Regine Meunier

Aspect-Oriented Software Development in Practice: Tales from AOSD-Europe

Aspect-oriented software development techniques provide a means to modularize crosscutting concerns in software systems. A survey of industrial projects reveals the benefits and potential pitfalls of aspectoriented technologies.The past decade has seen the increased use of aspect-oriented software development (AOSD) technique as a means to modularize crosscutting concerns in software systems, thereby improving a development organizations working practices and return on investment (ROI). Numerous industrial-strength aspect-oriented (AO) programming frameworks exist, including AspectJ, JBoss, and Spring, as do various aspect-oriented analysis and design techniques.The software systems using AOSD that we have studied are medium to large-scale and span a wide range of domains including enterprise systems, e-health, e-transport, telecommunications, Web based information systems, multimedia applications, and workflow systems. Our analysis highlights typical usage patterns of AO techniques-for instance, they are mainly used for modularizing well-known crosscutting problems and incrementally introduced, addressing developmental concerns and other noncore product features first.

Service workflow monitoring through complex event processing

This paper presents an approach for service monitoring through workflow modeling and complex event processing. Workflows allow the representation of services process interactions while complex event processing (CEP) is a concept for event driven architectures which offers an alternative solution for monitoring and supervision. In this paper we propose a methodology to combine both technologies where CEP is used to monitor workflows and to predict possible delays. A case study on medical equipment maintenance business is shown.

An aspect-oriented implementation of the EJB3.0 persistence concept

This paper demonstrates the power of aspect-orientation by implementation the EJB3.0 persistence framework. Our approach has advantages over existing mapping tools: Flexibility is higher as the functionality can be freely implemented and extended to users needs.

Learning Diagnostic Rules for Power Distribution Systems

This paper discusses the machine generation of diagnostic rules for fault diagnosis in power distribution systems. The Machine Learning technique that we have implemented processes examples of fault events with the associated diagnoses (from records of previous errors), and derives rules that correctly classify the available examples. In order to formalize relevant domain knowledge and to build adequate diagnostic rules, first order concepts had to be introduced. The paper describes the existing prototype RUDI (Learning Rules for Diagnosis) and the initial results of the test phase.