ontology and tool support for quality service management

Ontology and tool support forquality service management

Adel Alti and Abbdellah BoukerramComputer Science Department, University of Setif, Setif, Algeria, and

Philippe RooseComputer Science Department, LIUPPA – University of Pau,

Anglet, France

Abstract

Purpose – The purpose of this paper was to design ontology for describing semantic context-awarequality services, and to present a new web management tool that provides a great flexibility andenables automatic semantic adaptation and customization of mobile client services.

Design/methodology/approach – The tool is developed using ontology-based approach. Thisontology captures a shared conceptual schema common in the tourism domain and maintains semanticquality information in heterogeneous service providers for service model. The results of the tool will becompared to prior works from other quality and distributed based service selection methods formobile-based application.

Findings – The tool support is based in the ontology Context-aware Quality Semantic WebService called (CxQWS). At the first step, services are defined as a set of semantic metadata,reflecting service requirements and QoS parameters. At the second step, services with a semanticcontextual metadata are elaborated. Such a procedure ensures that the selection decisions should bebased on the semantic quality representation of the created services. The SELETOR tool resultssuggest that the level of intelligent method use continue to be high flexibility in World Tourismorganisations.

Research limitations/implications – The tourism services in a mobile environment have a criticalrole in creating tourist satisfaction. They are neither a uniform group, nor able to give consistentlyhigh service quality. Indeed they have significantly different platforms and a variety of heterogeneousservice providers which make the management of service qualities complex.

Practical implications – A significant proposition is to integrate new tourism quality attributes ofmobile-based application, to provide a dynamic adaptation of selection services based on contextmetadata parameters (user, environment, device, and service provider context) and the management ofthe heterogeneity of service needs, of mobile devices capacities and their various communicationprotocols (GPRS, WIFI, Bluetooth, etc.) as well as the media variety (sound, video, text and image),possibly reflecting the decreased time responses and the increased visibility of standard servicesmanagement methods.

Originality/value – The paper proposes SELECTOR, a dynamic service selection tool based onCxQWS ontology, defined as set of semantic metadata, which context and QoS parameters. The tool isbased on semantic services and offer architecture, with three layers (semantic query, managementand web services). The most innovative characteristic of the tool is that it profits from the potentialof semantic representation techniques to express high level explicit constraints, while they may beuseful to guide the selection and adaptation process. This tool provides low adaptation effort, e.g. takesinto account all the heterogeneous services as its various communication protocols (GSM, 3G,Bluetooth, etc.) as consequences of self-selection for dynamic context evolution guided by theadaptation policies.

Keywords Tourism, Mobile technology, Semantics, Data management, Context-aware,Selection of services, Quality of service, Context matching

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1328-7265.htm

Journal of Systems and InformationTechnologyVol. 15 No. 1, 2013pp. 4-23q Emerald Group Publishing Limited1328-7265DOI 10.1108/13287261311322567

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1. IntroductionRecently, the World Tourism Organization emphasized that the future of the tourismindustry depends on the ability to instill a culture of mobile communication and qualityin tourism services (Pashtan et al., 2003; Mizoguchi, 2004). The tourism services in amobile environment have a critical role in creating tourist satisfaction. They are neithera uniform group, nor able to give consistently high service quality. Indeed they havesignificantly different networks environments and a variety of heterogeneous serviceproviders which make the management of service qualities complex. This complexity isclearly noticeable when a given service is provided by two or more providers, within thesame area of the mobile clients; a decision should be made to select the most appropriateservice based on contextual description with the best quality of service. The identicalservices have common semantics characteristics (name, city, category), within differentproviders, versions, QoS and executions contexts.

The heterogeneity of components regarding embedded sensors, CPU power,communication mechanisms (GPRS, WIFI, Bluetooth, ZigBee, etc.), speed oftransmission as well as the media variety (sound, video, text, and image) requirestaking into account adaptation to a semantic level in order to avoid the ad hoc solutionswhich are not reusable and/or generalized. Diversity of languages, protocols, andhardware platforms lead to major incompatibility issues. Moreover, multimediaservices adaption guided by user and community requirements and preferences are noteasy to be performed in this context. Communication protocols, security, and QoS areother non-functional concerns which present new design challenges.

The application of context-awareness has been demonstrated in a large number oftourism applications (Abowd, 1997; Pashtan et al., 2004). Context aware computing playsan important role in tourism applications to better understand user context and adaptservice with the continuous evolution of its context. The web service is an ideal fit in thetourism services, because it allows the service easy integration over heterogeneoussystems. Ontologies provide a good way to treat semantic interoperability between theheterogeneous representations of web services. Indeed, the semantic web service ontology(OWL-S) offer sophisticated capabilities including automated discovery, composition,invocation and monitoring (Mcllraith and Martin, 2003). But OWL-S ontology does notaddress how to consider non-functional concerns, i.e. how to represent them. Furthermore,OWL-S does not really take into account the contextual information in web servicesquality selection for very limited mobile device like PDA, Smartphone, etc.

In this paper, we present the tool SELETOR which has been developed for servicesselection. We present also the ConteXt-aware Quality of Semantic Web Service Ontology(CxQWS) includes support for web service quality control and resources requirementschanges. The explicit separation of functional and non-functional concerns is the mainconcern of the CxOWS aiming to well capturing resources-awareness and controllingquality at semantic level.

