Abstract. This paper addresses the topic of Fair QoE measurements in networking. The research of new solutions in networking is oriented to improve the user experience. Any application or service can be improved and the deployment of new solutions is mandatory to get the user satisfaction. However, different solutions exist; thus, it is necessary to select the most suitable ones. Nevertheless, this selection is difficult to make since the QoE is subjective and the comparison among different technologies is not trivial. The aim of this paper is to give an overview on how to perform fair QoE measurements to facilitate the study and research of new networking solutions and paradigms. However, previously to address this problem, an overview about how networking affects to the QoE is provided.
a b s t r a c tNetwork reliability and resilience has become a key design parameter for network operators and Internet ∧ service providers. These often seek ways to have their networks fully operational for at least 99.999% of the time, regardless of the number and ∧ type of failures that may occur in their networks.This article presents a ∧ continuous-time Markov chain model to characterise the propagation of failures in optical GMPLS rings. Two types of failures are considered depending on whether they affect only the control plane, or both the control and data planes of the node. Additionally, it is assumed that control failures propagate along the ring infecting neighbouring nodes, as stated by the Susceptible-Infected-Disabled (SID) propagation model taken from epidemic-based propagation models. A few numerical examples are performed to demonstrate that the CTMC model provides a set of guidelines for selecting the appropriate repair rates in order to attain specific availability requirements, both in the control plane and the data plane.
The paper shows that a mode similar to the TMo, in circular geonietries can be also excited in non circtilar patches, short circiiited,in the center: the rectangular geometiy has been chosen /or it. Most oJthe interesting characteristics and excellent behaviour of the TM,, short circuited ring, mainly irsefiil j a r niobile user teriiiiiia1.s. have been also found in the proposedgeomerry.
Educational data mining (EDM) combines the techniques of data mining with educational data in order to provide students, instructors, and researchers with knowledge that can benefit academic processes. Due to globalization, foreign language learning (FLL) has become increasingly important. This work seeks to gain insight as to how data mining (DM) is being used to benefit FLL. For this purpose, an advanced review of pertinent research published from 2012 to 2017 was performed. After applying our screening method, 208 papers were selected for the exhaustive analysis. This analysis was divided into four aspects: context (educational environments, educational level), number of items, DM methods, and DM applications. The results indicated that 54% of studies were conducted in traditional environments, while only 3% of studies were performed in an m‐learning environment. In addition, 25 and 72% of the research was conducted in either a primary or secondary level, or in tertiary or adult level, respectively. Likewise, 76% of studies contained datasets of less than 1,000 items. The most utilized EDM methods were: factor analysis, regression, text mining, correlation mining, and causal DM. In addition, the studies analyzed showed that DM is mainly employed to predict the performance of students, to check learners' motivation, and to provide feedback for instructors. These results seem to indicate that although DM has much to offer the increasing number of language students, it is not being used to its full potential.
This article is categorized under:
Application Areas > Education and Learning
Fundamental Concepts of Data and Knowledge > Data Concepts
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