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Research_

Service computing

Towards a better understanding of complex service systems

An inherently interdisciplinary endeavour that focuses on the co-creation of value between customers and service providers in the context of the design, solution development, delivery and management of the service.

Our research

The service sector, broadly defined, accounts for more than 60 percent of the gross domestic product (GDP) and employment in the world’s developed economies, including Australia. Even in emerging nations such as China and India, the size of the service economy is approaching 30–40 percent. Information and communications technologies (ICT) play a central role in the engineering and management of the service systems that underpin the service economy.

Service computing refers to the scientific and technical body of knowledge that is emerging to enhance our understanding of complex service systems that integrate business and technical concerns. This is an inherently inter-disciplinary endeavour that focuses on the co-creation of value between customers and service providers in the context of the design, solution development, delivery, and management of the service. While the core technology suites for service computing include service-oriented architecture, web services, cloud computing, business process modelling and integration and business intelligence, there is a need to integrate key knowledge components from management and behavioural sciences, information systems as well as marketing and customer relationship management.

Key projects

Our experts: Professor Joseph Davis

Industry Partner: Data61

This project will explore the potential for integrating multiple ontologies to create a knowledge graph as a lightweight knowledge representation scheme to represent significant parts of an organisation’s network knowledge (network topology in particular). This will enable a range of semantic applications, which include reasoning about network topologies to resolve data inconsistencies and conflicts, performing extended semantic search and classifying objects, among others.

Our experts: Professor Joseph Davis

This project involves a systematic approach to detecting anomalous citation practices (citation stacking, cartels) using social network analysis of citation network data obtained from the Web of Science.  The approach is based on analysis of egocentric citation and co-author networks to first identify outliers in terms of anomalous patterns, and drilling down to detect citation cliques and cartel-like patterns as existence proof of such practices.

Our experts: Professor Joseph Davis

This project aims to develop new computational approaches to the recommendation problem using linked open data (LOD) and collaborative tagging systems data.

Our experts: Professor Albert Zomaya, Dr Mohammadreza Hoseinyfarahabady

Myriad applications today perform sophisticated forms of micro-services. There are several platforms that provide the virtualised infrastructure required such software architecture – one of the most recent is the virtualised Lambda platform. Enterprises can exploit Lambda platforms (for example, AWS Lambda) to extend other services with custom logic, or create their own back-end micro-services that operate at cloud scale, performance and security. A Lambda platform is a form of server-less computing service that can run the client's code in response to external events and manage the underlying compute resources automatically.

In this project our aim is to propose an effective solution based on Optimal Controller theory. We will use the promising technologies offered by Lambda platform to handle a burst of events coming to a Lambda cluster and reach an optimal trade-off between the server's utilisation and the amount of quality of service enforced by each user.