The contribution of this work is deals with a semantic level which help improvingservices qualities provided to tourists in a discovery and interaction mobileenvironment. The goal of the proposed ontology is to provide semantic level facilitiesthat allow the management of quality parameters of heterogeneous services. It providesalso a dynamic adaptation of selection services based on context metadata parameters(user, environment, device, and service provider context) for the better flexibility,adaptation and customization of mobile client software.

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The key idea of the proposed web service oriented architecture consists in theCxQWS. At the first step, services are defined as set of semantic metadata, which reflectservice requirements and QoS parameters. At the second step, services with a semanticcontextual metadata are elaborated. Such a procedure ensures that the selectiondecisions should be based on the semantic quality representation of the created services.

The rest of this paper is organized as follows: Section 2 presents our motivations andother related works. Section 3 details CxQWS ontology, while Section 4 presents anoverview of the proposed framework. Section 5 presents application scenarios of ourontology as well as implementation issues. Finally, Section 6 concludes and presentssome perspectives.

2. Motivation and related works2.1 Management of context-aware web services qualitiesThere is a diversity of contextual information and a variety of quality attributes, we findseveral ontological languages such as WSMO (WSMO, 2005), DAML-S (Horrocks, 2002),ConteXtML (Rayn, 1999), CxBR (Gonzalez and Ahlers, 1999) CWSC4EC (Boukadi et al.,2008), CA-Discovery (Suraci et al., 2007), CxG (Brezillon, 2003). These languages providethe means for defining QoS and context in specific application domains such aspervasive and mobile computing. However, neither various communication protocols,not heterogeneous services are discussed. Our approach addresses the limitations ofexisting approaches, suggesting the mechanisms to integrate new tourism qualityattributes of mobile-based application, to provide a dynamic adaptation of selectionservices based on context metadata parameters (user, environment, device, and serviceprovider context) and the management of the heterogeneity of service needs, of mobiledevices capacities and their various communication protocols (GPRS, WIFI, Bluetooth,etc.) as well as the media variety (sound, video, text, and image).

A semantic-based architecture for intelligent destination management system isproposed in Kanellopoulus and Kotsiantis (2007). In this architecture, a semantic webontology is used to model the tourism destinations, user profiles, and the user contexts.The proposed OWL-S is extended with context information taken into account. Thisarchitecture is context-aware but suffered from a semantic-based quality evaluation forachieving an efficient framework.

Boukadi et al. (2008) work which is a new context categorization suitablefor the enterprise collaboration environment and proposed a context-aware webservice ontology for describing and selecting web services enhances dynamicity andagility of collaboration. The proposed approach takes into account only contextualinformation necessary for web service collaboration, and not considers QoS parametersand their semantics for web services discovery and selection.

In Nicolicin-Georgescu et al. (2009) exploited ontologies and ontology-based rulesdata for improving query response times in data warehouses groups by modifyingcache memory allocations. This work ensures a better data warehouse management ata lower cost and enables autonomy by using autonomic loop control. However, thiswork concentrated only on service performance as indicator of the system quality andnot considers other QoS parameters like security and dynamic adaptation.

In Wang et al. (2006) presents a QoS model for service selection. This work is based onQoS ontology, which introduces a number of specific quality attributes and theirrespective quality measurements used for web service quality evaluation and selection.

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The authors have presented an interesting approach for annotating service descriptionwith QoS data. However, neither various communication protocols, not heterogeneousservices are discussed. Therefore, it does not provide support for dealing with the dynamicservice selection in order to adapt to highly varying context conditions. Moreover,proposed intelligent mechanisms (e.g. intelligent software agents) and techniques can beexploited in the practice of a new tourism quality management policy (Handler, 2003).

Currently, the OWL-S does not include a semantic description of contextualinformation. Thus, in its current state, it does not enable web business quality selectionin pervasives computing environments. Moreover, proposed intelligent mechanisms(e.g. intelligent software agents) and techniques can be exploited in the practice of anew QoS policy (Handler, 2003).

Recently, Keskes et al. (2010) proposed a model that makes an automatic selection ofbest service provider that is based on mixed context and QoS ontology for a given setof parameters of QoS. It approach provides interesting benefits for QoS enhancement,allows a well interoperability between heterogeneous services, but disagree in theautomatic service selection using multi-dimensional QoS and did not address any issuerelated to service integration in the limited portable devices.

Recently, in Lamolle et al. (2010) introduced an ontology framework for automaticnetworked multimedia systems deployment and configuration. Based on well-knownstandards, this framework integrates multimedia, distribution and platform concepts.This work offering excellent quality service management and deployment but disagreein the complex semantic context matching.

More recently Derdour et al. (2012), proposed the component, service, connector(CSC) platform for application-based components. This platform based on multimediasoftware architectures. It proposes a generic solution to the problem of incompatibilityof services, in term of media type to another, or of a media format to another format.This work treats syntactic interoperability between the heterogeneous services and didnot take into account the semantics of services and that of the location, category,QoS and context constraint and providers.

The methods and contributions of this paper are different from above related works.First, the paper exploits services selection of mobile devices for maximizing userQoS under resource constraint in mobile applications. Second, the paper proposes anintelligent business quality management system, which is based on the CxQWS. Thesuccess of this approach provides its members intelligent access to business qualityrelatively inexpensively. This approach provides improved performance, a dynamicadaptation of selection services based on context metadata parameters (user,environment, device, and service provider context) for the better flexibility. Above twocontributions do not appear in other related works.

