qos aware web services recommendations framework

7/30/2019 QoS Aware Web Services Recommendations Framework

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Hybrid technique has been adopted which has

primary objective to overcome the problems of the

combined methods but the important problem

scalability of CF has been left which has a significant

effect on the user profile generation using nearest

neighbor algorithm ; that will ultimately cause poor

QoS prediction while computing similarities using

PCC. The analysis has been focused using a single

domain i.e. book while for the quality prediction it

has to be analyzed on different domain for the better

QoS predictions. The user ranking while merging

the two approaches (CF and CB ) has been

researched to conduct the empirical analysis of this

hybrid approach[16].

In [17] a FA approach has been proposed based on

probabilistic matrix factorization to improve the data

sparsity and poor prediction accuracy problems by

including social contextual information, such as

social networks; in order to improve the data

sparsity problem in traditional recommender

systems, [18] present a novel, efficient, and general

recommendation framework combining a user-item

rating matrix with social contextual information that

apply probabilistic matrix factorization.

Online communities and networked learning

provide teachers with social learning opportunities

to interact and collaborate with others in order to

develop their personal and professional skills. In

[19] Learning Networks are presented as an open

infrastructure to provide teachers with such learning

opportunities. However, with the large number of

learning resources produced every day, teachers

need to find out what are the most suitable resources

for them. Keeping in view this situation

recommender systems are introduced as a potential

solution. Unfortunately, most of the educational

recommender systems cannot make accurate

recommendations due to the sparsity of the

educational datasets. To overcome this problem, a

research approach has to be proposed that describes

how one may take advantage of the social data

which are obtained from monitoring the activities of

teachers.

The international quality standard ISO 8402 (part of the

ISO 9000 (ISO9000 2002)) describes quality as the

totality of features and characteristics of a product or

service that bear on its ability to satisfy stated or impliedneeds. QoS are defined as nonfunctional properties

that affect the quality of services. As a description,

the categories are QoS related to runtime, transaction

support, configuration management and cost and

security.

With the rising occurrence of Web services, studies

on Quality of Service (QoS) have upraised the

concerns of Service-Oriented Computing (SOC)

researchers. A number of QoS-based models have

been functional to the domain of Web service

selection[20], Web service automatic composition [1]

and so on. The Study [21] focused QoS description of

client requirements by extending the WSDL( Web

Service Description Language) by giving a new

prototype E-WSDL to cover up the issue raised in

order to optimize the service composition. Theauthor proposed as future work the extended

support of UDDI to EWSDL, monitoring of EWSDL

and reduction of cost in the service composition.

[22] described a QoS aware selection model for

semantic web services in the domain of automating

the SOA with improving the semantics. The author

highlighted the problem of selection of best web

service according to users requests in case when

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multiple web services are meeting the QoS criteria as

well as users requirements. To accomplish this task

the author has at first specified the QoS ontology and

its vocabulary based on WSMO (Web Service

Modeling Ontology) in order to explain service

descriptions with QoS data. Secondly quality

attributes are measured along with selection modeland at last the results are normalized and analyzed

by using optimum normalized algorithm.

Machine to machine interaction over a network is

supported by interoperable software components

known as web services [23]. the increase of Web

services, Quality of Service (QoS) is usually

measured for unfolding nonfunctional characteristics

of Web services[24].

Few of QoS properties are user independent and

have same values for the different users with the

same functionality (e.g., price, popularity,

availability, etc.). The user-independent QoSproperties are usually presented by service providers

and registered in a repository (e.g., UDDI).

As a consequence of fast evolution in web services

applications and service providers, the users are

facing difficulty in selection of most suitable service

provider challenging the QoS in order to distinguish

service providers. Ranking and Optimization of web

services / Service providers structure are the

interesting areas of research to be given importance

in the recognition of web of services domain. In

ranking the recommendations generated by the users

seek most importance because further it is utilized in

the prediction process. This issue has been called by

the study [25] as fairness issue in recommendationprocess. The inconsistency among the ratings given

by the users can be calculated using spearmans rank

correlation which clearly indicates the degree of

correlation due to change in ranking that is if

applicable by the re-ranking according to the users

satisfaction.

By using the Spearmans rank correlation

where d = difference between two rankings given by

user

And n = no of web services: here in this case n = 10

We get r = 0.84 which indicates that if the re-ranking

of the users ratings is to be performed before the

prediction process then we can increase the fairness

of the prediction quality.

Poor prediction of best web services that falls in the

QoS domain of the semantics an improved QoS

based web Service Compositions Mechanism has

been proposed that is capable to deal with

Handling more numbers of requests, CPU usage,

Execution Time, and Vulnerability Level utilizing

user-based approach and item-based

recommendation approach in order to predict the

QoS values for the current user by using previous

Web Service QoS data from other similar users and

similar Web Services[26].

Predicting and evaluation of QoS values several

methods are utilized mentioned in table 1 used by

different researchers in the client side users [20]





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Ranking and optimization are widely used to

evaluate the semantic quality of automated web

service composition by describing web services,

semantic links among services and

compositions[27]. The Selection of most appropriate

web service among the web services fulfilling the

QoS leads to development of trustworthy QoS ofweb services. To achieve this purpose, extension of

UDDI frame, PAM Clustering and Event-driven

APSM are adopted to collect QoS feedbacks to

reduce the load on prediction server by using T-QoS

Algorithm to filter out the QoS feedback for the

prediction of trustworthy QoS in the domain of

Trust worthy computing approach to avoid the

influence of malicious attacks, failures operations

and false information[28].

