Web Service Discovery and Selection: Pragmatic Approaches Natallia Kokash PhD student, XX cycle...

Web Service Discovery and Selection:Pragmatic Approaches

Natallia KokashPhD student, XX cycle

Tutor: Vincenzo D’AndreaAdviser: Marco Aiello

20/10/2006 University of Lecce 2

DSSOC people

□ Marco Aiello <[email protected]>□ Fabio Casati <[email protected]>□ Vincenzo D’Andrea <[email protected]> □ Maurizio Marchese <[email protected]>

□ Ganna Frankova <[email protected]> – 3d year□ Natallia Kokash [email protected] – 3d year□ GR <[email protected]> – 3d year□ Alexander Ivanyukovich

<[email protected]> – 4th year□ Alexander Lazovik <[email protected]> – 5th year


Scope

□Web Service Composition□Distributed Systems□Web Service Discovery□Quality of Service (Security)□Intellectual Property and Licensing


Introduction

□Discovery of web services□Structural interface matching□Hybrid methods□Behavioral interface matching

□QoS Issues□Web service selection algorithms□Risk evaluation□Recommendation systems as a tool for

web service selection


Web Services

□ A Web service is □ a software system □ identified by a URI, □ whose public interfaces and bindings are defined and

described using XML. □ Its definition can be discovered by other software

systems.□ These systems may then interact with the Web service

in a manner prescribed by its definition, using XML based messages conveyed by internet protocols.

[Web Services Architecture, W3C Working Draft 14 November 2002, from http://www.w3.org/TR/ws-arch/ on 5th March 2002]


SOA and Web Services

Service Registry

(UDDI, ebXML)

Service Requestor

Service Provider

Publ

ish Bind

Find

Service Interface

Descriptions

Service Behavior

Descriptions

Service Quality

Service Service-oriented application

Service Description


Web Services Discovery

□Matching – meeting the functionality required by a user with specifications of existing services□Generic (heuristics, domain-independent

ontologies)□Personal (preferences, specific functions and

patterns for comparing requests and existing services)

□Community (domain-specific ontologies)□Selection – choosing a service with the

best quality among those able to satisfy the user’s goal


Thesis objectives

□It is difficult for a user to write a correct request.

□Automated semantic matching is not feasible.

□Users are in different conditions. □QoS characteristics are constantly

changing.□Collective user experience can be used to

improve service selection. □Domain specific quality factors should be

involved in service selection.


Service Description

□Web Service Description Language (WSDL)□Identity – unique identity of the interface□Input/Output– the meaning of input and output

parameters□Faults – specify the abstract message format

for any error messages that may be output as the result of the operation

□Types – declare data types used in the interface (XML Schema)

□Documentation – natural language service description and usage guide


Semantic Web Services (1)

□ Managing End-To-End OpeRations (METEOR-S)

□ Semantic Web Services Framework (SWSF) □ Web Service Modelling Ontology (WSMO)□ Ontology Web Language for Services (OWL-S)

□ Preconditions – a set of semantic statements that are required to be true before an operation can be successfully invoked

□ Effects – a set of semantic statements that must be true after an operation completes execution after being invoked.

□ Restrictions – a set of assumptions about the environment that must be true

□ Quality of Service – non-functional parameters such as response time, execution cost, capacity, etc.


OWL-S

ServiceResource

Service Grounding

ServiceModel

Service Profile

What does the service do?

How does it work?How is it accessed?

presents

describedBy

supports

provides

□ Does not provide context identification□ Does not describe objects used by the service but not provided by the

client□ Does not describe what service does


Semantic Web Services (2)

□WSDL-S□The semantics of service’s operations

are directly added to WSDL files□Easy to deploy and use□Does not support full features of OWL-S

process ontology


Motivating exampleGoogleSearch<message name=”doGoogleSearchResponse”> <part name=”return” type=”GoogleSearchResult”/></message>...<complexType name=”GoogleSearchResult”><all> <element name=”searchComments” type=”string”/> <element name=”estimatedTotalResultsCount” type=”int”/> <element name=”resultElements” type=”ResultElementArray”/>...< /all>< /complexType>

WolframSearch<message name=”WolframSearchResponse”> <part element=”WolframSearchReturn”/ ></message>...<element name=”WolframSearchReturn”><complexType><sequence> <element name=”Result” type=”WolframSearchResult”/ >< /sequence>< /complexType>< /element>...<complexType name=”WolframSearchResult”><sequence> <element name=”TotalMatches” type=”int”/ > <element name=”Comment” type=”string”/ > <element name=”Matches” type=”WolframSearchMatchArray”/ >...< /sequence>< /complexType>


WS Matching Algorithm

□Requirements:□Matched advertisements are returned in

sorted order, according to their degree of match

□For each element a matching confidence is known (easy to see where problems may occur)

□Tries to catch semantics


Web Service Interface Matching

Name,Description,

Input,Output

….