2.2 Uses and reasons of ontology importanceTo investigate the importance of ontology from the technical view, for four reasons:

(1) Homogenization. Describing different contextual tourism information and differentqualities tourism service using the same ontology confers to the ontology a role ofhomogenization for at least the service quality and the context information that itdescribes (Mizoguchi, 2004; Gargentilla and Gomez-Perez, 2004).

(2) Interoperability. Ontologies provide a good way to treat semantic interoperabilitybetween the heterogeneous representations of web services and thus QoS


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parameters and context parameters of tourism services by sharing commonrepresentation and semantics (Handler, 2007; Mao et al., 2010; Quality SupportCommittee, 2003).

(3) Expressiveness. Most ontology languages such as RDF and OWL-S with highexpressiveness are efficient and perform much better for expressing semanticsof various portable devices properties (PDA, smart phone, iPods, and Palms)and various QoS parameters.

(4) Easy service discovery and selection. Ontologies make the discovery and theselection of web business quality services easier, faster and automatic. Themain mechanism for service discovery is service registry and semantics canbe used for the discovery of web business quality service registries and for themanagement of QoS parameters and context parameters. This is for a betterservice choice among several equivalent functionalities (Handler, 2007).

2.3 Objectives and motivationsCurrently, the web service was described with XML standards languages such asOWL-S that facilitates the web service discovery and selection. However, contextualinformation and mobility awareness is not considered by OWL-S. This results to basicdrawbacks:

. the poor service quality results from employing syntactic and semanticdescription without semantic-based context technologies and semantic-basedmobility information;

. difficulty in meeting service quality such as reliability and efficiency; and

. the quality management applications for tourism business depend on theirhardware and software platforms.

These disadvantages can be tided, if we extend OWL-S for matching user requirementwith tourism destination specifications at the semantic level, with context informationand QoS parameters both takes into account. Thus, an efficient mechanism is providedmaking the web service of mobile application/system easy to discover and associatewith the semantic information contained in the ontology. Our proposed ontologyintroduces semantics related to the web business quality and also semantics related tothe context-aware service which is defined as set of semantic metadata (Figure 1).

Due to heterogeneous services as well as its various communication protocols(GSM, 3G, Bluetooth, etc.) can be specified more easily using a semantic representationto better support resource-awareness. The automated processing of web businessqualities in mobile-based applications requires that the user should be able to locate abusiness quality through its meaning independent of which service context is evolved.In our case, we refer by context-aware QoS modelling the OWL-S that will be used formatching user quality requirements with tourism destination specifications at thesemantic level, with context information of heterogeneous resources taken into account.

Since, there is a diversity of quality attributes with a variety of contextual information,we find several contextual languages such as WSMO (WSMO, 2005), ConteXtual Schema(CxS) (Turner, 1998), DAML-S (Horrocks, 2002), ConteXt-Based Reasoning (CxBR)(Gonzalez and Ahlers, 1999), Context Web Service and Community EnterpriseCollaboration (CWSC4EC) (Boukadi et al., 2008), Context-Awareness in Context

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Discovery (CA-Discovery) (Suraci et al., 2007), and ConteXtual Graphs (CxG) (Brezillon,2003). These languages provide the means for defining QoS and context in specificapplication domains such as pervasive and mobile computing. However, neither variouscommunication protocols, not heterogeneous services are discussed. Moreover, mobilityintroduces higher degree of heterogeneity and dynamicity than traditional distributedsystems. The semantic quality service discovery and selection becomes essential in orderto adapt to highly varying context conditions.

3. A ConteXt-aware Quality Web Service OntologyThe user should be able to locate a service quality through its meaning, independent ofwhat the service quality is called, which language is used, which service context isevolved, and who the service provider is. Not only the QoS of different services needto be compatible, but also the devices performing the service execution need to respectcertain properties and constraints. The resource properties and constraints naturemake service qualities management and sharing an even more difficult. To exploit thecontext-aware service quality to their full potential, we need ontologies to describe theirsemantics. Any web service can be based on our proposed ontology CxQWS.

Our ontology aiming to well capturing resources-awareness and controlling qualityof services at semantic level, to well share their devices properties and constraints, toprovide a semantic service selection facilities, to adapt and personalize a service to adevice’s capabilities and to user preferences.

3.1 The quality of service modelBased on previous related works, the quality of any mobile-based application can berepresented on our ontology CxQWS, which is defined as set of metadata parameters.These QoS parameters are:

(1) customer care and attention;

(2) professional level of staff;

(3) presentation of food and drink;

(4) quality of food and drink;

(5) safety and security;

Figure 1.The core of the

ConteXt-aware QualityWeb Service Ontology

hasVersion

Is-a Is-a Is-a

QoS1 QoSn

hasQoShasContext

hasCity

CxQWS

ServiceCategory

hasCategory

Id

hasId

QoS

Name

Context

Service City

Multimedia Content

hasContent

Version

hasName

HasContextQoS

HasParameters

ContextConstraint

hasConstraints

ContextParameters

Has_Const_Param

Object property

Is-a relation

QoS2


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(6) level of service;

(7) speed and efficiency of service;

(8) quality of facilities;

(9) variety of facilities;

(10) tourist information;

(11) accessories in bedrooms;

(12) decoration;

(13) availability of breakfast, dinner, and room services;

(14) continuity of service; and

(15) durability of service.

