International Journal of Computer Trends and Technology (IJCTT) – volume 4 Issue10 – Oct 2013

ISSN: 2231-2803 http://www.ijcttjournal.org Page3386

Trust Based Integration of Human Provided Services and Service Oriented Systems

Rajendran Satish#1, K.Praveen Kumar*2 #1M.Tech, Computer Science Engineering, MLRIT, Hyderabad, Andhra Pradesh, India #Assiatant Professor, Department of CSE, MLRIT, Hyderabad, Andhra Pradesh, India

ABSTRACT – As due to the incredible development of distributed technologies based on the SOA (Service Oriented Architecture) business process integration has turn out to be a reality. Specifically web services technology that supports collaboration of heterogeneous systems and as such systems are multifaceted in nature and a mixed service oriented systems that consist of services and also the human beings can be assumed for its importance. Some such mixed service oriented systems was proposed by Danial Schall et al. Their system involves in the two different types of users who realizes the benefits of the system. Each of this side in the market becomes its value based on the present on the other side. During evaluation and analysis of two-sided markets in such distributed, heterogeneous systems which reveals the stability of the two-sided markets and various kinds of coalitions present in such markets.

1. INTRODUCTION

Web services and SOA helps us in realizations of mixed service oriented systems. More over the addition of web services into a Business Process Execution Language (BPEL) process that leverages automation of execution flow of the services [1]. Though distributed applications work well, there are certain areas where human beings’ presence is crucial. Human Provided Services (HPS) [2] which promotes the flexibility in the mixed service oriented systems. We consider a mixed service oriented systems proposed by Danial Schall (2012) which is the combinations of the Software Based Services (SBS) and the HPS. ExpertHits has been introduced by Denial Schall [1] that combines the HPS and the SBS. The distributed networking application in ExpertHITS has an application which has two types of users. Software engineer is a user who seeks the required help from another kind of user namely as an expert. Experts are human beings that have adequate knowledge in various subjects of Information Technology. The software engineers get benefited from the presence of these experts that are always associated with the networks, while the experts get benighted by the distributed web based enterprise level application. A trust model has been used in the ExpertHITS, as it helps in determining the right expert for the given query [3], [4], [5] presented to them.

Additional to this mode, the ExpertHITS is the system on which these concepts of hubs and authorities can be used. Scores are provided for hubs and also authorities. Hubs are the expert who knows

the other experts and delegates the query given by its user. In the same manner, authority is an expert who really solves the given problem. As skill models and a trust models are used in this system. They are used to improve effectiveness of this system. The system which is having web services and also human beings working together are also having two-sided markets. Two-sided markets come into existence where there are two kinds of users who are involved in the system [6]. In the ExpertHITS system, the two users representing the other two networks they are software engineer (user) and the other is expert [7]. Evolution of such two-sided markets can bring about facts relating to their relationships and also the gaining of remunerations due to the presence of other side over network. Therefore we recommend improvement of ExpertHITS that guarantees the evaluation of two-sided markets. The proposed system also delivers a new algorithm known as “MarketEvaluation” algorithm. This algorithm is intended for evaluating two-sided markets which are present in the distributed system discussed above. Its discovery includes calculation of both same side and cross side effects when the number of users are increased or decreased in this system. The rest of the paper labels its related work and also various putting into practice information.

2. RELATED WORK

A service oriented system is not only used in web services but also in collaborations of the human beings. There has been study going on the standards that govern the mixed service oriented systems. Such standards are WS-HT [8] and Bpel4People [9] that

International Journal of Computer Trends and Technology (IJCTT) – volume 4 Issue10 – Oct 2013

ISSN: 2231-2803 http://www.ijcttjournal.org Page3387

were established in order to address the various necessity for all human interactions in mixed service built systems [10]. The aim of the mixed service oriented systems is to make the distributed system flexible, robust and a model in the real world resolution [11]. The presentations that run in excess of web allow all users to share their views and also their experiences that are known as task-based platforms [12]. Thus users can help others of same benevolent of different kind completely [13]. Expertise of users was known by HITS [14] and the Page Rank [15] in order to predict the expertise of the users as a whole. In business collaborations the trust always plays a crucial role. Recently [16] [17] [18] introduced trust management frameworks in the systems that uses SOA. Though plenty of researched articles came into presence from the last many years and addressing of essential research questions was not provoked. As the proposed system involves both the mixed service oriented systems with mechanisms for evaluating two-sided markets.

