Web Service Composition
Service-oriented computing promises rapid development of software application. New services can be developed by combining many interoperating, autonomous, distributed, reusable services on the Internet. Emergency earthquake services, for example, may be composed of multiple services: Google maps to show the location of patients; a hospital service showing available spaces at hospitals; and a traffic system outlining current road conditions. Someone could then use such a system to allocate patients to the best hospital within the shortest time. The fundamental challenge is to compute new value-added services by selecting and combining existing services that not only satisfy users’ functional requirements but also meet non-functional expectations, e.g., lowest price, lowest response time, and available and reliable all the time.
In the era of Big Data, the number and complexity of data-intensive services on the Internet is increasing rapidly. Traditional service composition approaches have come to a performance bottleneck, due the mass data. Data-intensive services bring new challenges because composition with data-intensive services across multiple network nodes must consider the costs of dependency checking, consistency enforcement, data transfer and storage. We will investigate the optimisation problems of data-intensive service composition and develop heuristic algorithms for solving them.
Service Location Allocation
Service-oriented computing has emerged as a new way of developing software. For example a travel booking system can be composed by a flight booking service, a hotel booking service and an online payment service. As service providers compete for service users, they try to minimize the costs of service provisioning while meeting the required quality levels. Distributing data-intensive services across multiple network nodes can improve quality, but one must consider the costs of dependency checking, consistency enforcement, data transfer and storage. We will investigate the optimisation problems of data-intensive service distribution design and develop heuristics for solving them.
Translation of a Geographic Information System (GIS) Conceptual Model to the Relational Data Model
Successful implementation of geographic applications starts with conceptual design. A conceptual schema will then be transformed into a database schema that can be implemented. Geography Markup Language (GML) has emerged as an open standard that provides a common grammar for coding geo-spatial content and exchanging over the Internet. In this project we study the transformation from Geometrically enhanced ER model (GERM) to GML. GERM is an extension of the classical ER model that has been successfully used for conceptual modelling of geographic applications. We will design some transformation rules such that relevant application semantics is preserved during the transformation. We will then design an bottom-up algorithm for transforming GERM schemas into their GML counterparts.