Name,Description,

Input,Output

…

xy

abc

Operation Operation

Similarity ?

Name,Description,Operations

….

Name,Description,Operations

…

XY

AB

Web Service Web Service

Similarity ?

Z


WS Matching Algorithm

□ Tokenization□ sequences of more than one uppercase letters□ sequences of an uppercase letter and following lowercase letters□ sequences between two non-word symbolsExample: ”tns:GetDNSInfoByWebAddressResponse” {tns, get, dns, info,

by, web, address, response}.□ Word stemming□ Stopwords removing

Registry Parsing Tagging Indexing

Matching

VSM Semantic Other

Evaluation Hybrid

Query

Ontology Meta-data

□http://dit.unitn.it/~kokash/sources


Structural Matching

wij

Requestedoperation

Provided operation

Maximum weight bipartite matching Kuhn’s Hungarian method (polynomial time)

Define overall similarity scoreQuery type: similarity or inclusion

NameOperationsDescription

ServiceName

Input messageOutput message

Description

OperationNameParts

Description

MessageNameType

Description

PartName

ElementsDescription

TypeName

Description

Element


Lexical MatchingMetric

Semantic

VSM+WordNet

Vector-Space Model (VSM)

tf-idf

• semantic matching of word pairs • semantic matching of sentences

Seco, N., Veale, T., Hayes, J.: “An intrinsic information content metric for semantic similarity in WordNet”, ECAI, 2004, pp. 1089-1090


Experimental Results (Test 1)

10 % 40%

15% 45%VSM Semantic

•40 web services •5 groups


Experimental Results (Test1)

Average precision Processing time


Experimental Results (Test2)•371 web services •68 groups


Related Work1. [Sajjanhar’04] Sajjanhar, A., Hou, J., Zhang, Y.: ”Algorithm for Web Services

Matching”, APWeb, 2004, pp. 665–670.2. [Bruno ’05] Bruno, M., Canfora, G. et al.: ”An Approach to support Web Service

Classification and Annotation”, IEEE International Conference on e-Technology, e-Commerce and e-Service, 2005.

3. [Corella’06] Corella, M.A., Castells, P.: “Semi-automatic semantic-based web service classification”, International Conference on Knowledge-Based Intelligent Information and Engineering Systems, 2006.

4. [Dong’04] Dong, X.L. et al.: ”Similarity Search for Web Services”, VLDB, 2004.5. [Platzer’05] Platzer, C.; Dustdar, S.: “A vector space search engine for Web

services”, Proceedings of IEEE European Conference on Web services (ECOWS), 2005.

6. [Stroulia’05] Stroulia, E., Wang, Y.: ”Structural and Semantic Matching for Accessing Web Service Similarity”, International Journal of Cooperative Information Systems, Vol. 14, No. 4, 2005, pp. 407-437.

7. [Wu’05] Wu, J., Wu, Z.: ”Similarity-based Web Service Matchmaking”, IEEE International Conference on Services Computing, 2005, pp. 287-294.

8. [Zhuang’05] Zhuang, Z., Mitra, Pr., Jaiswal, A.: ”Corpus-based Web Services Matchmaking”, AAAI, 2005.

9. [Verma’05] Verma, K., Sivashanmugam, K., et al.: “Meteors wsdi: A scalable p2p infrastructure of registries for semantic publication and discovery of web services.” Journal of Information Technology and Management. Special Issue on Universal Global Integration, Vol. 6, No.1, 2005, pp. 17-39.


Hybrid algorithms

Hybridization

AlgorithmsData

AugmentationMixed

Cascade

Switching

Combination


Hybrid algorithms: Experimental results


Future work

□Hybrid algorithms□Rocha, C. et al.: “A Hybrid Approach for

Searching in the Semantic Web”, International World Wide Web Conference, 2004, pp. 374-383)

□Castells, P., Fernandez, M., Vallet, D.: “An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval”, IEEE Transactions on Knowledge and Data Engineering, 2007, to appear.