Rochet and Tirole [19] did their research on the economic study of two-sided markets. Later Weyl [20] had *extended this work. Between these platforms and suppliers a game was presented by Lee [21] in the form of two-sided markets. The research revealed that these competitions can lead to sub-optimal steadiness. Vertical integration of the market shares is added researches that are done in the similar lines [10]*. When new user is added to an existing network some of the values get added to network users, this is termed as “network effect”. Robert Metcalfe [11]* in 1995 introduced a concept that is with the number of consumers, the product pricing is amplified. Tim Berners Lee, the author of WWW also specified that these structured data on the web will be generated a lot of new values. In [22], [23] preferential attachment was described in social network and also in [22]* social networking technology is clearly described. The proposed system is a mixed service-oriented system always consists of web based application which can be accessed in various kinds of web services and also in the implementation of these two-sided markets and in the estimation of the matches found.

3. PROPOSED MODEL

3.1 Two-Sided Markets

A two-sided market arises when two dissimilar types of users may recognize advances by interrelating with one another through one or more platforms or mediators available.

A two-sided market is a method in which the connections between three types of agents: users are of the first type (software developers or questioners), users of the second type (authorities or answerers) and intermediaries (hubs or delegators)

Cross-side network effects: If a original software developer (blue user) joins some other platform, then it makes that platform more attractive for the expert users, and vice-versa.

Same-side network effect: If the authority (green users) users grow in numbers, then it can add its value to the platform through shares its reviews or through sharing its access tools available. Note: However that in some of the cases there might be a negative same-side network effect: If more such users are in a platform which has a value which is higher in competitions.

3.2 Market State and Evaluation

When the two-sided markets have the three types of users and also have the two types of users. Then interactions are always between the two types of users. As per the above descriptions of the terms, the market state at any given time “t” can be computed using the resulting equation:

M (t) = (B1(t), …, Bk(t), G1(t), …, Gk(t)

And the following formula can be used to evaluation.

M (t + 1) = F (M (t)) where F is a stochastic function

The principle of the algorithm is to described in the subsequent listing:

Listing 1: This shows Market Evaluation Algorithm

As this can be seen in the above listing and it is evident that the same side market value gradually

International Journal of Computer Trends and Technology (IJCTT) – volume 4 Issue10 – Oct 2013

ISSN: 2231-2803 http://www.ijcttjournal.org Page3388

decreases whenever a new user is added in the system and the cross side benefits are also increased gradually. When an existing user is removed from the system then the same-sided effect is positive while the cross-sided effect is negative. In the same manner, the benefits of the same side are decreased when new experts are added to the network and cross side effect is increased. When an expert is removed from the network, then the same-sided effect is positive while the cross-sided effect is negative.

4. IMPLEMENTATION

The implementation environment in a Personal Computer is by using Windows 7 as OS and NetBeans as Integrated Development Environment (IDE). JDK 1.6 is the Java Standard Edition (JSE) and Oracle 10G used as its backend. The front end is built using Servlets and the JSP pages while services are implemented using the web services technology. The web application provides the web user interface while the servlets in turn interacts with these services. The architecture used in a distributed system is n-tier architecture. The implemented system has about two users. They are: User and Admin are the two types. User epitomizes a software engineer who can give queries affecting to their effort to their experts. Expert discovery is prepared before an expert is nominated. It also makes use of various skills which are matching algorithms to match its abilities.

5. EMPIRICAL RESULTS

The outcomes of these two-sided market effects are presented graphically in this segment. The graphs are the result of the various experiments made on the conscious enterprise application. Various operations that can be provided are adding new user (or) deleting an existing user (or) adding new expert (or) deleting new expert. From the user perspective, supposing that expert count remains the same for some time, and then the results of various experiments are shown in figure 1.

Fig. 1: Shows two-sided markets evaluation from users’ perception

This can be seen in the above figure, as user tally increases, same side effect is reduced and the cross side effect gets increased. While making these experiments a value such as weight is measured for each of its network. The weight is handled when a new user is added or removed. In same of the cases happens with experts. The following figure illustrations network effects from experts’ perspective.

Fig. 2: Shows two-sided markets evaluation from experts’ perspective

This can be seen in the above figure, as expert counts increases, same side effect gets reduced and the cross side effect gets increased. While making experiments a value such as the weight is considered for each of the network. The weight is manipulated whenever expert user is added or removed. Same case this also happens with experts.