Figure 1 shows the relevant classes of our meta-ontology. It allows the servicesproviders to publish metadata about their services within their context constraints inthe UDDI registry. Providers would instantiate the classes from the ontology andpublish the resulting individuals as OWL files on their web sites. The CxQWS definecommon concepts as well as relations ships among those concepts to represent QoSparameters and context constraints. CxQWS distinguishes the following concepts:

. Service has a name, a version and unique ID.

. service_category ¼ {hotels, hostels, motels, restaurants, cafes, camping,bungalows, etc.}.

. service_city: the city which the service facilities are located.

. QoS_parameters: the list of the QoS parameters. This class includes the above50 basic subclasses. QoS parameters concern not only the services but also thedevice resources where the execution is taking place.

. multimedia_content ¼ {text, images, sound, video}.

3.2 The context modelThe quality services selection must take the capabilities and limitations of a mobiledevice into account as well as any user requirements or preferences with respect to theservice. Context is any information can be collected from service (name, version, andresources needs), from device (resources capacities), from environment (time andlocation) and from user (preferences). CxQWS uses these informations to select aservice quality in a dynamic environment, by confronting a required context (userand service) for a provided context (mobile node). Figure 2 shows the basic subclassesof the class Context of CxQWS ontology. CxQWS ontology consists of user profile,preference, service provider, and device capability. Context ¼ {Service, User, Device,Environment}.

In the user-related context we identify user profile and user preferences. The userplays an important role within business quality. The appliances within its environmentshould adapt to the user, and not vice versa. Important properties include a user’sprofile, but also his preferences.

The user profile contains personnel, location and multimedia information. Userprofile describes user’s personnel information such as name, role, ID, phone, address,email, multimedia_content. The user preference includes some quality of web services

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profiles such as performance quality attributes. This is very interesting in the case ofhaving two web services providing the same functionalities. They may varying valuesof their performance quality attributes. In fact, these attributes model the competitiveadvantage that web service may have over one another.

The context environment representing available information characterizingnetwork connections, time, date and location constraints. Network connectionsconnect hardware components (PDAs, smart phone) having a limited bandwidth. Due toheterogeneous services as well as its various communication protocols (GSM, 3G,Bluetooth, etc.) can be specified more easily using a semantic representation to bettersupport resource-awareness. The location, the date and the time represent, respectively,the geographical location within the exactly period of time that the service opportunityshould be accomplished:

. Environment: {Time, Location, Date, Network}.

. Network ¼ {type, protocol, bandwidth}.

. Type ¼ {wired j wireless}.

. Protocol ¼ {GSM j 3G j WLAN j Bluetooth}.

. Bandwidth ¼ {128K j 256K j512K j 256K j 256K j 1M}.

The hardware components are mobile devices like PDAs or smart phone, areconstrained in their resources (memory size, CPU speed, battery energy, etc.) and act asexecution environment for web service. A resource-awareness about current usage ofprocessing power, memory, etc. is a prerequisite to guarantee a minimum QoS.

The service capability describes the operational features of a web service. Servicecapability includes semantic attributes like pre-conditions, effects and post-condition.The service confidence level is very interesting in the case of having two web servicesproviding the same functionalities. They may have varying values for their performancequality attributes.

Figure 2.An overview

of the hierarchy rootedin the context class

Is-a

Is-a

Is-a

Is-a

hasTime

hasDate

Context

Context User

Context Device

Context Service

ContextEnvironnement

Is-a

Is-aUser Profile

UserPreferences

Time

Date

LocationhasLocation

Network

hasType Type

Protocol

BandwithhasBandwidth

hasProtocol

Is-a

Is-a

Is-a

IODevice

Resource

hasCPUCPU Speed

Battery energy

Memory SizeHasMemory

hasBettery

hasVersionVersion

Confidence LevelhasLevel

Service CapabilityhasCapability


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3.3 Context constraintA context constraint is defined through the terms context parameters and contextexpression, which is further categorized by simple expression and/or compositeexpression, thus fuming a multi-level context ontology as shown in Figure 3:

. Context_ parameters. Each context category has specific context parameters. Forexample, the device-related context is a collection of parameters (memory size,CPU power, bandwidth, battery lifecycle, etc.). Some context parameters may usesemantically related terms, e.g. CPU power and CPU speed.

. Context_expression. Denotes an expression which consists of context parameter,logic operator and logic value.

. Context_constraint. Consists of simple or composite context expression. Forexample, a context constraint can be bandwidth ¼ 10000 or memory_size ¼ “less”.

4. Overview of the framework4.1 Dynamic selection framwork overviewThe proposal framework is a generic, dynamic and interpretable system regarding themanagement of web tourist service qualities. The framework facilitates semanticdiscovery and interoperability of web services that manage and deliver parts of touristservice qualities (for future reservation). It offers two operations. The first is the findweb services qualities. This takes as input the user query, then returns list of candidateweb services from the web service registry. The second is to allow providers to publishmetadata about their services dynamically and to define their own specializations ofthe default classes based on the ontology CxQWS as shown in Figure 1.

Our framework for the context-aware quality web service discovery, as shown inFigure 4, is a three layers architecture consisting of top-level layer dedicated to the semanticquery layer. The middle layer consists in three basic agents which are the Quality SemanticService Discovery Agent (QSDA), the Quality Semantic Context Evaluator Agent (QCEA)and the Quality Semantic Service Sensor Agent (QSSA). The basic layer is a group of webservices which the sequel consumes the local web services exposed by every web servicesprovider offering services qualities and contexts descriptions.

Figure 3.Upper ontology forcontext constraint

hasOperator

hasContextType

HasLogicValue

ContextConstraint

ContextParameters

Id Name

hasId hasName

ContextExpression

LogValue

LogicOperator

HasExpression

Has_Const_Para

Context Type

HasParameters

isComposedBy

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The cornerstone of our framework for dynamic quality web services discovery andselection is QSDA. The QSDA takes each user request and initiate the web servicediscovery mechanism based on the functional constraints. The QSDA offers twooperations: the first is the findWebService operation. This takes as input the XML filedescribing the user’s goals, then returns a list of candidate web services from the webservice registry (UDDI) fitting the goal description. The second is updateServiceListoperation used by QCEA in order to update list of services which provide a betterservice quality to a specific mobile client context.

4.2 The dynamic quality service selection processThe discovery and selection process is a three-step process.

Step 1. The mobile client has no previous knowledge about the discovery services. Inaddition, when a given service is provided by two or more providers within the same areaof the mobile clients; a decision should be made to select the most appropriate servicebased on contextual description with a better quality of service. Users submit queriesthrough the mobile client application interface. As soon as a user describes the servicequalities queries, the QSDA initiate the web service discovery mechanism based on thefunctional constraints.

Step 2. The QSDA responsible to communicate with the corresponding web services inorder to initiate the query and finally to present personalized results. This is made thanksto semantic-based matching discovery which the QSDA could exploit the categorization ofthe ontology hierarchy to find suitable matches. The QSDA captures user’s goals fromuser’s queries which express a user’s intention that s/he expects to get the results fromthe framework. Each goal represents functionalities and non-functionalities that aparticular business domain offers. The information in goal is semantically described in ourontology by the service properties (service_category, service_city, QoS parameters,multimedia_content). Each goal can be expressed as an AND-OR of simple expression.The QSDA identify web services which matches functional requirements and QoSparameters of user’s queries. A list of matched web services is passed to the QCEA forfurther selections.

Figure 4.Description of the

framework architecture


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Step 3. Depending on the resources availability on the mobile device, on theenvironment context and on the service provider context, the function of QCEA is toselect a better service from the primary list identified in the previous step within theequivalent functionalities:

<?xml version ¼ “1.0”?><rdf:RDFxmlns:rdf ¼ “http://www.w3.org/1999/02/22-rdf-syntax-ns#”xmlns:rdfs ¼ “http://www.w3.org/2000/01/rdf-schema#”xml:base ¼ “http://univ-setif.dz/schemas/context_aware_qality_service_ontology”><rdfs:Class rdf:ID ¼ “QoS_parameters”/><rdfs:Property rdf:ID ¼“Service_id”/><rdfs:Property rdf:ID ¼“Service_name”/>. . .<rdfs: Property rdf:ID¼“Is_Associatedwith”>

<rdfs:range rdf:resource ¼ “# Context”/><rdfs:domain rdf:resource¼“#Servie”/>

</rdfs:Property ><rdfs: Class rdf:ID ¼“QoS_parameters”/><rdfs: Property rdf:ID ¼“QoS_1”/><rdfs: Property rdf:ID ¼“QoS_2”/>. . .<rdfs:Class rdf:ID¼“ressource”>

<rdfs:subClassOf rdf:resource ¼ “#Context_Device”/><rdfs:Property rdf:ID ¼“Memory_size”/><rdfs:Property rdf:ID ¼“CPU_speed”/><rdfs:Property rdf:ID ¼“Battery_energy”/>

Step 3.1. The QSSA is validated to provide collections of context metadata(context metadata) based on the ontology as shown in Figure 1. In case of changes inthe client context (i.e. less available memory, less processor power) or in the environmentcontext (i.e. less bandwidth), the QSSA will be notified by a set of contexts metadata inorder to dynamically skip the next selected service of the primary list of services offeringthe same service functionalities which requires less available resources.

Step 3.2. This step consists on matching context metadata of each service of theprimary list of Step 1 with the device context parameters from the QSSA. The serviceselections are based on inference techniques and semantic interpretation to:

(1) detect context semantic matching between two services contexts based on theirsemantic representation (context type, context parameters, and logical operators);

(2) evaluate the resulting matching; and

(3) automatically add a service in the list of selected web services.

In Verma et al. (2005), this comparison uses a simple syntactic comparison of webservices policies but our top level ontology resolves the semantic and syntacticheterogeneities that resulted from the different representation languages and serviceproviders. This step returns a list of web services which provide a better service qualityto a specific mobile client:

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Dynamic Service Selection AlgorithmInput Goal¼{g1, g2, g3, . . . gn} // a set of user’s goals

Service ¼ {s1, s2, . . . sn} // a set of servicesOutput

MatchedService_List1 ˆ f // a set of services that meet the user’s goalMatchedService_List2 ˆ f // a set of services that meet the user’s goal and

sensorcontext constraints

Begin// match user’s goal with servicesfor each s in Service {

for each g in Goal {if match_service_category(g, s) AND match_service_city(g, s) AND

AND match_multimedia_content(g, s) match_service_QoS_parameters(g, s) then

{ MatchedService_List1.add(s);}}

}// match context constraints of Quality Semantic Service Sensor Agent (QSSA)with selected service constraintsif MatchedService_List1.size ! ¼ 0 {

Context sensor_cx 5 sensor_getContext ( ); // return a set of contextsconstraints

for each s in MatchedService_List1 {// return context description ofservice

Context s_cx 5 service_getContext (service ID, UDDI);for each cx in sensor_cx {

if match_context_type(cx, s_cx) AND match_context_constraint(cx,s_cx) then

{ MatchedService_List2.add(s);}}

}}return MatchedService_List2;

End;

4.3 Implementing CxQWS ontologyWe have utilized Protege (The Protege Ontology Editor and Knowledge AcquisitionSystem, 2009) to build the CxQWS ontology using Resource Description Framework(RDF/S) language in which each statement consists of a triplet (subject, predicate andobject). In particular we have used the RDF schema to describe our ontology conceptsand its relationships. We briefly illustrate selected features of implementation of ourontology through snippets of the RDF code.