□Empirical evaluation of different algorithms using a similar collection of web services


Related work

□ Syeda-Mahmood, T., Shah, G., et al.: “Searching service repositories by combining semantic and ontological matching”, International Conference on Web Services, 2005, pp. 13-20.

“(1) The domain-independent relationships are derived using an English thesaurus… (2) The domain-specific ontological similarity is derived by inferencing the semantic annotations associated with web service descriptions.

…better relevancy results can be obtained for service matches from a large repository, than could be obtained using any one cue alone.”

□ Klusch, M. Fries, B., Sycara, K.: “Automated Semantic Web Service Discovery with OWLS-MX”, AAMAS, 2006.

“…under certain constraints logic based only approaches to OWLS service I/O matching can be significantly outperformed by hybrid ones.”


Composition Patterns

□Sequence □Loop □AND split followed by AND join. □AND split followed by a m-out-of-n

join □XOR split followed by a XOR join□OR split followed by OR join□OR split followed by a m-out-of-n join


NotationSequential operator s1 ; s2

Parallel operator s1 | s2

Choice operator s1 + s2

Web service si

Start state t0

End state t

Quality parameters q(si)

s1

+

|

+n

|n


Behavioral Interface matching

• How to obtain a (composite) service, if there is no direct match for a request in current service registry?

□Behavioral interface - interfaces that capture ordering constraints between interactions.

□BPEL4WS – Business Process Execution Language for Web services


Interface transformationA BFlow

A

BGather C

AB

ScatterC

A BiBurst

Ai BCollapse

A Hide

Source interface

Targetinterface


References

□ Robert J. Hall and Andrea Zisman, “Behavioral Models as Service Descriptions”, ICSOC, 2004.

□ Dumas, M., Spork, M., Wang, K. : “Algebra and Visual Notation for Service Interface Adaptation”, 4th International Conference on Business Process Management (BPM), 2006.

□ Benatallah, B., Hacid, M-S., Leger, A., Rey, K., Toumani, F.: ”On automating Web services discovery”, VLDB Journal, N 14, 2005, pp. 84–96.

□ Lang, Q.A., Su, St. Y.W : "AND/OR Graph and Search Algorithm for Discovering Composite Web Services", International Journal of Web Services Research, 2(4), 46-64, 2005.


Web Services Discovery

□Matching – meeting the functionality required by a user with specifications of existing services□Generic (heuristics, domain-independent

ontologies)□Personal (preferences, specific comparison

functions) □Community (domain-specific ontologies)

□Selection – choosing a service with the best quality among those able to satisfy the user’s goal


QoS Issues

□ How to define QoS? □ complexity of run-time QoS information □ dependencies among different QoS parameters

□ How to specify user preferences? □ How to match user requirements with existing services in

terms of QoS?□ How to perform ranking of similar services w.r.t. to user

preferences?□ How to predict QoS factors under certain environmental

conditions. □ dependencies among different QoS parameters □ relations with contextual factors

□ The same questions for composite web services


QoS characteristics

□Multidimensionality: □Different QoS driven web service

selection algorithms

□Subjectivity: □dependence on context, consumer, etc.□QoS run-time monitoring and analysis

on user side is required.


QoS parametersThroughput The number of requests served in a given time

period.

Capacity A limit of concurrent requests for guaranteed performance.

Latency The round-trip time between client request and service response.

Response time (duration)

The time taken by a service to process its sequence of activities.

Availability The probability that a service is available.

Reliability Stability of a service functionality, i.e., ability of a service to perform its functions under stated conditions

Reputation The average rate of the service reported by clients.

Execution cost (price)

The amount of money for a single service execution.


