Let's Dance: A Language for Service Behavior Modeling

In Service-Oriented Architectures (SOAs), software systems are decomposed into independent units, namely services, that interact with one another through message exchanges. To promote reuse and evolvability, these interactions are explicitly described right from the early phases of the development lifecycle. Up to now, emphasis has been placed on capturing structural aspects of service interactions. Gradually though, the description of behavioral dependencies between service interactions is gaining increasing attention as a means to push forward the SOA vision. This paper deals with the description of these behavioral dependencies during the analysis and design phases. The paper outlines a set of requirements that a language for modeling service interactions at this level should fulfill, and proposes a language whose design is driven by these requirements.

IT service desk process improvement : a narrative style case study

This paper is a detailed case narrative on how a Faculty of a leading Australian University conducted a rigorous process improvement project, applying fundamental Business Process Management (BPM) concepts. The key goal was to increase the efficiency of the faculty’s service desk. The decrease of available funds due to reducing student numbers and the ever increasing costs associated with service desk prompted this project. The outcomes of the project presented a set of recommendations which leads to organizational innovation having information technology as an enabler for change. The target audience includes general BPM practitioners or academics who are interested in BPM related case studies, and specific organisations who might be interested in conducting BPM within their service desk processes.

Data mining for lifetime prediction of metallic components

The ability to accurately predict the lifetime of building components is crucial to optimizing building design, material selection and scheduling of required maintenance. This paper discusses a number of possible data mining methods that can be applied to do the lifetime prediction of metallic components and how different sources of service life information could be integrated to form the basis of the lifetime prediction model