6. CONCLUSION

Service Oriented System with the Human interactions (Mixed Service Oriented Systems) becomes common in the distributed computing environment. The aim of this assignment is to evaluate two-sided markets. Though, ExpertHITS has been enhanced for this resolution. It already has the users and the experts in an inventiveness application. All the users queries will be answered by experts and also by some software services which are provided. This mixture of human interaction and the software services make this project a very springy. When the two-sided market evaluation has been done on the enterprise application, the results reveal that the network benefits are always affected by the presence of cross side users which are present. The possible future effort for this task is to study the stability of market evaluation under a number of models of clatter.

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International Journal of Computer Trends and Technology (IJCTT) – volume 4 Issue10 – Oct 2013

ISSN: 2231-2803 http://www.ijcttjournal.org Page3389

1. REFERENCES

[1] From A. Agrawal et al., “WS-BPEL Extension for People (BPEL4People), Version 1.0.” 2007. [2] D. Schall, “Human interactions in mixed systems - architecture,protocols, and algorithms,” Ph.D. dissertation, Vienna University of Technology, 2009 [3] OASIS, “Business Process Execution Language for Web Services,Version 2.0.” 2007. [4] D. Artz and Y. Gil, “A survey of trust in computer science and the semantic web.” J. Web Sem., vol. 5, no. 2, pp. 58–71, 2007. [5] J. Golbeck, Computing with Social Trust (Human-Computer Interaction Series), 1st ed. Springer, December 2008 [6] J. Yang, L. Adamic, and M. Ackerman, “Competing to share expertise: the taskcn knowledge sharing community,” in International Conference on Weblogs and Social Media, 2008. [7] J. M. Kleinberg, “Authoritative sources in a hyperlinked environment,”J. ACM, vol. 46, no. 5, pp. 604–632, 1999 [8] A. Agrawal et al., “Web Services Human Task (WS-HumanTask),Version 1.0.” 2007. [9] F. Leymann, “Workflow-based coordination and cooperation in a service world,” On the Move to Meaningful Internet Systems 2006:CoopIS, DOA, GADA, and ODBASE, pp. 2–16, 2006. [10] F. Skopik, D. Schall, and S. Dustdar, “Modeling and mining of dynamic trust in complex service-oriented systems,” Elsevier Informat. Systems, 2010. [11] P. Jurczyk and E. Agichtein, “Discovering authorities in question answer communities by using link analysis,” in CIKM ’07. New York, NY, USA: ACM, 2007, pp. 919–922 [12] L. Page, S. Brin, R. Motwani, and T. Winograd, “The PageRank Citation Ranking: Bringing Order to the Web,” Stanford Digital Library Technologies Project, Tech. Rep., [13] D. Kovac and D. Trcek, “Qualitative trust modeling in soa,”Journal of Systems Architecture, vol. 55, no. 4, pp. 255–263, 2009. [14] Ravi Kumar et al. Evolution of Two –Sided Markets. [15] A. L. Barabasi and R. Albert. Emergence of scaling in random networks. Science, 286:509{512, 1999. [16] N. Immorlica, J. Kleinberg, M. Mahdian, and T. Wexler. The role of compatibility in the di_usion of technologies through social networks. In Proc. 8th EC,pages 75{83, 2007. [17] R. S. Lee. Competing platforms, 2008. Working paper. [18] R. S. Lee. Vertical integration and exclusivity in platform and two-sided markets, 2009. Working paper. [19] R. Metcalfe. Metcalfe's law: A network becomes more valuable as it reaches more users. Infoworld, 1995. [20] J.-C. Rochet and J. Tirole. Platform competition in two-sided markets. Journal of the European Economic Association, 1(4):990{1029, 2003. [21] E. G. Weyl. The price theory of two-sided markets,2009. Working paper. 319 [22] Z. Malik and A. Bouguettaya, “Reputation bootstrapping for trust establishment among web services,” IEEE Internet Computing,vol. 13, no. 1, pp. 40–47, 2009.

Authors

Rajendran Satish, he is pursuing M.Tech (CSE) MLRIT, Hyderabad, AP, INDIA. He has received B.Tech Computer Science and Engineering in the year 2011. His main research interest includes ethical hacking, Web designing and Databases.

K. Praveen Kumar, M.Tech CSE. He is currently with the Department of Computer Science and Engineering, MLRIT, Andhra Pradesh, India. He is having 7 years of teaching experience. He has received M.Tech Computer Science Engineering degree in the year 2009. He has completed B.Tech Computer Science Engineering in the year 2005.