Linear programming approach[Zeng et al. 2004]

Scaling

Weighting

Linear combination of:□ price□ duration□ reputation□ success rate□ availability

where Wj are user preferences


Some questions1. Scaling:

• Availability - 100%• 0 – 0• 100% – 1

• Response time:• 0 – 1• timeout - 0

2. Objective function: • Linear combination - ?• Can we rely on the preferences defined by a user?

3. Which service is better: • Cheap but not reliable,• Reliable but expensive?

4. A service failed but the task should be accomplished• Structure of a redundant composition graph


New WS selection algorithm□ Notation:

□ c – composition□ q(si) – quality parameter (response time, execution cost)□ p(si) – probability of success□ qmax – resource limit

□ Time vs. cost:□ The basic approach is to take the less important

parameter as objective function provided that the most important criterion meets some requirements.

where


Experimental results


Example□ Goal: Translate a document from Belarusian to

Turkish□ Available web services:

□ Belarusian – English (b-e)□ Belarusian – German (b-g)□ German – Turkish (g-t)□ English – Turkish (e-t)□ German – English (g-e)

□ WS compositions that can satisfy the user’s goal:□ Belarussian – English – Turkish □ Belarussian – German – Turkish □ Belarussian – German – English – Turkish


Example

b-g

b-e

+

+g-t

e-t

+g-e

+

b-g

b-e

+

+g-t

e-t

+g-e

+


Risk management

□ Requires assessment of inherently uncertain events and circumstances

□ Two dimensions: □ how likely the uncertainty is to occur (probability)□ what the effect would be if it happened (impact)

□ Example:□ Movie: title=Rainmaker, format=DVD,

languages=Italian, English□ Convert DVD to AVI: language=English□ SimpleDivX converter: time=2 hours, language = Italian□ Impact on time: 2 hours are lost


Failure riskFailure risk – considers the probability that some fault will occur and

the resulting impact of this fault on the composite service

where is the probability of the service failure.

Loss function – defines the cost of service failure (money, time, resources)


Scenario

s2

s1

++

s3

s5

+s4+

End-user

Provider

Partners

s0

□ Service failures□ Service changes□ Violations of Service

Level Agreements (SLAs)□ Absence of alternative

solutions (penalties)

Invoke


Failure risk: examplep=0.5 cost = 1 penalty = 2

1.75

1.375

1.5625

1.625

1.25

s1 s2

s1 s2

s3

+ +s4

s2s1

s3

+ +

s1

s3

+ +s2

s4

++

s1s2

s3

++


Related Work1. [Zeng 2004] Zeng, L., Benatallah, B., et al.: ”QoS-aware Middleware for Web

Services Composition”, IEEE Transactions on Software Engineering, Vol. 30, No. 5, 2004, pp. 311–327.

2. [Ardagna 2005] Ardagna, D., Pernici, B.: ”Global and Local QoS Constraints Guarantee in Web Service Selection,” IEEE International Conference on Web Services, 2005, pp. 805–806.

3. [Yu 2005] Yu, T., Lin, K.J.: ”Service Selection Algorithms for Composing Complex Services with Multiple QoS Constraints”, International Conference on Service-Oriented Computing, 2005, pp. 130–143.

4. [Claro 2005] Claro, D., Albers, P., Hao, J-K.: “Selecting Web Services for Optimal Composition”, Proceedings of the ICWS 2005 Second International Workshop on Semantic and Dynamic Web Processes, 2005, pp. 32-45.

5. [Canfora 2006] Canfora, G., di Penta, M., Esposito, R., Villani, M.-L.: “QoS-Aware Replanning of Composite Web Services”, Proceedings of the International Conference on Web Services, 2005.

6. [Martin-Diaz 2005] Martin-Diaz, O., Ruize-Cortes, A., Duran, A., Muller, C.: ”An Approach to Temporal-Aware Procurement of Web Services”, International Conference on Service-Oriented Computing, 2005, pp. 170–184.

7. [Bonatti 2005] Bonatti, P.A., Paola Festa, P.,: “On Optimal Service Selection”, Proceedings of the 14th international conference on World Wide Web , 2005, pp. 530-538.

8. [Lin 2005] Lin, M., Xie, J., Guo, H., Wang, H.: “Solving QoS-driven Web Service Dynamic Composition as Fuzzy Constraint Satisfaction, IEEE International Conference on e-Technology, e-Commerce and e-Service, 2005, pp. 9-14.

9. [Gao 2006] Gao, A., Yang, D., Tang, Sh., Zhang, M.: “QoS-driven Web Service Composition with Inter Service Conflicts”, APWeb: 8th Asia-Pacific Web Conference, 2006, pp. 121 – 132.


Quality of Service Issues

□Multidimensionality: □QoS driven WS selection algorithms

□Subjectivity: □dependence on context, consumer, etc.□QoS run-time monitoring and analysis

on user side is required.


How to define QoS parameters?

