TY - GEN
T1 - Big log analysis for E-learning ecosystem
AU - Zheng, Qinghua
AU - He, Huan
AU - Ma, Tian
AU - Xue, Ni
AU - Li, Bing
AU - Dong, Bo
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/10
Y1 - 2014/12/10
N2 - 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.
AB - 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.
KW - characteristics of e-Learning log data and log analysis
KW - e-Learning ecosystem
KW - e-Learning log data
KW - logging architecture
UR - https://www.scopus.com/pages/publications/84920744335
U2 - 10.1109/ICEBE.2014.51
DO - 10.1109/ICEBE.2014.51
M3 - 会议稿件
AN - SCOPUS:84920744335
T3 - Proceedings - 11th IEEE International Conference on E-Business Engineering, ICEBE 2014 - Including 10th Workshop on Service-Oriented Applications, Integration and Collaboration, SOAIC 2014 and 1st Workshop on E-Commerce Engineering, ECE 2014
SP - 258
EP - 263
BT - Proceedings - 11th IEEE International Conference on E-Business Engineering, ICEBE 2014 - Including 10th Workshop on Service-Oriented Applications, Integration and Collaboration, SOAIC 2014 and 1st Workshop on E-Commerce Engineering, ECE 2014
A2 - Li, Yinsheng
A2 - Fei, Xiang
A2 - Chao, Kuo-Ming
A2 - Chung, Jen-Yao
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE International Conference on E-Business Engineering, ICEBE 2014
Y2 - 5 November 2014 through 7 November 2014
ER -