manish(ECE)x - vsrd international journals division


Nov 3, 2013 (4 years and 6 months ago)


Context Aware Service Matchmaking Using Naïve Bayesian Classifier

Manish Saxena

Reserch Scholar

Electronics & Communication Engineering

CMJ University




S. K. Shrivastava


SBITM, Batul



With enormous increase in the availability of Web services, matchmaking of relevant web services has become
a sign
ificant challenge in the process of service discovery. The current solutions available for service
matchmaking use keywords, through which only primitive matching is possible. These keyword based
mechanism provides irrelevant results and also do not have c
ontrol over all the available services. To cope with
this problem, this paper focus on classifying the available services and locate the relevant services based on the
context of information requested by customers. To automatically generate wrappers for th
e service description,
we used co
occurrence based Clustering algorithm and Naive Bayesian classifier model

Keywords :

Web services Matchmaking, Service selection, Service discovery, Naïve Bayes classifier,



Web services technology
allows applications to

communicate with each other in a platform and

language independent manner for the

development of distributed computing based on SOC

(Service Oriented
Computing) and deliver application

functionalities as services to eithe
r end
user application

or other services.
These services are autonomous,

independent entities that can be delivered,

published, discovered and
loosely coupled. With technologies such as SOAP [13] a simple XML

lightweight protocol to let
ications exchange

information over HTTP, WSDL [14] an XML

language for describing Web
services and how to

access them. UDDI [15] is a repository for dynamic

service publication and discovery. Web
services are the

powerful mechanism for integrating ex
isting software

applications over the web, independently

programming language, execution platform or

transport protocol.

Web Services keep on emerging at an ever

increasing pace. As Web services increases, many

businesses are providing similar services

functionalities. Therefore, it is very

difficult for users to find an appropriate service among

the sea of services.
Research in web services covers

various areas of web services starting from service

grounding to service
composition and s
ervice mining.

Web service discovery is one such area that deals with

the process of locating
or discovering related service

descriptions that describes a particular web service

using the Web Service
Description Language (WSDL).

Service matchmaking in web
service discovery is

concerned with matching the
service query

requirements with number of available services in the


To make the process of service
matchmaking in

implementing service discovery more appropriate,

service matchmaking can be automat
ed. Not
only the

automated system should produce a result, but it

should also do so in an efficient manner. Efficient here

means the context and functional quality of the

services as specified by the user as their preferences.

Context is
the information th
at characterizes the

interactions between humans, applications, and the

environment. From a Web services

perspective, context is defined as a set of common

data about the
current execution status of a Web

service and its capability of
collaborating with peers,

possibly enacted by
distinct providers or customers [1].

Here, context is considered as key information

characterizing the customer’s
information, e.g.,

customer preferences, needs of customer, about the

location, and useful infor
mation about the

where customer operates, e.g., date, time, on

activities and interactions with services and/or


This paper proposes a matchmaking process to find

services by making use of context and semantics


the service query. To define and extract

services among available services in the registry, this

paper makes
use of domain ontology. Merely, to make

the process of service matchmaking easier, this paper

also focuses on
classifying the service description.


is achieved by automatically generating wrappers for

the service
description with the use of co

based clustering algorithm and Naïve Bayesian

classification model.

The remainder of the paper is structured as follows:

Section 2 describes th
e related works on service

matchmaking process. The proposed matchmaking

process is described and illustrated in Section 3.

Section 4
covers the classification technique of service

description by naïve bayes classification model.

Finally, Section 5
gives t
he conclusion and future work


Various techniques and approaches have been

proposed in the literature for service matchmaking to

service discovery.