□ Advertised by providers:□ Simple (popular)□ Providers may not advertise QoS information □ Providers are not able to predict QoS in a neutral manner □ Providers are interested in overstating the real QoS □ Providers do not intend to revise constantly advertised QoS□ Not effective and trust-aware.

□ Monitored on the client side: □ active monitoring and/or explicit user feedback (ratings) □ high computational overheads

□ Evaluated by a third party: □ specialized unbiased agency □ tests web services and publishes QoS data□ expensive and static

□ Hybrid


QoS Sources

ProviderClient

AgencyMonitoring Feedback

Reliable - + - +

Objective - + - +

Up-to-date - + + -

Free of charge + + + -Low computational overheads

+ - - +


Recommendation Systems (RS)

□Examples□Movies (MovieLens), □Music (JUKEBOX), □Books (Amazon), □Hotels, resorts and vacations

(TripAdvisor)□Types:

□Content-based Filtering□Collaborative Filtering□Hybrid


Content-based Filtering

□Recommendations are based on information on the content of items rather than on other users’ opinions □A buys books on economics □B is not interested in computer science

□Use machine learning/text mining algorithms to create user profiles about user preferences from examples based on a description of content


Content-based Filtering

□Problems:□Requires content that can be

transformed into a list of features□Users’ tastes must be represented as a

function of these features□Unable to exploit quality judgments of

other users


Collaborative Filtering

□ Users explicitly assign ratings to items

□ Predict rating of a user U for an item I:

1. Find users similar to U (neighbors) 2. Calculate rating of user U to item I as

weighted sum of ratings given by neighbors to item I


Collaborative Filtering

□ Problems:□ Cannot recommend new items (first-rater problem)

□Random: Choose item randomly with equal probabilities□Content analysis: Apply previously described approach if

cannot find similar users□Filterbots (programs simulating users): Constantly do

searches and rate items using some primitive algorithms□ Cannot match new users: they have rated nothing (cold

start problem)□Provide average ratings□User agents collect implicit ratings□Put users in categories □Select items for users to rate


From recommendations to decision making

□Define a problem□Return ticket from Trento to Lecce

□Identify alternatives□By train:

□price: 100€, duration: 26 hours, personal comfort: high□By plain (Venezia – Brindisi):

□price: 250€, duration: 14 hours, personal comfort: low

□Make the choice□Train

□Explain the decision□It is much cheaper


Implicit Culture

□Provide actors with suggestions based on behavioural patterns extracted from history of actions

□Community has knowledge specific to the environment: community culture

□Encourage a newcomer to behave according to community culture

□http://www.dit.unitn.it/~implicit


Implicit Culture Definitions

□ Action – something that can be done□ Agent (actor) – somebody or something performing an

action□ Object – something that passively participate in the

action□ Situation – a state of the world faced by the agent.

Includes a set of objects and a set of possible actions□ Culture – a usual behavior of the group of agents□ Group G – group of agents which behaviour is observed □ Group G' – group of agents who require recommendations□ Implicit Culture relation – situations in which agents of the

group G behave similarly to agents of the group G'□ System for Implicit Culture Support (SICS) – a system which

tries to establish IC relation


System for Implicit Culture Support

D B o f o bs e rv a tio ns

Inductiv e M o dule

C ultura l A c tio n F ind er

S ceneP ro d ucer

C o mpo s e r

th e o ry

a g e n ts , o b je c ts , a c t io n s , s c e n e s

s c e n e s

d o ma in th e o ry

o b s e rv a t io n s

o b s e rv a t io n s

s c e n e s

O bs e rv e r

a g e n ts , o b je c ts , a c t io n s , s c e n e s

Stores information

about actions

Produce a theory about common user

behaviorProduce

recommendation about

action


SICS Architecture

CoreAOP

Helpers

SIC

S C

ore

Co

nfi

gu

rati

on

an

d

Sto

rag

e L

aye

r

Configuration Module

Rule Storage Module

Storage Module

SIC

S L

aye

rComposer Adapters

Composer

Inductive Module

Application

SIC

S R

em

ote

Cli

en

tS

ICS

Re

mo

te M

od

ule

R e mote M oduleAO P H e lpe rs

ExceptionManager

LoggingService SICS Adapters

Spring Proxies/Adapters

AxisEJB

Seria lizableObjects

Over RMI


Over SOAP


Remote C lient Adapters

Spring Proxies/Adapters

R e mote M oduleAO P H e lpe rs

ExceptionManager

LoggingService


IC-Serv ice

SIC

S C

ore

□ The IC-Service is implemented in java and uses some libraries

□ Can be used in an application in a number of ways:□ As a java library□ As an EJB component in

