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- 저자 : Guoyong Lin (검색결과 2 건)
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Guoyong Lin,Fan Huang 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.6
Object: With the continuous advancement of information technology, more and more systems use database to store basic data, the security of data is an important part of the design of the business system. Method: In order to effectively improve the viability of data, this paper proposes a kind of multi point and multi hop database remote disaster recovery and backup technology. Process: Based on the in-depth analysis of the functions and demand of database, this paper introduces the working principles and key technologies of disaster recovery technology, describes the principles and realization process of multi point and multi hop database remote disaster recovery and backup technology, and carries on the experimental analysis. Conclusion: Theoretical analysis and experimental results show that this method is an effective new way of database remote disaster recovery and backup. So this technology has many advantages, such as multi point and multi hop backup, good real-time performance, fine backup granularity and so on.
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Improving Recommendation Accuracy and Diversity through Cost-Awareness Probabilistic Spreading
Guoyong Cai,Dong Zhang,Yumin Lin 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.11
Recommender systems provide users with personalized suggestions for products. A key challenge is how to improve the diversity of recommendation results as much as possible, while maintaining reliably accurate suggestions. Although the bipartite graph based probabilistic spreading algorithm has its advantages of good accuracy and low computational complexity, its diversity is poor. In this paper, we introduce a cost-aware probabilistic spreading algorithm, and show how it can improve both recommendation accuracy and diversity by designing different spreading costs. Comparative experiments on widely used datasets confirm the effectiveness of the cost-aware probabilistic spreading approach in terms of accuracy, aggregate diversity and individual diversity of recommendation results. In addition, the time complexity of the proposed algorithm is also analyzed.
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