Weili Han, Xingdong and et al [2] proposed

context and semantics based matchm
aking where

semantics of both the technical and business process of

registered services were used to improve the

of matchmaking in an integrated development

environment based on Eclipse. However, the context

information they used depends on t
he infrastructure

and was suitable for the closed system, so it is weak in


Sudhir Agarwal and Stephen Lamparter [3]

proposed a generic approach for modelling

of information for various web services attributes as

part of
ontology for describing composite web

services. They have used a tool that allows a user to

enter his preferences and provide the user a list of

services sorted by rank. Depending on these

services a user can decide more easily which of the

ces, he
wishes to execute. Further, users can

define an earlier threshold of rank for the services,

which he wishes to be
executed. Kuster proposed

[4] an approach to automate service discovery,

matchmaking and composition.
During the service

king, they use a single and multiple
effects to

rank the qualified services. Keild et al
[5] presented a

generic framework to support the development of

aware adaptable Web services. This

framework separates clients/Web services from the

context fr
amework that supports clients and services.

transfer of context information is performed

through SOAP message header. Context information

can be
explicitly and directly processed by clients or

Web services or be automatically handled by the


The support for context
aware services depends on

an improved semantic model of services by

ontologies that support formal description and

reasoning [6]. Semantic model may contribute not only

handle problems related to service interoperab

but also in order to take into account different aspects

of the
environment in which the service is executed.

Yoji Yamato and Hiroshi Sunaga [7] proposed a

flexible service
composition framework, where a

level service situation is translate
d and its

components are
dynamically originate, elected, and

bound. Service components are changed with respect

to the context change
since user situations may change

instantly. Jian Wu and Zhaohui Wu [8] developed a

model by classifying the
properties of
web services

into four categories. These categories have been given

certain similarity assessment
methods and based on

these assessment methods; the matchmaking process

has been done on the web to
improve the effect of web

services. Here, WSDL specificatio
ns are developed

independently from the module

Zakaria Maamar et al [9] proposed a technique

which takes into account both the context and policy

Web services composition. The role of context

provides information about environment the service

occurs. The transition of one level to

another level produce the action taken place, the

context and type of policy
used. Three types of policies

are used in this technique namely Engagement,

Mediation and Deployment.
Policies are defined as

information which can be used to modify the

behaviour of a system’’. The structure for
each type of

context is defined along with its arguments. The

composite level includes arguments such as label,

global ontology, previous component web service,

component, next component, beginning time,

date. The semantic kevel includes arguments such

as label, current composite web service, current component
web service, triggered mapping function per current composite web service, triggered

conversion funct
ion per
current component web

service, and date.

Most existing matchmaking techniques are intended

for tightly coupled systems, within the control of a

organization. The interaction model between

components in a Web service
based system is also

erent from
that of components in a single


environment. Therefore, it is clearly seen

from the literature that the
embracing of context

with semantic matchmaking of services enhances the

performance to produce the
optimized solution when

compared with conventional service matchmaking



Service matchmaking is the process of selecting

appropriate service(s) from a set of available services

for a
service request. In other words, when we check

whether a service

matches to a request, we actually

whether some functional and temporal properties

required by the consumer are satisfied by the service or

not. It
is executed only when the web service request

and web service advertisement matches. The main

issue in

matchmaking is how to represent the

advertised and requested services, and how to calculate

matching between these services. The

advertised and requested services can represent data or

services by using
many type of representation. Syntax

d matchmaking yields irrelevant retrieval of

services since nature of
service is not captured in the

service description. The traditional keyword

matchmaking supported by
UDDI registries only

considers inputs and outputs for matchmaking or

simplify t
he functional descriptions. For

keywords extracted by many queries in the same

domain may not be considered with the same

importance, since some keywords may be more

significant than others. Therefore, the need to give a

interpretation a
ccording to the context

emerges. Here we propose a service matchmaking

model where the
context and functional properties of a

service are expressed in terms of inputs, preconditions

and effects. Input
terms are represented as concepts

with the use of domai
n ontologies. Domain ontology

provides a conceptual
structure of a problem domain

that holds information collected and processed in a

classified manner. Thus, it
eliminates the problem

caused by syntactic matchmaking.

