命令行接口CLI(Command Line Interface)作为当今网络管理的主要操作方式,提供给用户一定的配置、管理、监控和维护功能。文章简单介绍了命令行接口的基本结构和功能流程,设计了一个完整的CLI管理系统的框架,并对命令树生成和解析这个...命令行接口CLI(Command Line Interface)作为当今网络管理的主要操作方式,提供给用户一定的配置、管理、监控和维护功能。文章简单介绍了命令行接口的基本结构和功能流程,设计了一个完整的CLI管理系统的框架,并对命令树生成和解析这个核心问题给出详细的实现方案。该管理系统能够应用于不同的网络设备。展开更多
The paper studies the problem of incremental pattern mining from semi-structrued data. When a new dataset is added into the original dataset, it is difficult for existing pattern mining algorithms to incrementally upd...The paper studies the problem of incremental pattern mining from semi-structrued data. When a new dataset is added into the original dataset, it is difficult for existing pattern mining algorithms to incrementally update the mined results. To solve the problem, an incremental pattern mining algorithm based on the rightmost expansion technique is proposed here to improve the mining performance by utilizing the original mining results and information obtained in the previous mining process. To improve the efficiency, the algorithm adopts a pruning technique by using the frequent pattern expansion forest obtained in mining processes. Comparative experiments with different volume of initial datasets, incremental datasets and different minimum support thresholds demonstrate that the algorithm has a great improvement in the efficiency compared with that of non-incremental pattern mining algorithm.展开更多
文摘命令行接口CLI(Command Line Interface)作为当今网络管理的主要操作方式,提供给用户一定的配置、管理、监控和维护功能。文章简单介绍了命令行接口的基本结构和功能流程,设计了一个完整的CLI管理系统的框架,并对命令树生成和解析这个核心问题给出详细的实现方案。该管理系统能够应用于不同的网络设备。
文摘The paper studies the problem of incremental pattern mining from semi-structrued data. When a new dataset is added into the original dataset, it is difficult for existing pattern mining algorithms to incrementally update the mined results. To solve the problem, an incremental pattern mining algorithm based on the rightmost expansion technique is proposed here to improve the mining performance by utilizing the original mining results and information obtained in the previous mining process. To improve the efficiency, the algorithm adopts a pruning technique by using the frequent pattern expansion forest obtained in mining processes. Comparative experiments with different volume of initial datasets, incremental datasets and different minimum support thresholds demonstrate that the algorithm has a great improvement in the efficiency compared with that of non-incremental pattern mining algorithm.