Using Java API we have implemented a full selecting tool based on CxQWSdefinitions, we called it Selector. It is deployed as an Eclipse Plug-in. This environmentallows architects to ability to select, to re-use existing services and to better understanduser context and dynamic service selection with the continuous evolution of its context.


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The most innovative characteristic of the tool is that it profits from the potential ofsemantic representation techniques to:

. expressing high level explicit constrains, while they may be useful to guide theselection and adaptation process;

. providing a semantic meta-level which help improving services qualitiesprovided to user in a discovery and interaction mobile environment;

. offering an unified context quality service to its users and facilitates the sharingand reusing of context information and quality information;

. separating functional from non-functional concerns to well capturingheterogonous context information and controlling quality at semantic level;

. exploiting services selection of mobile devices for maximizing user QoS underresource constraint in tourism domains; and

. providing interesting benefits for QoS enhancement, allows automatic serviceselection using multi-dimensional QoS and address issue related to specifyexplicit constraints.

5. The CxQWS ontology: usage scenariosAn adventurous tourist is planning a backpacking trip and looking for new places toexplore. His Smartphone with integrated GPS has become indispensable for findingnearby activities that match his interests and is handy for finding cheap accommodations.To facilitate quality service discovery, CxQWS ontology on the Smartphone provides asemantic markup of the user profile, the Smartphone context and the link to the CxQWSontology. Tourists want to find service qualities, in which their quality’s profiles areexpressed by a certain media types (e.g. video, image, audio, and text).

First scenarioA first simple scenario (Figure 5), tourists having specific requirements want to findservice qualities with combined criteria. For example, visitors want to find restaurantsmap, located in Setif, with high speed and efficiency of service, as well as statuaryobligations. This query can be answered by using the following semantic metadata:service_category, service_city, speed_and_efficency_of_service and statuary_obligations_of_ service. Our selection tool allows tourists to specify the type of service, its location anddifferent quality of services parameters; the type of context is its properties(e.g. throughput and memory size). This query can be submitted as follows:

(service_category ¼ “Restaurant”) AND(multimedia_content ¼ “Image”) AND (service_city ¼ “Setif”)

(speed_and_efficency_of_service ¼ “high”)AND(statuary_obligations_of_service ¼ “True”)

Second scenarioA second scenario (Figure 6), tourists want to find services all services on the Smartphone,that semantically matches the Smartphone context (i.e. the less available memory) and theenvironment context (i.e. the less bandwidth). The context constraint can be:

(context_type ¼ “Client”) AND (memory_size ¼ “low”) AND

(context_type ¼ “Environment”) AND (bandwidth_network ¼ “128K”)

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Third scenarioWhen he asked for the restaurant map, the QSDA returned two services: a black andwhite map and a coloured map (Figure 7). To limit the search results when browsing therestaurant map on the Smartphone, only the first one is returned that semanticallymatches the Smartphone context (i.e. the less available memory) and the environmentcontext (i.e. the less bandwidth). The context constraint can be:

(service_category ¼ “Restaurant”) AND(multimedia_content ¼ “Image”) AND (service_city ¼ “Setif”)

(speed_and_efficency_of_service ¼ “high”) AND(statuary_obligations_of_service ¼ “True”) AND

(context_type ¼ “Client”) AND (memory_size ¼ “low”) AND(context_type ¼ “Environment”) AND (bandwidth_network ¼ “128K”)

5.1 Intelligent software agents in the context-aware quality semantic serviceSoon, providers of web services will advertise their services and contexts constraints onthe semantic web, so that intelligent software agents can find and adapt them

Figure 5.Research results

for the first scenario


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dynamically. For, these agents (Figure 4), the semantic web infrastructure will be basedon the ontology as shown in Figure 1. This ontology would be published as OWL file.The ontology CxQWS would allow providers to instantiate the classes from theontology and publish the resulting individuals as OWL files on their web services.Then, a semantic web service specialized in entertainment context could send out acrawler agent to collect the available activities. If a user then asks for a specific type ofrestaurant for mobile limited device (low bandwidth, less memory, etc.), the softwareagent could exploit the categorization of the ontology hierarchy to find suitablematches, and call auxiliary web services. Providers of web services cannot only publishtheir metadata dynamically, but can also define their own specializations of the defaultclasses. For example, an ontology module could define chic_restaurant as subclass ofrestaurant, and put semantic restrictions on these classes to describe its characteristics.Then, if a software agent searches for restaurant facilities it would also find the instancesof the subtype, and also learn that chic restaurants are more expensive than usualrestaurant, because they involve the ability for guests to sing, etc. When a chic restaurantservice delivers a high density color image or video sequence, a notification was sent to theQSSA telling that there is not enough available memory to continue storing such an image.In this case, the QCEA will skip to the next available service offering the same image

Figure 6.Research resultsfor the second scenario

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with requires less memory (due to different internal representation technique). Moreover,proposed intelligent software agents (i.e. QSDA, QCEA, and QSSA agents) can beexploited in the practice of a new tourism quality management policy (Handler, 2007).

