Expert Discovery and Interactions in Mixed Service-Oriented Systems(2012)

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Web-based collaborations and processes have become essential in today’s business environments. Such processes typically span interactions between people and services across globally distributed companies. Web services and SOA are the de-facto technology to implement compositions of humans and services. The increasing complexity of compositions and the distribution of people and services require adaptive and context-aware interaction models. To support complex interaction scenarios, we introduce a mixed service-oriented system composed of both human-provided and software-based services interacting to perform joint activities or to solve emerging problems. However, competencies of people evolve over time, thereby requiring approaches for the automated management of actor skills, reputation, and trust. Discovering the right actor in mixed service-oriented systems is challenging due to scale and temporary nature of collaborations. We present a novel approach addressing the need for flexible involvement of experts and knowledge workers in distributed collaborations. We argue that the automated inference of trust between members is a key factor for successful collaborations. Instead of following a security perspective on trust, we focus on dynamic trust in collaborative networks. We discuss Human-Provided Services (HPS) and an approach for managing user preferences and network structures. HPS allows experts to offer their skills and capabilities as services that can be requested on demand. Our main contributions center around a context-sensitive trust-based algorithm called ExpertHITS inspired by the concept of hubs and authorities in Web-based environments. ExpertHITS takes trust-relations and link properties in social networks into account to estimate the reputation of users.

Existing System

The process model may be composed of single tasks assigned to responsible persons, describing the steps needed to produce a software module. After finishing a common requirements analysis, an engineer evaluates the re-usability of existing work, while a software architect designs the framework.

Existing approaches in personalized expertise mining algorithm typically perform offline interaction analysis.

Proposed System

Here we propose the Expert Web consisting of connected experts that provide help and support in a service oriented manner. Examples are crowd-sourcing applications in enterprise environments or open Internet based platforms. These online platforms distribute problem solving tasks among a group of humans. The members of the Expert Web are either humans, such as company employees offering help as online support services or can in some cases be provided as software-based services. Applied to enterprise scenarios, such a network of experts, spanning various organizational units, can be consulted for efficient discovery of available support. The expert seekers, for example the software engineers or architect in our use case, send requests for support, abbreviated as RFSs. Experts may also delegate RFSs to other experts in the network, for example when they are overloaded or not able to provide satisfying responses. Following this way, not only users of the expert network establish trust in experts, but also trust relations between experts emerge.

In our future work, we will study network effects of two-sided markets in ‘mixed service-oriented’ systems. Also, we plan to make the system available for public use.


1.     Solve emergent problems in distributed collaboration environments

2.     Trust relations

3.     Expert-HITS is calculated online


1.     Trust Emergence

Traditional rating and ranking models usually neglect social aspects and individual preferences. However, actors in the Expert Web may not be compatible with respect to working style and behavior. As a consequence, social aspects need to be considered and require dynamic interaction models. In this paper, we focus on social trust to support and guide delegations of requests. In contrast to a common security perspective, social trust refers to the flexible interpretation of previous collaboration behavior and the similarity of dynamically adapting interests.

2.     Personalized Expert Queries

We define this concept as expert hubs that are well-connected (i.e., social network structure and connections based on joint collaborations) given a particular query context. Delegation is important in flexible, interaction-based systems because expert hubs will attract many RFSs over time, thus presenting bottlenecks in terms of processing or delegating RFSs. On the other side, being a hub in the Expert Web also means that a person knows many other experts in similar fields of interest. We argue that the likelihood of being able to delegate RFSs to other experts greatly increases depending on the hubness of a person arising from being a member in expert areas (e.g., communities or interest groups). The major challenge in this scenario is that hubness needs to be calculated on demand based on a given query. A query determines the context specified as the set of relevant skills.

3.     Expert Discovery Application

1)   The expert seeker specifies a set of demanded skills (Fig (a)) using dropdown lists. For simplicity, we do not visualize selection options for matching preferences.

2)    A list of experts is retrieved matching the search criteria (Fig. (b)). the set of expert skills are visualized. Additionally, the experts’ profile can be retrieved. Such profile information is typically available via public Web sites containing information about collaborators, joint projects or scientific papers published by an expert.

3)   the expert seeker enters information regarding the RFS (simplified form for brevity). Upon submission, form elements are translated into XML and SOAP messages.

System Requirements:

Hardware Requirements:

•         System                : Pentium IV 2.4 GHz.

•         Hard Disk         : 40 GB.

•         Floppy Drive      : 1.44 Mb.

•         Monitor               : 15 VGA Colour.

•         Mouse                 : Logitech.

•         Ram                     : 512 Mb.

Software Requirements:

•         Operating system         : Windows XP.

•         Coding Language         : ASP.Net with C#

•         Data Base                    : SQL Server 2005     

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