Programming models for concurrency are optimized for dealing with nondeterminism, for example to handle asynchronously arriving events. To shield the developer from data race errors effectively, such models may prevent shared access to data altogether. However, this restriction also makes them unsuitable for applications that require data parallelism. We present a library-based approach for permitting parallel access to arrays while preserving the safety guarantees of the original model. When applied to SCOOP, an object-oriented concurrency model, the approach exhibits a negligible performance overhead compared to ordinary threaded implementations of two parallel benchmark programs.
The concept of views is an application of readers-writers locks first introduced by Courtois, Heymans and Parnas @cite_8 , tailored to the concept of slices.
@cite_8
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A viewpoint is a triple consisting of an entity, a topic related to this entity and sentiment towards this topic. In time-aware multi-viewpoint summarization one monitors viewpoints for a running topic and selects a small set of informative documents. In this paper, we focus on time-aware multi-viewpoint summarization of multilingual social text streams. Viewpoint drift, ambiguous entities and multilingual text make this a challenging task. Our approach includes three core ingredients: dynamic viewpoint modeling, cross-language viewpoint alignment, and, finally, multi-viewpoint summarization. Specifically, we propose a dynamic latent factor model to explicitly characterize a set of viewpoints through which entities, topics and sentiment labels during a time interval are derived jointly; we connect viewpoints in different languages by using an entity-based semantic similarity measure; and we employ an update viewpoint summarization strategy to generate a time-aware summary to reflect viewpoints. Experiments conducted on a real-world dataset demonstrate the effectiveness of our proposed method for time-aware multi-viewpoint summarization of multilingual social text streams.