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6 articles

Data-Intensive Service Provision Based on Particle Swarm Optimization

Lijuan Wang, Jun Shen
Pages: 330 - 339
The data-intensive service provision is characterized by the large of scale of services and data and also the high-dimensions of QoS. However, most of the existing works failed to take into account the characteristics of data-intensive services and the effect of the big data sets on the whole performance...

Multi-feature based event recommendation in Event-Based Social Network*

Jiuxin Cao, Ziqing Zhu, Liang Shi, Bo Liu, Zhuo Ma
Pages: 618 - 633
As a new type of heterogeneous social network, Event-Based Social Network (EBSN) has experienced rapid development after its appearance. In EBSN, the interaction data between users and events is relatively sparse because of the short life cycle of events, which brings great challenges to event recommendation....

On-line Evolutionary Sentiment Topic Analysis Modeling

YongHeng Chen, ChunYan Yin, YaoJin Lin, Wanli Zuo
Pages: 634 - 651
As the rapid booming of reviews, a valid sentiment analysis model will significantly boost the review recommendation system’s capability, and present more constructive information for consumers. Topic probabilistic models have already shown many advantages for detecting potential structure of topics...

A Comparison of Outlier Detection Techniques for High-Dimensional Data

Xiaodan Xu, Huawen Liu, Li Li, Minghai Yao
Pages: 652 - 662
Outlier detection is a hot topic in machine learning. With the newly emerging technologies and diverse applications, the interest of outlier detection is increasing greatly. Recently, a significant number of outlier detection methods have been witnessed and successfully applied in a wide range of fields,...

A computer aided analysis scheme for detecting epileptic seizure from EEG data

Enamul Kabir, Siuly, Jinli Cao, Hua Wang
Pages: 663 - 671
This paper presents a computer aided analysis system for detecting epileptic seizure from electroencephalogram (EEG) signal data. As EEG recordings contain a vast amount of data, which is heterogeneous with respect to a time-period, we intend to introduce a clustering technique to discover different...