J2EE environment□ As a web service

□ Observations are stored in XML files or in database


Composer & Inductive Module

C omposer U tilities

C omposer Implementation

C AF U tilitiesSimilarity U tilities

Inductive Module Apriori Implementation

Apriori R ulesGenerator

Apriori AlgorithmOther algorithms

Instance Configuration

Inductive Module Configuration

Inductive ModuleConstants

Composer Configuration

ComposerConstants

Configuration OfSimilarity Functions

XML DefinitionLoader Sim ple Class

W rapper

XML file

Exploits the observations and thetheory in order to suggest actions in a

given situation.

Analyzes the stored observations and applies data mining techniques to find a theory about the community culture


IC for Web Service Selection

<respond>

<develop><invoke><respond>

<register>

<query>

<report>

<recommend>

Web ServiceWeb Service Proxy(Axis)

Proxy(Axis) ApplicationApplication

Registry

Community

IC-ServiceIC-Service

<feedback>

□How to select a web service with high quality suitable for your problem?□History-based selection□Quality of Service = Quality of Experience


Observation of web service invocations

□ Actors:□ Applications (application name, user name, location)□ Users (user name, location)

□ Objects:□ Operation (operation name, web service name, category)□ Inputs/Outputs (parameter name, parameter value)□ Requests (operation names, input/output parameters,

category)□ Actions:

□ Bind (timestamp, web service), □ Invoke (timestamp, operation, input),□ Get response (timestamp, operation, output, response time), □ Raise exception (timestamp, operation, exception type, input), □ Provide feedback (report about contract violations, domain-

specific QoS parameters), □ Submit query (request, preferences)


Example of the theory rules

□Observations:□submit(A, newRequest(category=“currency”);…)□invoke(A, {http://www.webserviceX.NET/}

CurrencyConvertor & conversionRate;…)

□Theory rules:□submit(_U; _Q; …) invoke (_Y, *(category =

extract(category, _Q))); …)

□Request:□submit(newClient;

newRequest(category=currency); …)


Future work

□Empirical evaluation□Customizable similarity evaluation□Other mining algorithms□Enrich SICS with semantic matching:

□Hierarchy of actions, objects, attributes, etc.


Related work

□ [Blanzieri01] Blanzieri, E., Giorgini, P., Massa, P., Recla, S.: “Implicit culture for multi-agent interaction support”, Proc. of the Int. Conf. on Cooperative Information Systems, 2001, pp. 27-39.

□ [Maximilien04] Maximilien, E.M., Singh, M.P.: A framework and ontology for dynamic web services selection. IEEE Internet Computing 8(5) (2004) 84-93

□ [Manikrao05] Manikrao, U. Sh., Prabhakar, T.V.: “Dynamic Selection of Web Services with Recommendation System”, International Conference on Next Generation Web Services Practices (NWeSP'05), 2005, pp. 117-121.


Further information

□ Kokash, N.: "A Comparison of Web Service Interface Similarity Measures", Proceedings of STAIRS'06, Riva del Garda, Italy, August 2006, pp. 220--231, full paper. Extended version: Technical Report No DIT-06-025, April 2006, University of Trento, Italy.

□ Kokash, N., Van den Heuvel, W.-J., D'Andrea, V.: "Leveraging Web Services Discovery with Customizable Hybrid Matching", Proceedings of ICSOC, Chicago, December 2006, short paper, to appear. Extended version: Technical Report No DIT-06-042, July 2006, University of Trento, Italy.

□ Kokash, N.: "A Service Selection Model to Improve Composition Reliability", International Workshop on AI for Service Composition, in conjunction with ECAI'06, Riva del Garda, Italy, August 2006, pp. 9--14, full paper.

□ Birukou, A., Blanzieri, E., D'Andrea, V., Giorgini, P., Kokash, N., Modena, A.: "IC-Service: A Service-Oriented Approach to the Development of Recommendation Systems", The ACM Symposium on Applied Computing, Special Track on Web Technologies (WT), March 2007, to appear. Technical Report No DIT-06-044, July 2006, University of Trento, Italy.

□ http://dit.unitn.it/~kokash/

Web Service Discovery and Selection: Pragmatic Approaches Natallia Kokash PhD student, XX cycle...

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