Expert Discovery and Interactions in Mixed Service-Oriented Systems

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.


  • Trust Emergence
  • Personalized Expert Queries
  • Expert Discovery Application

Tools Used:

Front End : ASP.Net with C#
Back End : SQL Server 2005