Service Discovery with Rough Sets

Service Discovery with Rough Sets

Maozhen Li (Brunel University, UK), Man Qi (Canterbury Christ Church University, UK) and Bin Yu (Level E Limited, UK)
DOI: 10.4018/978-1-60566-184-1.ch015
OnDemand PDF Download:


The computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates resource sharing and large-scale problem solving over the Internet. Service discovery becomes an issue of vital importance in utilizing grid facilities. This chapter presents ROSSE, a Rough sets based search engine for grid service discovery. Building on Rough sets theory, ROSSE is novel in its capability to deal with uncertainty of properties when matching services. Services with WSDL interfaces or OWL-S interfaces can be registered with ROSSE and then be discovered.
Chapter Preview


The past few years have witnessed the rapid development of grid computing, a computing paradigm that can be employed to utilise various resources on the Internet. The evolution of grid computing can be divided into the following stages:

  • Parallel computing is targeted at high performance computing using parallel computers of which each has multiple processors. A parallel library such as MPI (Message Passing Interface, can be used to make multiple processors of a supercomputer work together to achieve high performance. Parallel computing environment focuses on high performance and utilize dedicated resources.

  • Cluster computing is a computing paradigm that couples inexpensive personal computers in a LAN to utilise resources. Most cluster computing environments employ a master-slave mode with one master node and multiple working nodes. Compared with parallel computing environments, a cluster is cheap to deploy, and the capacity of resources can increase dynamically. Unlike parallel computing environments, resources in a cluster environment can be non-dedicated, and can be effectively shared. Software technologies such as Condor ( can be used to build a cluster computing environment.

  • Meta-computing is a computing paradigm that can be used to build a large scale computing environment on top of cluster computing environments and parallel computing environments. A meta-computing environment is characterised by coupling heterogeneous resources which may spread across organizational boundaries. Globus ( are two representative middleware technologies for developing meta-computing systems.

  • Grid computing aims to provide a uniform interface for people to utilise various virtualised resources on the Internet for computing on demand. Grid computing is a kind of meta-computing but focuses on large scale computing environments. A number of grid middleware technologies are available including Globus, EGEE (Enabling e-Science in Europe,

Key Terms in this Chapter

Open Grid Services Architecture (OGSA): Promoted by Open Grid Forum and enabled by Web services technologies, OGSA is a standard architecture for next generation service oriented grids.

Parallel Virtual Machine (PVM): PVM is a software system for developing parallel applications. Using PVM, a heterogeneous collection of UNIX and/or Windows systems can work as a single virtual machine.

Service Oriented Architecture (SOA): An architecture to facilitate loose coupling of software components.

Semantic Web: An initiative to augment unstructured Web content as structured information and to improve the efficiency of Web information discovery and machine-readability.

RDF: (Resource Description Framework): A metadata model for describing resources on the Internet.

OWL-S (OWL Web Service Ontology): OWL-S is an OWL-based Web service ontology providing a core set of markup language constructs for describing the properties and capabilities of Web services in unambiguous, computer-interpretable form.

R-GMA (Relational Grid Monitoring Architecture): R-GMA is an implementation of the GMA promoted by Open Grid Forum as a monitoring and information management service for distributed resources.

Web Services: An XML based standard middleware technology for developing interoperable service-oriented distributed systems.

Web Services Resource Framework (WSRF): A set of specifications that models stateful resources with Web services.

MDS (Monitoring and Discovery Service): MDS is a grid information service provided by Globus.

Web Service Modeling Ontology (WSMO): WSMO was developed by Digital Enterprise Research Institute (DERI), a leading European research institute in the field of Semantic Web and Semantic Web services (SWS) technology. It is a set of ontology specifications that provide a conceptual framework and a formal language for semantically describing all relevant aspects of Web services in order to facilitate the automation of discovering, combining and invoking electronic services over the Web. WSMO was submitted to the W3C for consideration in 2005.

Message Passing Interface (MPI): A specification for peer-to-peer communications in a parallel environment.

UDDI (Universal Description, Discovery, and Integration): UDDI is an industry standard for Web services registration and discovery.

OWL (Web Ontology Language): OWL is a W3C recommended language for describing domain ontologies.

Complete Chapter List

Search this Book: