doi:10.1016/j.tcs.2008.09.019 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Manuscript

Concurrent programming is indispensable. On the one hand, distributed and mobile environments naturally involve concurrency. On the other hand, there is a general trend towards multi-core processors that are capable of running multiple threads in parallel.

Abstract. Data in object-oriented programming is organized in a hierarchy of classes. The problem of object-oriented pattern matching is how to explore this hierarchy from the outside. This usually involves classifying objects by their run-time type, accessing their members, or determining some other characteristic of a group of objects. In this paper we compare six different pattern matching techniques: object-oriented decomposition, visitors, type-tests/type-casts, typecase, case classes, and extractors. The techniques are compared on nine criteria related to conciseness, maintainability and performance. The paper introduces case classes and extractors as two new pattern-matching methods and shows that their combination works well for all of the established criteria. 1

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Recent research on Distributed Event-based Systems (DEBS) has focussed on event correlation, which is the task of processing events to identify meaningful patterns of events in the event cloud. In DEBS, software components communicate by generating, disseminating and receiving event notifications, which reify and describe the event. Several parts of an event notification are context-sensitive, depending on where the software component producing the event is deployed, the communication infrastructure available for event dissemination etc. Event contexts may be added during the production of an event (e.g. by the runtime system executing the component) or during dissemination (by a middleware) and play an integral part in event correlation. Examples of contextual information include physical time, logical time, geographical coordinates, information about the source of events, digital signatures, etc. EventJava [7], an extension of Java with advanced support for event correlation, explicitly integrates the notion of event context, thereby allowing a programmer to customize the way in which events are ordered, propagated and correlated with other events. In this paper, we explain why contexts are indispensable to DEBS, present an overview of EventJava and illustrate the use of contexts through programming examples.