Architecture of remote monitoring and diagnosis system based on service-oriented architecture(SOA)

In view of the increasingly complexity of services logic and functional requirements, a new system architecture based on SOA was proposed for the equipment remote monitoring and diagnosis system. According to the design principles of SOA, different levels and different granularities of services logic and functional requirements for remote monitoring and diagnosis system were divided, and a loosely coupled web services system was built. The design and implementation schedule of core function modules for the proposed architecture were presented. A demo system was used to validate the feasibility of the proposed architecture.

Lessons learned from INTAMAP, an interoperable web service for the real-time interpolation of environmental variables

Interpolated data are an important part of the environmental information exchange as many variables can only be measured at situate discrete sampling locations. Spatial interpolation is a complex operation that has traditionally required expert treatment, making automation a serious challenge. This paper presents a few lessons learnt from INTAMAP, a project that is developing an interoperable web processing service (WPS) for the automatic interpolation of environmental data using advanced geostatistics, adopting a Service Oriented Architecture (SOA). The “rainbow box” approach we followed provides access to the functionality at a whole range of different levels. We show here how the integration of open standards, open source and powerful statistical processing capabilities allows us to automate a complex process while offering users a level of access and control that best suits their requirements. This facilitates benchmarking exercises as well as the regular reporting of environmental information without requiring remote users to have specialized skills in geostatistics.

Dynamic QoS management and optimisation in service-based systems

Service-based systems that are dynamically composed at run time to provide complex, adaptive functionality are currently one of the main development paradigms in software engineering. However, the Quality of Service (QoS) delivered by these systems remains an important concern, and needs to be managed in an equally adaptive and predictable way. To address this need, we introduce a novel, tool-supported framework for the development of adaptive service-based systems called QoSMOS (QoS Management and Optimisation of Service-based systems). QoSMOS can be used to develop service-based systems that achieve their QoS requirements through dynamically adapting to changes in the system state, environment and workload. QoSMOS service-based systems translate high-level QoS requirements specified by their administrators into probabilistic temporal logic formulae, which are then formally and automatically analysed to identify and enforce optimal system configurations. The QoSMOS self-adaptation mechanism can handle reliability- and performance-related QoS requirements, and can be integrated into newly developed solutions or legacy systems. The effectiveness and scalability of the approach are validated using simulations and a set of experiments based on an implementation of an adaptive service-based system for remote medical assistance.

The move to servitization (from a product service system point of view)

Product Service Systems (PSSs) emphasize the substitution of products with services. The term “Servitisation” was introduced by Sandra Vendermerwe in the 80s to represent the addition of services to increase a company’s competitive edge. Key to PSS, and Servitisation more generally, is the “informated product”. The informated product enables health monitoring of the product in use and can be key to a workable PSS. This paper reviews the evolution of servitisation and the associated business benefit. It also then reviews the concept of informated product reconfiguration techniques and remote services that enables PSS to be delivered.

Visualisation of quality information for geospatial and remote sensing data:providing the GIS community with the decision support tools for geospatial dataset quality evaluation

The evaluation of geospatial data quality and trustworthiness presents a major challenge to geospatial data users when making a dataset selection decision. The research presented here therefore focused on defining and developing a GEO label – a decision support mechanism to assist data users in efficient and effective geospatial dataset selection on the basis of quality, trustworthiness and fitness for use. This thesis thus presents six phases of research and development conducted to: (a) identify the informational aspects upon which users rely when assessing geospatial dataset quality and trustworthiness; (2) elicit initial user views on the GEO label role in supporting dataset comparison and selection; (3) evaluate prototype label visualisations; (4) develop a Web service to support GEO label generation; (5) develop a prototype GEO label-based dataset discovery and intercomparison decision support tool; and (6) evaluate the prototype tool in a controlled human-subject study. The results of the studies revealed, and subsequently confirmed, eight geospatial data informational aspects that were considered important by users when evaluating geospatial dataset quality and trustworthiness, namely: producer information, producer comments, lineage information, compliance with standards, quantitative quality information, user feedback, expert reviews, and citations information. Following an iterative user-centred design (UCD) approach, it was established that the GEO label should visually summarise availability and allow interrogation of these key informational aspects. A Web service was developed to support generation of dynamic GEO label representations and integrated into a number of real-world GIS applications. The service was also utilised in the development of the GEO LINC tool – a GEO label-based dataset discovery and intercomparison decision support tool. The results of the final evaluation study indicated that (a) the GEO label effectively communicates the availability of dataset quality and trustworthiness information and (b) GEO LINC successfully facilitates ‘at a glance’ dataset intercomparison and fitness for purpose-based dataset selection.