In addition to these ontologies, conte
xt of the user

play a major role in matching the desired services.


pictorial representation of the

service matchmaking
process. A service retriever uses

the service lookup directory to access the services from

the UDDI repository
based on the service c

request. It initially uses the semantic matchmaking to

select the appropriate
services. This can be done by

using ontologies, which would be discussed detailed in


The retrieved
services are matched with respect to

contextual parameters suc
h as business and

environmental properties. The
business properties

includes such as cost, organization arrangement,

payment method and reputation. The

properties include the temporal and location of the

service. The service retriever selects


services from the repository and uses the matchmaking

algorithm to select the related services with
respect to

the contextual parameters. Figure 1 clearly depicts the

service matchmaking process. The process
starts by

submitting the user
query to retrieve the services that

satisfy their needs and requirements. The Query

processor on the client side, process the query by

identifying the functional semantics and non functional

qualities embodied in the user query.

The service context extract
or analyzes the related

contexts of the service to be selected with aid of the

processor agent and prepares the contexts

information of the web service with the use of domain

ontology. On
the provider side, the service retriever

agent analyzes the se
rvices that are published by the

service provider and
evaluates the functionality of

services defined in the service specification. The

service specification holds
service id, service name,

providers name, provider’s id, service description, QoS

details et
c. The evaluated
services are then passed to

service context extractor. The extraction of service

context is a critical process which
is not focused in this

paper. The context extractor extracts the context of all

the services and is send to service
fier. The

process of service description classification is

described in the next section.

Service Lookup directory on the client side is used

to select the services for matchmaking that has been

used or retrieved by the user. It has all the

ble services the user has retrieved recently.

Initially the service
retriever selects the services with

the intervention of service lookup. Here, the semantic

based retrieval is
supported by the ontology. Ontology

represents information about a certain dom
ain. This

information includes
entities in the domain, their

property and relationship with each other. Entities in

the ontology are termed as
Concepts. A well

syntax is required to explicitly represent concepts of a

domain. RDF [10] context is
d for describing

ontology. Web Ontology Language (OWL) [11] is

developed on the top of RDF and is used
for ontology


The service repository is where all the published

services are found and invoked across
the Web. Based

on XML, SOAP and other s
tandards for service

invocation allow an application to interact with

applications to select the required services. In this

matchmaking process service repository is used to

access all the services requested by the consumer. The

service matchmaker
through the service lookup

may consume the services and check for the

services with the requested and advertised service

description if it is
been already used. Otherwise, the

process of matchmaking is done by matching with the

classified service
description kept in the service


The services, which match exactly both the service

requester and service provider description, are

consumed by
the service consumer for further service

discovery and service composition process.


To make the process of service matchmaking easier,

this section describes the classification of service

description. Once the service description of the

published services is classified, the task of matching

semantics found in service

request to semantics of

available services in the repository can be easily done


Classification of service description reflects the

semantics of the services as well as its region and we

would like
to induce from

the concepts that

relations between different

regions. In general it is
easier to induce the

relationship from shared concepts but the

relationship is harder to
deduce. We assume that the

irrelevance of two regions can be deduced by

the irrelevance of all
concepts between two regions.

This assumption is reasonable because although

classification of description is
likely to be incomplete,

it is always complete for those concepts that we care


4.2 CO


In general, high concurrence concepts are likely to

be used together to describe same service description.

other words, two services are likely to belong to the

same conceptual group if they have high co

vice versa.


After the pair
wise construction between two

regions of service description, we perform clustering to

region clusters. K
Means is a popular

clustering method. Since K
Means cann
ot directly

handle pair

constraints, we adapt a variant of

Means performing the clustering.


The matchmaking process should be efficient in service selection to locate and select the requested

services by
the consumer. This paper
uses both the

context and semantic property of services for

matchmaking. To make the
process of service

matchmaking easier, a service description classifier

technique is proposed.



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