5.2 Experiments frameworkIn this section, we evaluate the performance of dynamic mobile selection algorithms(SSA) for querying semantic web services into context-aware applications using ourJava VM simulator. The experiments are conducted to compare our CxQWS selectionalgorithms (SCA) for querying of semantic web services into context-awareapplications with QoS Broker algorithm (Badidi et al., 2004), which is quality anddistributed based service selection model for mobile-based application. The reason forchoosing reference (Badidi et al., 2004) as the comparison is that both our work andreference (Badidi et al., 2004) provide a various selection policies based selectionalgorithm for mobile environment.

We simulate a tourist case study (second scenario) for two approaches using ourJava VM simulator and we have varied the user (and, respectively, the mobile device)from 1 to 150. The users use the system as modeled by a Poison-process with an arrival

Figure 7.Research results

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rate of 10 per hour. There are two communication protocols between the server and themobile client, either a GPS link, which has a rather a low bandwith, 3G link, which hasa higher bandwith. The simulation took not more than approximately 10 seconds on a3.5 Ghz Pentium PC. In the simulator, different agents are used QSDA, QCEA, andQSSA. The framework dynamic service selection starts a listening thread that listensto the requests from users. It receives the requests of users and puts them into thequeue. While the queue is not empty, the QSDA starts the dynamic service selectionalgorithm to find the right match.

5.3 Analysis and discussionThe first experiment is to test convergence of mobile services selection algorithms (SSA)which includes quality service semantic level algorithm and resources-awarenesssemantic level algorithm. In Figure 8, when user arrival number is low (n ¼ 15), theconvergence iteration of SSA is one. When user arrival rate increases to 45, theconvergence iterations of SSA increase to two. In task intensive case (n ¼ 90), there aremore request waiting for a match than the complete responses. When user arrival rateincreases quickly, the match of the services will increase, and some users will not affordrequests services.

The second experiment is to test performance of our CxOWS with QoS Broker.Figure 9 shows the evaluation results, meaning that our approach turns out to be thebest. When user arrival rate is small, there are enough resources (e.g. network bandwith)for mobile users to select efficient service, and obtain better response times. Comparedwith QoS Broker, the response times of SCA increase slowly than QoS Broker when theuser arrival rate increases. This result is practically significant as well related to thelow adaptation effort, e.g. takes into account all the heterogeneous services as itsvarious communication protocols (GSM, 3G, Bluetooth, etc.) as consequences ofself-selection for environment evolution (e.g. network bandwith) guided by the adaptionpolicies.

Figure 8.Convergence iterationsunder various devicearrival rate

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The selector tool managing the quality of tourism services at the semantic level, withcontext information taken into account. The tool provides a great flexibility, adaptationand an intelligent customization of tourism destination content. In our tool, this wasachieved with ontologies defining the quality of service and the context (user, device,service, and environment) by expressing high level explicit constraints.

6. Conclusion and future worksThe paper proposed CxQWS ontology being an extension to the web service, allows forconsidering quality and resource-awareness while discovering and selecting web service.It allows web services to share their media resources using semantic representation tothe end-user. Using quality and context parameters for describing and selecting webservices enhances dynamicity and transparency to mobile applications.

The paper proposed also an intelligent context-aware quality service managementtool (Selector). This tool allows web services to interoperate both with internal andexternal applications using the semantic quality and context matching techniques. Theidea of presented framework consists of two steps. At the first step, services are definedas set of semantic metadata, which reflect service requirements and QoS parameters. Atthe second step, services with a semantic contextual metadata are elaborated. Sucha procedure ensures that the selection decisions should be based on the semantic qualityrepresentation of the created services. We presented application scenarios of ourontology. As a result, Therefore, CxQWS provides a great flexibility and enablesintelligent adaptation and customization of mobile client software.

The most innovative characteristic of the tool is that it profits from the potential ofsemantic representation techniques to expressing high level explicit constraints, whilethey may be useful to guide the selection and adaptation process. It is fully adaptableto the user’s preferences and can be provide high quality service to mobile clients.For the near future, our tool will be included refinements on the ontology proposed forthe representation of the context-aware services qualities.

Figure 9.Response time under

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References

Abowd, G. (1997), “Cyberguide: a mobile context-aware tour guide”, Wireless Networks, Vol. 3No. 5, pp. 421-433.

Badidi, E., Esmahi, L. and Serhani, M.A. (2004), “A queuing model for service selection ofmulti-classes QoS-aware web services”, 2005 IEEE International Conference onWeb Services (ICWS 2005), pp. 17-24.

Boukadi, K., Ghedira, C., Chaari, S., Vincent, L. and Bataineh, E. (2008), “CWSC4EC: how toemploy context, web service, and community in enterprise collaboration”, Proceedings of8th International Conference on New Technologies in Distributed Systems (NOTERE’08),Lyon, France, Vol. 1, pp. 22-33.

Brezillon, P. (2003), “Context-based modelling of operators practices by contextual graphs”,Proceedings of 14th Mini Euro Conference in Humman Contered Processes.

Derdour, M., Roose, P. and Dalmau, M. (2012), “An adaptation platform for multimediaapplications CSC (component, service, connector)”, International Journal of Systems andInformation Technology, Vol. 1 No. 3, pp. 209-231.

