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In this article, we present a novel algorithmic method for the calculation of thresholds for a metric set. To this aim, machine learning and data mining techniques are utilized. We define a data-driven methodology that can be used for efficiency optimization of existing metric sets, for the simplification of complex classification models, and for the calculation of thresholds for a metric set in an environment where no metric set yet exists. The methodology is independent of the metric set and therefore also independent of any language, paradigm or abstraction level. In four case studies performed on large-scale open-source software metric sets for C functions, C++, C# methods and Java classes are optimized and the methodology is validated.

Carrier-grade networks of the future are currently being standardized and designed under the umbrella name of Next Generation Network (NGN). The goal of NGN is to provide a more flexible network infrastructure that supports not just data and voice traffic routing, but also higher level services and interfaces for thirdparty enhancements. Within this paper, opportunities to integrate grid and cloud computing

Preliminary analysis showed that users did not stick to the intended forum behavior of discussing exactly one topic in one thread. Instead, they deviated from the original topic over time, sometimes coming back to the original topic. In short, any topic could appear in any thread. Thus, we needed a way to decide for every individual post, whether it was relevant or not. The simplest approach is to scan every post for a set of predefined keywords. However, we assumed that the context of a post also plays a role when determining the relevance of a post. We thus defined an Information Retrieval algorithm, that extends the keyword-based approach by also taking structural (contextual) information of posts into account. (The following is mostly taken from [5].) Reasoning behind the new AlgorithmApproaches based on linguistic features alone can not be expected to perform well. This is because:

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