C15
C12

Bundling
C7
4 C8
9 ! C15
13

C5 C8 ! C13
C6 C8 ! C Operations.
Table 4.4:14
C6 C9 ! C15

• clustering C7
C. ID C8
Elements ! C15 Cluster. Cluster.msb
C15 {C6 C9 , C7 C8 } [1111] 8
C14 {S17 , C6 C8 } [1110] 8
C13
Table 4.4: Operations.[1101]
{S16 , C4 C9 , C5 C8 } 8
• push heuristic C12 {C4 C8 } [1100] 8
C11ID
C. Elements
{S14 , S15 } Cluster.
[1011] Cluster.msb
8
C9
C15 {S13 } C9 , C7 C8 }
{C6 [1001]
[1111] 8
8
C8
C14 {S11 , S12 } C8 }
{S17 , C6 [1000]
[1110] 8
8
C13 {S16 , C4 C9 , C5 C8 } [1101] 8
C12 {C4 C8 } [1100] 8
C11 Table 4.5: Upper Clusters - after merging.
{S14 , S15 } [1011] 8
C9 {S13 } [1001] 8
C8
C. ID Elements}
{S11 , S12 [1000]
Cluster. 8
Cluster.msb
C7 {S10 , C2 C5 , C3 C4 } [0111] 4
C6 {C2 C4 } [0110] 4
C5 {S8 , S9 }
Table 4.5: Upper Clusters - after merging.
[0101] 4
C4 {S7 } [0100] 4
C3 ID
C. Elements
{S6 , C1 C2 } Cluster.
[0011] Cluster.msb
2
C2
C7 {S4 , , C} C5 , C3
{S10 S5 2 C4 } [0010]
[0111] 2
4
C1
C6 {S1 , S2CS}}
{C2 , 4 3 [0001]
[0110] 1
4
C5 {S8 , S9 } [0101] 4
C4 {S7 } [0100] 4
C3 {S6 , C1 Table 4.6:
C2 } Lower Clusters - after merging.
[0011] 2
C2 {S4 , S5 } [0010] 2
C1 {S1 , S2 , S3 } [0001] 1
combination of clusters such that their F Cs: 1) do not overlap, and 2) match
all the required consequences. E.g., if we combine C14 with C1 , we can ob-
serve that C14 . does not overlap with C1 . ,- besides C14 C1 provide all the
Table 4.6: Lower Clusters after merging.

4.1. SVN COMPOSITION 71

Bundling bundles provide all the required FCs. E.g. the bundle composed of the services
S4 , S7 and S13 is a complete solution since S4 provides F C3 , S7 provides F C2
and S13 provides F C1 and F C4 .
• clustering
Solution Clusters Solution Bundles
C6 C9 {C2 C4 } C9
• push heuristic C7 C8 {{S10 }, {C3 C4 }, {C2 C5 }} C8
C6 C9 {{S4 , S7 }, {S5 , S7 }} S13
C7 C8 {{S10 }, {S1 , S4 , S7 }, {S1 , S5 , S7 },
{S2 , S4 , S7 }, {S2 , S5 , S7 },
{S3 , S4 , S7 }, {S3 , S5 , S7 },
{S4 , S8 }, {S4 , S9 },
{S5 , S8 }, {S5 , S9 }} {S11 , S12 }
C6 C9 {S4 , S7 , S13 }, {S5 , S7 , S13 }
C7 C8 {S10 , S11 }, {S10 , S12 },
{S1 , S4 , S7 , S11 }, {S1 , S5 , S7 , S11 },
{S2 , S4 , S7 , S11 }, {S2 , S5 , S7 , S11 },
{S3 , S4 , S7 , S11 }, {S3 , S5 , S7 , S11 },
{S4 , S8 , S11 }, {S4 , S8 , S12 },
{S4 , S9 , S11 }, {S4 , S9 , S12 }, {S5 , S8 , S11 },
{S5 , S8 , S12 }, {S5 , S9 , S11 }, {S5 , S9 , S12 }

Table 4.7: The pools of solution clusters and bundles.

Animation of the Bundling
• http://www.youtube.com/watch?v=sUnPs53F-cA


World Wide Value Web: Automated Design of Real-World Multi-party Services on the Web

World Wide Value Web: Automated Design of Real-World Multi-party Services on the Web

Recomendados

Recomendados

Más contenido relacionado

Último

Último (20)

Destacado

Destacado (20)

World Wide Value Web: Automated Design of Real-World Multi-party Services on the Web