Gargentilla, J.A. and Gomez-Perez, A. (2004), “A survey on ontology-based applications:e-commerce and knowledge management will deserve special attention”, OntoWebDeliverable 1.6.

Gonzalez, A.J. and Ahlers, R. (1999), “Context-based representation of intelligent behavior intraining simulations”, Int. Transactions of the Society for Computer Simulation, Vol. 15,pp. 153-166.

Handler, J. (2007), “Agents and the semantic web”, IEEE Intelligent Systems, Vol. 16 No. 2,pp. 30-37.

Horrocks, I. (2002), “CDAMLþOIL: a description logic for the semantic web”,IEEE Data Engineering Bulletin, Vol. 25, pp. 4-9.

Kanellopoulus, D. and Kotsiantis, S. (2007), “A semantic-based architecture for intelligentdestination management systems”, International Journal of Soft Computing, Vol. 2 No. 1,pp. 61-68.

Keskes, N., Lehireche, A. and Rahmoun, A. (2010), “Web services selection based on contextontology and quality of services”, International Arab Journal of e-Technology, Vol. 1 No. 3,pp. 98-105.

Lamolle, M., Gomez, J. and Exposito, E. (2010), “MODA: une architecture multimedia dirigee par lesontologies pour des systemes multimedia en reseau”, Proceedings of 4eme Conferencefrancophone sur les Architectures Logicielles (CAL’2010), Pau, France, pp. 137-151.

Mcllraith, S. and Martin, D. (2003), “Bringing semantics to web services”, IEEE IntelligentSystems, Vol. 18 No. 1, pp. 90-93.

Mao, M., Peng, Y. and Spring, M. (2010), “An adaptive ontology mapping approach withneural network based constraint satisfaction”, Journal of Web Semantics, Vol. 8 No. 1,pp. 14-25.

Mizoguchi, R. (2004), “Ontology engineering environments”, Handbook of Ontologies, Springer,Berlin, pp. 275-298.

Nicolicin-Georgescu, V., Benatier, V., Lehn, R. and Briand, H. (2009), “An ontology-basedautonomic system for improving data warehouse performances”, Proceedings of 13thInternational Conference on Knowledge-Based and Intelligent Information and EngineeringSystems (KES 2009), pp. 262-269.

Pashtan, A., Heusser, A. and Sheuermuem, P. (2004), “Personnel service areas for mobile webapplications”, IEEE Internet Computing, Vol. 8 No. 6, pp. 34-39.

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Pashtan, A., Blattler, R., Heusser, A. and Scheuermann, P. (2003), “CATIS: a context-awaretourist information system”, Proceedings of the 4th International Workshop of MobileComputing, Rostock, pp. 263-277.

(The) Protege Ontology Editor and Knowledge Acquisition System (2009), available at: http://protege.stanford.edu/

Quality Support Committee (2003), Quality in Tourism: A Conceptual Framework, QualitySupport Committee, Varadero, May.

Rayn, N. (1999), “ConteXtML: exchanging contextual information between a mobile client andthe FieldNote server”, available at: www.cs.kent.ac.uk/projects/mobicomp/fnc/ConteXtML.html

Suraci, V., Silvano, M. and Massimiliano, T. (2007), “Context-awareness in content discoveryarchitectures”, STreaming Day 2007.

Turner, R.M. (1998), “Context-mediated representation of intillegent agents”, Humman-ComputerStudies, No. 48, pp. 307-330.

Verma, K., Akkiraju, R. and Goodwin, R. (2005), “Semantic matching of web services policies”,paper presented at 2nd International Workshop on Semantic and Dynamic Web Process(SDWP’2005).

Wang, X., Vivtar, T., Kerrigan, M. and Toma, I. (2006), “A QoS-aware selection model forsemantic web services”, Proceedings of 4th International Conference on ServiceOriented Computing (ICSOC’06), Lecture Notes in Computer Science Springer 4294,pp. 390-401.

WSMO (2005), Web Service Modeling Ontology, available at: www.wsmo.org/TR/d2/v1.2/20050413/D2v1-2_20050414.pdf

About the authorsAdel Alti was awarded a PhD degree in Software Engineering from University of Setif (UFAS),Algeria, in 2011. Currently he is an Associate Professor at University of Setif and is a memberof the research group LRSD. His area of interests includes automated software engineering,mapping multimedia concepts into UML, semantic integration of architectural description intoMDA platforms, context-aware quality software architectures and automated servicemanagement and he has published a number of publications concerning these subjects.

Abbdellah Boukerram is an Associate Professor at University of Setif (UFAS), Algeria. Heobtained his PhD degree in Computer Science from university of L. Pasteur, Strasbourg, Francein 1991. He is interested in software parallel architecture, and grid computing. He is the Presidentof the scientific committee of the Computer Science Department (UFAS), where he is supervisingmany Master and PhD students. He is also the head of a research group in the Computer ScienceDepartment.

Philippe Roose is Associate Professor at the University of Pau. He obtained his PhD in 2000and his upper class thesis in 2008. He created the ALCOOL (Software Architecture, Components& Protocols) in 2006. In 2006 and 2010, he obtained a grant from the French National ResearchAgency (ANR). He has supervised five PhD thesis and is currently supervising three. He ischairman of UBIMOB and CFIP/NOTERE 2012. He is also involved in several program/editorcommittees and is member of the ACM. Philippe Roose is the corresponding author and can becontacted at: [email protected]

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ontology and tool support for quality service management

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