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首页> 外文会议>2014 IEEE 11th International conference on e-business engineering >Big Log Analysis for E-Learning Ecosystem
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Big Log Analysis for E-Learning Ecosystem

机译：电子学习生态系统的大日志分析

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摘要

Recently, e-Learning emerges a rapid development, and e-Learning ecosystem has been proposed to provide sustained and stable services to cope with the growing demands of e-Learning. In e-Learning ecosystem, the amount of e-Learning log grows exponentially, introducing the variety and complexity of e-Learning log analysis. Therefore, a robust, scalable and practical logging architecture is urgently needed. Firstly, the characteristics of e-Learning log and its analysis are studied in this paper. Specifically, e-Learning log implies complicated characteristics, such as multi-dimensional correlation, heterogeneous multi-source, and cascading generation. Furthermore, the log analysis represents abundant diversity of demands, variety of methods and low-latency requirement in computation. Thereupon, this study presents a comprehensive logging architecture covering the whole life cycle of e-Learning log data which includes log collection, transport, storage, computation and service. To verify the proposed logging architecture, a related experimental implementation is developed for a realistic e-Learning ecosystem, and three typical e-Learning analyses are proposed.
机译：最近，电子学习迅速发展，已经提出了电子学习生态系统，以提供持续稳定的服务来应对不断增长的电子学习需求。在电子学习生态系统中，电子学习日志的数量呈指数增长，从而引入了电子学习日志分析的多样性和复杂性。因此，迫切需要一个健壮，可扩展且实用的日志记录体系结构。首先，研究了电子学习日志的特点及其分析。具体而言，电子学习日志包含复杂的特征，例如多维相关性，异构多源和级联生成。此外，对数分析代表了需求的丰富多样性，方法的多样性和计算的低延迟需求。随即，本研究提出了一个全面的日志记录体系结构，涵盖了电子学习日志数据的整个生命周期，其中包括日志收集，传输，存储，计算和服务。为了验证提议的日志记录体系结构，针对实际的电子学习生态系统开发了相关的实验实现，并提出了三种典型的电子学习分析。

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