Catálogo de publicaciones - libros

The aim of this paper is to discuss the development of a system for the discovery of valuable new knowledge and to create effective sales strategies based on that knowledge by using massive amounts of click stream data generated by site visitors. This paper discusses and clarifies the process as to how detailed consumer behavior patterns are extracted from click stream data of Internet mall retail site and how such patterns can be used as a source of new ideas for creating new marketing strategies. We will also discuss our successful use of an improved version of the genome analysis system called E-BONSAI to extract and analyze special character strings related to site visitor behavior indicated by distinctive click patterns.

Network security is an important issue in maintaining the Internet as an important social infrastructure. Especially, finding excessive consumption of network bandwidth caused by P2P mass flow, finding internet viruses, and finding DDoS attacks are important security issues. Although stream mining techniques seem to be promising techniques for network security, extensive network flow prevents the simple application of such techniques. Since conventional methods require non-realistic memory resources, a mining technique which works well using limited memory is required. This paper proposes a sampling-based mining method to achieve network security. By analyzing the characteristics of the proposed method with real Internet backbone flow data, we show the advantages of the proposed method, i.e. less memory consumption.

In this paper, we contrast inductive nursing risk management and abductive nursing risk management, point out the importance of the abductive type, and suggest cooperation between them. In general risk management, inductive management is usually adopted. If we computationally conduct inductive management, it is vital to collect a considerable number of examples to perform machine learning. For nursing risk management, risk management experts usually perform manual learning to produce textbooks. In the Accident or Incident Report Database home page, we can review various types of accidents or incidents. However, since reports are written by various nurses, the granularity and quality of reports are not sufficient for machine learning. We, therefore, explain the importance of conducting dynamic nursing risk management that can be achieved by abduction, then illustrate cooperation between abductive and inductive types of nursing risk management.