After realizing the importance of the re-rating of

the services to enhance the issue of fairness in

recommendation which leads to prediction trust

that falls in the domain of quality of the

recommendation system, we will enhance the

general recommendation process as given in figure

2 [3] by embedding the re-rating process before the

system has to recommend the required services in

order to improve the fairness.

Based on above idea our proposed framework will

be given in figure 3:

In the proposed system and working procedure given

above the processing involves the QoS matching with

the users requirements and then on the basis of these

ranking will be performed to identify the bestmatching web services to be provided to the user. For

this matching we have to use the selection of web

services to be generated according to the requirements

that are specified by the user. When web services are

selected according to users QoS requirements then

composition have to be formed. This applies a

constraint for describing QoS requirements on the web

services that can be selected for the composition and

we refer to this type of constraint as a Q-requirement

constraint[29]. The Q-requirement constraints that are

applied to individual web services of a composition are

referred to as local Q-requirement constraints. TheseQ-requirement constraints can be matched using peer-

to-peer matching techniques, as they only require one-

to one matching. For ranking of web services different

methodologies can be employed for ranking web

service compositions i.e. link analysis techniques as a

modified PageRank algorithm combined with the QoS

data . A service can be ranked high by PageRank

algorithm given in [30] , if it is pointed to by many

Collect User to build Customer

Database

Recommend accordin toEvaluation Recommendation

Results for adjustmentFeedback[1,

2]

Re-rating of users





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other web services or points to many other highly

ranked web services based on their semantic inputs

and outputs. The highly ranked services will be given

an index number to be placed in the index array

generated at run time , after completing this procedure

before the recommendations are to be made the context

information according to published QoS will be storedin a data base . Further it will be utilized in re-rating

process in order to improve the fairness issue for the

better prediction quality . To accomplish this task the

algorithm presented by [31] will be modified according

to the idea of semantic matching with the users

previous rating and the context of the requirements,

specified in query.

The summary of the related work has been given in

table 4 that clearly indicates the importance of

fairness issue in the prediction quality of services

according to users requirements and also the

importance of re-rating pointed out by many

authors. The study [32] has explored the trust issue

and acceptance of the recommendation systems by

using a user adaptive system based on content

based CHIP system , but in this study only

transparency has been given more focus and trust ,the more user adaptive systems are recommended

as future work. [24] emphasized the importance of

impartiality of the QoS evaluation at consumer end

during QoS execution by utilizing the CF approach

giving the solution as service request model for the

specification of consumer side effects and also

emphasized the improvement in efficiency of the

QoS evaluation to be optimized for the users trust

and acceptance.[2] focused the major problem

caused in CF i.e. the Sparsity problem and as

solution described a framework based on subjective

logic in order to remove the sparsity for the

performance improvement. [33] explored the

context aware recommendations based on user

preferences and feedback but has faced the

difficulty for the validation of the real data for the

improvement of the efficiency. Trust based web

personalized recommender system has been

proposed by the [34] focusing major on the user

profiling and cold start problem to achievebreakthrough on the social network

environment.[30] utilized the concept or re-rating in

the movie recommendation system where noise

effect the user ratings so this can be extended

towards different domains by considering the other

factors like semantic matching and user behavior

during the rating process which will definitely

improve the trust over the recommendation systems

by the users.

The need of discovering an approach for reliable

and trustable automatic web service selection

according to users Q-requirements and ranking has

been addressed by many researchers. Until now,

several attempts have been made in this field for

designing techniques and supporting tools to

achieve required objectives such as selecting reliable

services with guaranteed QoS levels based on

collaborative filtering However, offering reliable

models of web service selection, ranking,

verification, and evaluation with considering QoS

attribute values have been mostly studied with

different QoS parameters. In this study we have

presented an idea extracted from the previous

studies that in the collaborative mechanism while

improving the fairness of the predictions the re-

rating by the users has significance and must be

given concentrations as previously focused. In this

proposed our main focus will be on the solving

fairness issue caused by the rating of user according

to their preferences . Currently we are working in

depth on this research to be practically implanted

and then will be analyzed using public dataset to

verify its performance. As future work have plan to

develop prototype for the proposed model so that it

can be implanted practically and be analyzed using

different kinds of publically available datasets.





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I would like to thank Malaysia Ministry of Higher

Education and Universiti Teknologi Malaysia for the

facilities and support in conducting this research study.

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is a PhD student at Faculty of

Computing Universiti Teknologi Malaysia, and also

Assistant Professor at ICIT, Gomal University Dera

Ismail Khan Pakistan. His Research area includes Web

Services; Recommender Systems and collaborative

filtering. He received his MSC from Gomal University.

is a senior lecturer at the

Department of Information Systems, Faculty of

Computing, and Universiti Teknologi Malaysia.

She has been servicing UTM for more than 10

years after a few years experience working as a

system developer in the industry. Her current

research project relates to the development of

ontology based data warehousing and data

mining model for oral cancer research data

repository and improvement of data

mining techniques for risk and survival analysis

of cancer patients.




qos aware web services recommendations framework

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