TY - GEN
T1 - Emotion-based social computing platform for streaming big-data
T2 - 13th International Conference on Service Systems and Service Management, ICSSSM 2016
AU - Zhang, Leihan
AU - Zhao, Jichang
AU - Xu, Ke
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/9
Y1 - 2016/8/9
N2 - Exploration of user generated content in the epoch of Web 2.0 brings unprecedented challenge to the social computing, which has to provide real-Time solution in the circumstance of massive data volumes and evolving application scenarios. This paper presents an emotion-based social computing platform namely ESC for streaming big-data. The main aim of ESC is to provide sentiment analysis as the foundation of social computing and enable both real-Time computation on streaming big-data and batch computation on off-line big-data with high performance and low risk. Different from conventional data processing technologies, ESC is designed as a scalable and QoS-optimized adaptive platform for developers to only focus on business models instead of being distracted by details of the computing infrastructure. In addition, continuous streaming computing is emphasized in ESC to keep tracking on long term dynamic evolution in social media, which can provide a valuable proxy for in-depth social analytics. The architecture of ESC is implemented by distributed storage, sentiment analysis, data parallelism and routing, real-Time streaming computation, batch computation and distributed machine learning. And the evaluation results from real-Time and batch computations testify the high performance and scalability of ESC. Moreover, a few applications based on it further demonstrates its usability in enacting on different streaming big-data and variety of social computations.
AB - Exploration of user generated content in the epoch of Web 2.0 brings unprecedented challenge to the social computing, which has to provide real-Time solution in the circumstance of massive data volumes and evolving application scenarios. This paper presents an emotion-based social computing platform namely ESC for streaming big-data. The main aim of ESC is to provide sentiment analysis as the foundation of social computing and enable both real-Time computation on streaming big-data and batch computation on off-line big-data with high performance and low risk. Different from conventional data processing technologies, ESC is designed as a scalable and QoS-optimized adaptive platform for developers to only focus on business models instead of being distracted by details of the computing infrastructure. In addition, continuous streaming computing is emphasized in ESC to keep tracking on long term dynamic evolution in social media, which can provide a valuable proxy for in-depth social analytics. The architecture of ESC is implemented by distributed storage, sentiment analysis, data parallelism and routing, real-Time streaming computation, batch computation and distributed machine learning. And the evaluation results from real-Time and batch computations testify the high performance and scalability of ESC. Moreover, a few applications based on it further demonstrates its usability in enacting on different streaming big-data and variety of social computations.
UR - https://www.scopus.com/pages/publications/84986550390
U2 - 10.1109/ICSSSM.2016.7538620
DO - 10.1109/ICSSSM.2016.7538620
M3 - 会议稿件
AN - SCOPUS:84986550390
T3 - 2016 13th International Conference on Service Systems and Service Management, ICSSSM 2016
BT - 2016 13th International Conference on Service Systems and Service Management, ICSSSM 2016
A2 - Chen, Jian
A2 - Cai, Xiaoqiang
A2 - Zhou, Changchun
A2 - Qin, Kaida
A2 - Yang, Baojian
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 June 2016 through 26 June 2016
ER -