期刊文献+

基于web和关系数据库的super-Domain系统的实现

Realizing the Super- Domain System based on Web and Related Database
下载PDF
导出
摘要 面对互联网的快速发展和信息的海洋,如何组织、管理和维护海量信息并为用户提供有效的服务也就成为一项重要而迫切的研究课题,正是基于这个难点我们开发和研究了基于web和数据库以及直接面向了大规模网络管理的网络管理系统——Super-Domain系统。这种系统是中文的能够实现多套收费政策,并且能够很好的在性能、安全、计费和配置以及故障方面实现其应有的功能,并且能很好的进行网络管理。 Considering fast developed network and information, it is an urgent problem to manage and organize large number informa- tion and to serve users. According with the requirement, we develop the network management system - super domain based on web and database. The system has a lot of function, such as Chinese language, various charge pattern, safety and fault management etc.
作者 杜敏成
出处 《辽宁科技学院学报》 2014年第1期18-20,共3页 Journal of Liaoning Institute of Science and Technology
关键词 Super—Domain系统 WEB 关系数据库 Super - Domain Web Related database
  • 相关文献

参考文献3

  • 1葛丽娜,钟诚.一个有效的分布式并行挖掘关联规则算法[J].计算机工程与设计,2004,25(8):1258-1260. 被引量:6
  • 2Davin, J, Galvin, J, McCloghrie K. SNMP administrative model. RFC 1351, 1992, http ://www. ietf. or I/rfc/rfe1351. txt.
  • 3Case, J, Fedora M. Schoffstall Met al. A simple network management protocol. DDN Network Information Center, SR1 Interna- tional, 1990.

二级参考文献10

  • 1Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases [A]. Proceedings of ACM SIGMOD[C]. New York: Association for Computer Machinery,1993.207-216.
  • 2Agrawal R, Srikant R. Fast algorithms for mining association rules in large databases [A]. Proceedings of the 20th international conference on very large databases [C]. New York: Institute of Electrical and Electronics Engineers, 1994.
  • 3Agrawal R, Srikant R. Mining sequential patterns[A]. Proc of the 1 1th Int'l conference on data engineering [C]. Washington, D.C:IEEE Computer Society Press, 1995.
  • 4Maurice Houtsma, Arun Swami. Set-oriented mining of association rules [A]. Research report RJ9567, IBM almaden research center[C]. San Jose, California: [s.n.], 1993.
  • 5Park J S, Chen M, PS Yu. An effective hash-based algorithm for mining association rules[C]. Proc of the ACM SIGMOD Int'l Confon Management of Data, San Jose, CA, 1995. 175-186.
  • 6Ashok Savasere Edward Omiecinski Shamkant Navathe. An efficient algorithm for mining association rules in large database[C]. Proceeding of the VLDB Conference, Zurich Switzerland,1995.432 -444.
  • 7Brin S, Motwani R, Ullman J, et al. Dynamic itemset counting and implication rules for market basket data[C]. Proc the 1997 ACM-SIGMOD Conference on Management of Data, 1997.255-264.
  • 8David W Cheung, Kan Hu Shaowei Xia. Asynchronous parallel algorithm for mining association rules on a shared-memory multi-processors[C]. 10th ACM Symp Parallel Algorithm and Architectures, 1998. 279- 288.
  • 9Zaki M J, Parthasarathy S, Ogihara M, et al. New algorithms for fast discovery of association rules[C]. 3rd Intl. Conf on Knowledge Discovery and Data Mining, 1997. 283-286.
  • 10Cheng D, Han J, Ng V, et al. A fast distributed algorithm for mining association rules[C]. 4th Intl Conf Parallel and Distributed Info Systems(PDIS-96), Miami Beach, Florida, 1996.31-43.

共引文献5

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部