Please wait a minute...
New Technology of Library and Information Service  2005, Vol. 21 Issue (5): 27-29    DOI: 10.11925/infotech.1003-3513.2005.05.06
Current Issue | Archive | Adv Search |
Modeling of Personalized Pushing System Using UML
Qu Fang1,2   Sun Yong2
1 (Library of Xuzhou Normal University,Xuzhou 221116, China)
2 (Computer Science and Technology School,Suzhou University,Suzhou 215006,China)  
Export: BibTeX | EndNote (RIS)      

In this paper content of libaray personalize service is summarized, system analysis modeling is completed with UML and ROSE. Case diagram is used in analizing and interactice digram, class digram,component diagram are also used in design.

Key wordsUML      Personalize      Pushing      Libarary      Model     
Received: 24 December 2004      Published: 25 May 2005



Corresponding Authors: Qu Fang     E-mail:
About author:: Qu Fang,Sun Yong

Cite this article:

Qu Fang,Sun Yong. Modeling of Personalized Pushing System Using UML. New Technology of Library and Information Service, 2005, 21(5): 27-29.

URL:     OR

1施昊华,张朝辉. UML面向对象结构设计与应用. 国防工业出版社. 2003
2[美]Martin Fowler,Kendall Scott著,徐家福译. UML精粹 第2版 标准对象建模语言简明指南. 清华大学出版社,2000
3焦玉英, 柯青. 统一建模语言在网络主动推送系统设计中的应用. 中国图书馆学报,2003(5):57-60
4Wendy Boggs,Michael Boggs著,邱仲潘等译,UML与Rational Rose 2002从入门到精通北京:电子工业出版社,2002:17-18
5宋丽哲,牛振东,宋瀚涛,师雪霖,孙一钢. 数字图书馆的个性化服务计算机工程,2004(2):46-48

[1] Shan Xiaohong,Wang Chunwen,Liu Xiaoyan,Han Shengxi,Yang Juan. Identifying Lead Users in Open Innovation Community from Knowledge-based Perspectives[J]. 数据分析与知识发现, 2021, 5(9): 85-96.
[2] Che Hongxin,Wang Tong,Wang Wei. Comparing Prediction Models for Prostate Cancer[J]. 数据分析与知识发现, 2021, 5(9): 107-114.
[3] Lu Quan, He Chao, Chen Jing, Tian Min, Liu Ting. A Multi-Label Classification Model with Two-Stage Transfer Learning[J]. 数据分析与知识发现, 2021, 5(7): 91-100.
[4] Chen Xingyue, Ni Liping, Ni Zhiwei. Extracting Financial Events with ELECTRA and Part-of-Speech[J]. 数据分析与知识发现, 2021, 5(7): 36-47.
[5] Zhu Hou,Fang Qingyan. Quantifying and Examining Privacy Paradox of Social Media Users[J]. 数据分析与知识发现, 2021, 5(7): 111-125.
[6] Cao Rui,Liao Bin,Li Min,Sun Ruina. Predicting Prices and Analyzing Features of Online Short-Term Rentals Based on XGBoost[J]. 数据分析与知识发现, 2021, 5(6): 51-65.
[7] Wang Yizhen,Ou Shiyan,Chen Jinju. Automatic Abstracting Civil Judgment Documents with Two-Stage Procedure[J]. 数据分析与知识发现, 2021, 5(5): 104-114.
[8] Yi Huifang,Liu Xiwen. Analyzing Patent Technology Topics with IPC Context-Enhanced Context-LDA Model[J]. 数据分析与知识发现, 2021, 5(4): 25-36.
[9] Shi Xiang,Liu Ping. Extraction and Representation of Domain Knowledge with Semantic Description Model and Knowledge Elements——Case Study of Information Retrieval[J]. 数据分析与知识发现, 2021, 5(4): 123-133.
[10] Zhang Xin,Wen Yi,Xu Haiyun. A Prediction Model with Network Representation Learning and Topic Model for Author Collaboration[J]. 数据分析与知识发现, 2021, 5(3): 88-100.
[11] Feng Yong,Liu Yang,Xu Hongyan,Wang Rongbing,Zhang Yonggang. Recommendation Model Incorporating Neighbor Reviews for GRU Products[J]. 数据分析与知识发现, 2021, 5(3): 78-87.
[12] Zhao Tianzi, Duan Liang, Yue Kun, Qiao Shaojie, Ma Zijuan. Generating News Clues with Biterm Topic Model[J]. 数据分析与知识发现, 2021, 5(2): 1-13.
[13] Shen Wang, Li Shiyu, Liu Jiayu, Li He. Optimizing Quality Evaluation for Answers of Q&A Community[J]. 数据分析与知识发现, 2021, 5(2): 83-93.
[14] Wu Yanwen, Cai Qiuting, Liu Zhi, Deng Yunze. Digital Resource Recommendation Based on Multi-Source Data and Scene Similarity Calculation[J]. 数据分析与知识发现, 2021, 5(11): 114-123.
[15] Ding Hao, Ai Wenhua, Hu Guangwei, Li Shuqing, Suo Wei. A Personalized Recommendation Model with Time Series Fluctuation of User Interest[J]. 数据分析与知识发现, 2021, 5(11): 45-58.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938